diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc.h
new file mode 100644
index 0000000000000000000000000000000000000000..3130a77f5b164b5782e5a6ef677f3ad56a937a7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_gammaincc_ops.h>
+
+namespace at {
+
+
+// aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_gammaincc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_gammaincc_out::call(self, other, out);
+}
+// aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_gammaincc_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_gammaincc_out::call(self, other, out);
+}
+
+// aten::special_gammaincc(Tensor self, Tensor other) -> Tensor
+inline at::Tensor special_gammaincc(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_gammaincc::call(self, other);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a711d334332bf98dceb790ff82074c48341c4f39
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_gammaincc(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_gammaincc_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_gammaincc_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..da4540a1132180a849aa2a2aa7199dc1b453202c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_gammaincc(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_gammaincc_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..964573c907f0cd1d7b8f022606e5c66fb7efb8d6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaincc_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_gammaincc_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_gammaincc";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_gammaincc {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_gammaincc";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_gammaincc(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln.h
new file mode 100644
index 0000000000000000000000000000000000000000..02ef5e840584ddc72e3a9437ee8f2123b7ecf092
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_gammaln_ops.h>
+
+namespace at {
+
+
+// aten::special_gammaln(Tensor self) -> Tensor
+inline at::Tensor special_gammaln(const at::Tensor & self) {
+    return at::_ops::special_gammaln::call(self);
+}
+
+// aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_gammaln_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_gammaln_out::call(self, out);
+}
+// aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_gammaln_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_gammaln_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1fb0bf105a779d8fa3c7c28a2071ef132b32e210
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_gammaln(const at::Tensor & self);
+TORCH_API at::Tensor & special_gammaln_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_gammaln_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b43bd201ea4dd5e9ec9a6dcc33602cab48f258e0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_gammaln(const at::Tensor & self);
+TORCH_API at::Tensor & special_gammaln_out(const at::Tensor & self, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..35c76732e1546eedc14f3928d35f01258e07cb41
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_gammaln_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_gammaln {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_gammaln";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_gammaln(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_gammaln_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_gammaln";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c3eecc02a116daf7807931fb61bdb761701bb5d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_hermite_polynomial_h_ops.h>
+
+namespace at {
+
+
+// aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_h::call(x, n);
+}
+
+// aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_hermite_polynomial_h(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_h_x_scalar::call(x, n);
+}
+
+// aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_hermite_polynomial_h_n_scalar::call(x, n);
+}
+
+// aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_h_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_h_out::call(x, n, out);
+}
+
+// aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_h_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_h_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..434fb83fb492f190c4ba85b19f44c19f69ad1433
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c3a44d48df6fac3b67de2171910ced647f1a05c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5a3447069dea6ab0215b4c99a01267fcdd2ee03
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9895fccca145e8e1daa2f079f040cdc9300f9ec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..432c7628f3bba9ff040d4d6fc28cabaf3b19a76b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_hermite_polynomial_h : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5d97f9c918ad6dfb2383ca254d370f60d36a01f1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..20b4fe4450171d664c95f45083cbfb41a1b7c824
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_hermite_polynomial_h_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_hermite_polynomial_h_out : public at::meta::structured_special_hermite_polynomial_h {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_hermite_polynomial_h(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_h_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..935205860c5c087b8d893ad6925d8d4fcae8ae0b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_h_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_hermite_polynomial_h {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_hermite_polynomial_h_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_hermite_polynomial_h_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_hermite_polynomial_h_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_hermite_polynomial_h_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_hermite_polynomial_h_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_h";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he.h
new file mode 100644
index 0000000000000000000000000000000000000000..e07d233668c898fc9b6975da83aee1cffcc15c8f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_hermite_polynomial_he_ops.h>
+
+namespace at {
+
+
+// aten::special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_he::call(x, n);
+}
+
+// aten::special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_he_x_scalar::call(x, n);
+}
+
+// aten::special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_hermite_polynomial_he_n_scalar::call(x, n);
+}
+
+// aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_he_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_he_out::call(x, n, out);
+}
+
+// aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_he_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x, n, out);
+}
+// aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_hermite_polynomial_he_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e90ea164a1ca94cd75aac0caef7281bd3bc54958
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6dac4c151575a2b71a112b665fac6196d6a60236
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..781f7c8eec6e8616c5d5a5ef445067e586d79220
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..34814ebc4a250a89cd7a3798c94e5891cf3d5c6d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..7bc14b146a52831fe2f0b6ca662c1a42ddac75dd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_hermite_polynomial_he : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b0ae95104ca4b441bc8ef7b57f3119958b64b7c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..638c1ffe8def6fc67131701c0af966fb5cdb13c5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_hermite_polynomial_he_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_hermite_polynomial_he_out : public at::meta::structured_special_hermite_polynomial_he {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_hermite_polynomial_he_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..80e76c21f6bb7e72aea22d5b50aee1b4a50787fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_hermite_polynomial_he_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_hermite_polynomial_he {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_hermite_polynomial_he_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_hermite_polynomial_he_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_hermite_polynomial_he_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_hermite_polynomial_he_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_hermite_polynomial_he_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_hermite_polynomial_he";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0.h
new file mode 100644
index 0000000000000000000000000000000000000000..60c287fd0d1042a793056592a919cab5a803dc43
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_i0_ops.h>
+
+namespace at {
+
+
+// aten::special_i0(Tensor self) -> Tensor
+inline at::Tensor special_i0(const at::Tensor & self) {
+    return at::_ops::special_i0::call(self);
+}
+
+// aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i0_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_i0_out::call(self, out);
+}
+// aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i0_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_i0_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3f6c636f356df60fdae86f3f6e033f6ddd20a2a0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_i0(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i0_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..944ab587d90961a263d37fe1ca924dcbde26e64e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_i0(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0_out(const at::Tensor & self, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..01b87211ca503beba83e026f1764ba2685d14376
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_i0 {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i0";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_i0(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_i0_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i0";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e.h
new file mode 100644
index 0000000000000000000000000000000000000000..067dfe1824623dd441c70f95a0d7cdc144b40861
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_i0e_ops.h>
+
+namespace at {
+
+
+// aten::special_i0e(Tensor self) -> Tensor
+inline at::Tensor special_i0e(const at::Tensor & self) {
+    return at::_ops::special_i0e::call(self);
+}
+
+// aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_i0e_out::call(self, out);
+}
+// aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_i0e_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b75fbfddd0d71e537bb4a5f39ee883daa9522cd6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_i0e(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a59b536a52deff0fd31418afe5f3b647bd2b10cd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_i0e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..eed4e1cff11d87ac33d0b6eb36bddfa644c45f31
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_i0e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..77f2bb4baffae874ff4a55a22d7437e0d8fcc7b0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_i0e : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..136156df067f1618c7e0df704bb27a684d3a35f6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_i0e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8a26657c9a386c4af215cc7c419b839fede86af
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_i0e_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_i0e_out : public at::meta::structured_special_i0e {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..abf748d855e9ec5edec95329a6e130d1f223f62b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i0e_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_i0e {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i0e";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_i0e(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_i0e_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i0e";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1.h
new file mode 100644
index 0000000000000000000000000000000000000000..bec209b582698d21fed083ebd3171ab7571b65ec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_i1_ops.h>
+
+namespace at {
+
+
+// aten::special_i1(Tensor self) -> Tensor
+inline at::Tensor special_i1(const at::Tensor & self) {
+    return at::_ops::special_i1::call(self);
+}
+
+// aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_i1_out::call(self, out);
+}
+// aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_i1_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2fd0fdd80b73d55053bcf719d448a5671ce36b6d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_i1(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7504ffc7a966306c574a6fd629595cacfe293b7b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_i1(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..88a69f8343e3c91d8d26bc3f28cfeda84c9ca983
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_i1(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..0e043e1730942d55e4ff3c3b081459150e7bac4f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_i1 : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8cb59a021a937603b3f311fef6060131decd0afe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_i1(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a37fa56b81cf856f76b1b7bcd85c0c733b54818
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_i1_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_i1_out : public at::meta::structured_special_i1 {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..399bdf09023646929cf54fd3671ea5c77ad4193b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_i1 {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i1";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_i1(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_i1_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i1";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac9970207d431ceb9d1e7bd7f593bd76ddd6e465
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_i1e_ops.h>
+
+namespace at {
+
+
+// aten::special_i1e(Tensor self) -> Tensor
+inline at::Tensor special_i1e(const at::Tensor & self) {
+    return at::_ops::special_i1e::call(self);
+}
+
+// aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_i1e_out::call(self, out);
+}
+// aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_i1e_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f7829c9f5b21dfc7c86a94872ceb3592edf4ccdb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_i1e(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..36049fb8c569d819488536ece367000a01480527
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_i1e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..012bace7d98803a68e3ed53fd099e474bf596271
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_i1e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..045b0a2377bc492ba1f95602afae88b003ad7aba
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_i1e : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb6b00da04025984a84a3f6453570315cd731494
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_i1e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i1e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ec5388676249125930c7e74eef6d540f121482ca
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_i1e_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_i1e_out : public at::meta::structured_special_i1e {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..424cb329909bd25e5e812a58b0ab0e3c5684c636
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_i1e_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_i1e {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i1e";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_i1e(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_i1e_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_i1e";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l.h
new file mode 100644
index 0000000000000000000000000000000000000000..fc49f88888cc92f2a2578308c5727a54c5bbf75c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_laguerre_polynomial_l_ops.h>
+
+namespace at {
+
+
+// aten::special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_laguerre_polynomial_l::call(x, n);
+}
+
+// aten::special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_laguerre_polynomial_l_x_scalar::call(x, n);
+}
+
+// aten::special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_laguerre_polynomial_l_n_scalar::call(x, n);
+}
+
+// aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_laguerre_polynomial_l_out::call(x, n, out);
+}
+// aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_laguerre_polynomial_l_out::call(x, n, out);
+}
+
+// aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n, out);
+}
+// aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_laguerre_polynomial_l_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x, n, out);
+}
+// aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_laguerre_polynomial_l_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2493a1c1ba3b9743552f059e43855e6d56a10fbd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..875b1dd39a9e278a96ffe7087852fffbb09c1923
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..efcfe502b8cbaf3c5ac37dc26ad23694656ad573
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..04aedbe51b8f90414698254408cc241fcd796460
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a274db6916adc22092f4aa037130278cba9779e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_laguerre_polynomial_l : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bde62bdd9257a845bc1c975a8c462bd2acc82a71
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ec91712eb2135cc23d269551f94d06d3ae76d824
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_laguerre_polynomial_l_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_laguerre_polynomial_l_out : public at::meta::structured_special_laguerre_polynomial_l {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d25098a2b49be2802fa90d5854941c9bbf405f43
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_laguerre_polynomial_l_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_laguerre_polynomial_l {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_laguerre_polynomial_l_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_laguerre_polynomial_l_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_laguerre_polynomial_l_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_laguerre_polynomial_l_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_laguerre_polynomial_l_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_laguerre_polynomial_l";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p.h
new file mode 100644
index 0000000000000000000000000000000000000000..57c10722473012bc117633c38d969b9f43ee0087
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_legendre_polynomial_p_ops.h>
+
+namespace at {
+
+
+// aten::special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_legendre_polynomial_p::call(x, n);
+}
+
+// aten::special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_legendre_polynomial_p(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_legendre_polynomial_p_x_scalar::call(x, n);
+}
+
+// aten::special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_legendre_polynomial_p_n_scalar::call(x, n);
+}
+
+// aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_legendre_polynomial_p_out::call(x, n, out);
+}
+// aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_legendre_polynomial_p_out::call(x, n, out);
+}
+
+// aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n, out);
+}
+// aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_legendre_polynomial_p_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x, n, out);
+}
+// aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_legendre_polynomial_p_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0dbd2b384fcf5cac1193e2e87e1d7df0944eed44
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..370c34e07940bf643fff41b3050b7acba81c1fe8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6cd978f1f3cb2d3f9bc5b85cbe32d2c8edda7f54
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..79db3964d4cc831551a4f44a00a07493b2a66881
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad22c1cdfc28d1bf91c06e860e189a7683658f25
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_legendre_polynomial_p : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4a57c6efda3f9b511e2fdbd26eacc5402c30df13
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e5980b6092da28f56e12e7f9f7c7d225a645ad7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_legendre_polynomial_p_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_legendre_polynomial_p_out : public at::meta::structured_special_legendre_polynomial_p {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_legendre_polynomial_p(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_legendre_polynomial_p_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d79154b33b4e014f6c1466794af9a2715ee99bbc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_legendre_polynomial_p_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_legendre_polynomial_p {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_legendre_polynomial_p_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_legendre_polynomial_p_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_legendre_polynomial_p_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_legendre_polynomial_p_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_legendre_polynomial_p_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_legendre_polynomial_p";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p.h
new file mode 100644
index 0000000000000000000000000000000000000000..e96c4a156be5bbc4a43a677b2c7bd6bab4fc348e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_log1p_ops.h>
+
+namespace at {
+
+
+// aten::special_log1p(Tensor self) -> Tensor
+inline at::Tensor special_log1p(const at::Tensor & self) {
+    return at::_ops::special_log1p::call(self);
+}
+
+// aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_log1p_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_log1p_out::call(self, out);
+}
+// aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_log1p_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_log1p_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8fa322c8682ec33497f80df70b99b45ee112ac1d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_log1p(const at::Tensor & self);
+TORCH_API at::Tensor & special_log1p_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_log1p_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..52e6ab859521a5b259df3eb49dde948a3188c2d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_log1p(const at::Tensor & self);
+TORCH_API at::Tensor & special_log1p_out(const at::Tensor & self, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..69a4cd9359922db235f444d1fe2dae44a72d7520
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log1p_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_log1p {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_log1p";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_log1p(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_log1p_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_log1p";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr.h
new file mode 100644
index 0000000000000000000000000000000000000000..27845aafbcfa60a94cd7e22b2eadb9c847bd0d9f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_log_ndtr_ops.h>
+
+namespace at {
+
+
+// aten::special_log_ndtr(Tensor self) -> Tensor
+inline at::Tensor special_log_ndtr(const at::Tensor & self) {
+    return at::_ops::special_log_ndtr::call(self);
+}
+
+// aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_log_ndtr_out::call(self, out);
+}
+// aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_log_ndtr_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e51c50a1a056fb7b8602952fd7720b11f5feced
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..37eb5d53d7c05ed26a683676d6fe6aa4be741937
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a05f392c9967892004f4725744b25d2f578aa79d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..1f0cefe78c2fd5d6c81cf5303a2a7795122a47fb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_log_ndtr : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..72d601723e994852ecb2c272013519ae272b1ff5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_log_ndtr(const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_log_ndtr_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..334b5a52aab6af4c72a81da7bca0564123380146
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_log_ndtr_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_log_ndtr_out : public at::meta::structured_special_log_ndtr {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b46b6c3ed1c548e4c66f851b532eb12fac216a07
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_ndtr_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_log_ndtr {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_log_ndtr";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_log_ndtr(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_log_ndtr_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_log_ndtr";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax.h
new file mode 100644
index 0000000000000000000000000000000000000000..b52322581bbb5a6ef10c71b910d4b544b5b1b9a5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_log_softmax_ops.h>
+
+namespace at {
+
+
+// aten::special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor special_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::special_log_softmax::call(self, dim, dtype);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..489ce4cd366b6236c64dfe5ab961b68bb11789be
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..87e1626cfcac6836dbec1b7c4bccc3b61724b3c8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_log_softmax_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_log_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_modified_bessel_k0.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_modified_bessel_k0.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5828f057e1c24601aa59d9f22dbc25bfadff391
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_modified_bessel_k0.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_modified_bessel_k0_ops.h>
+
+namespace at {
+
+
+// aten::special_modified_bessel_k0(Tensor self) -> Tensor
+inline at::Tensor special_modified_bessel_k0(const at::Tensor & self) {
+    return at::_ops::special_modified_bessel_k0::call(self);
+}
+
+// aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_modified_bessel_k0_out::call(self, out);
+}
+// aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_modified_bessel_k0_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_modified_bessel_k0_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d184797c64a87c74da40e1c90050ef5bfb1bc816
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_u_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_u_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3dea27053111069ec6eb363c433f03367462ad4a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_u_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_u_out : public at::meta::structured_special_shifted_chebyshev_polynomial_u {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_u(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_u_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_u(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_u_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b054d4144d976879a0004486c495385d4b31642
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_u_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_u_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_u";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v.h
new file mode 100644
index 0000000000000000000000000000000000000000..f6d78f0f86299e8603937f5994d384110dcb3e26
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h>
+
+namespace at {
+
+
+// aten::special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_out::call(x, n, out);
+}
+
+// aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..436f6477d177517f905e8a31d683860072909903
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9c31f1e3624b67ddced1e52cef0b38d25951da1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cec135081f13c1423023cc88e0b2c17dbcf261b9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bb4693030aa09d2e1af928b51bfa6f24f34ea9d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1f6bf5d44b5e411869fbfb311e33807008f78e9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_v : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b18842d2e184d53eae95419c95813752191b056
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d0481f27706d95c46c4c90f3a310b1e065c9216
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_v_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_v_out : public at::meta::structured_special_shifted_chebyshev_polynomial_v {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_v(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_v_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..0bc3f407efcf69673f28ccddaea2508630c41c11
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_v_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_v_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_v";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w.h
new file mode 100644
index 0000000000000000000000000000000000000000..898a377e20851b990a746c27d1f91e2c5076102a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h>
+
+namespace at {
+
+
+// aten::special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor
+inline at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar::call(x, n);
+}
+
+// aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_out::call(x, n, out);
+}
+
+// aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::call(x, n, out);
+}
+
+// aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
+}
+// aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out) {
+    return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::call(x, n, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2f8d1ce4ece672c1ff818d5394662e259449f08f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9ad9f811769239cb7916ae6d2dd4ba8e9201ddc6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b9c948b04b9f25d6627e1f3e5d35b06790cefb9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..00e4d99047fce7b296a4d9db81564ad3d6746e3d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..2cedf92cd7546b85bb0fc44a2262af5bf8c0a668
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_w : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x, const at::Tensor & n);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d54a9b56b4808047748acc5b0580f931ff794e0a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..37cb67f3f134e4f709d9725baf751f262dba44b7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_w_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_shifted_chebyshev_polynomial_w_out : public at::meta::structured_special_shifted_chebyshev_polynomial_w {
+void impl(const at::Tensor & x, const at::Tensor & n, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Scalar & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+TORCH_API at::Tensor special_shifted_chebyshev_polynomial_w(const at::Tensor & x, const at::Scalar & n);
+TORCH_API at::Tensor & special_shifted_chebyshev_polynomial_w_out(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e6566e87d176a1c5b72797b9c6b6f2a0cf6e720
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_shifted_chebyshev_polynomial_w_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "x_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor";
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "n_scalar";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "x_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_w_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_shifted_chebyshev_polynomial_w";
+  static constexpr const char* overload_name = "n_scalar_out";
+  static constexpr const char* schema_str = "special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc.h
new file mode 100644
index 0000000000000000000000000000000000000000..4438b14c18c8cf819504c3ec202ce2cf763a9b42
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_sinc_ops.h>
+
+namespace at {
+
+
+// aten::special_sinc(Tensor self) -> Tensor
+inline at::Tensor special_sinc(const at::Tensor & self) {
+    return at::_ops::special_sinc::call(self);
+}
+
+// aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_sinc_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::special_sinc_out::call(self, out);
+}
+// aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_sinc_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_sinc_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..74445dc75e6b7d73b60b78926c9fd2551de451c2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_sinc(const at::Tensor & self);
+TORCH_API at::Tensor & special_sinc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_sinc_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9ddcb7ca4025aa36eba079336e9070efd9462bbf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_sinc(const at::Tensor & self);
+TORCH_API at::Tensor & special_sinc_out(const at::Tensor & self, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..df1ab9b2bc6a1a55e1bc308134937c3ad2b6af88
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_sinc_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_sinc {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_sinc";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_sinc(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_sinc_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_sinc";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7432b3c364c4f78ba2a13aec47b671b1510526c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_softmax_ops.h>
+
+namespace at {
+
+
+// aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor
+inline at::Tensor special_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::special_softmax::call(self, dim, dtype);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f6e2ab15762ad1376312e2202ec415c0b3ca219d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e0e0ad73a6b3d85e103fc7089b8caec3e0c1ba6c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_softmax(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4cad9f28222944978311afd6727393e2326d1e8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_softmax_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_softmax {
+  using schema = at::Tensor (const at::Tensor &, int64_t, ::std::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_softmax";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, ::std::optional<at::ScalarType> dtype);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f63de5faf081e83befac6ad8658c1055d82af38
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_spherical_bessel_j0_ops.h>
+
+namespace at {
+
+
+// aten::special_spherical_bessel_j0(Tensor x) -> Tensor
+inline at::Tensor special_spherical_bessel_j0(const at::Tensor & x) {
+    return at::_ops::special_spherical_bessel_j0::call(x);
+}
+
+// aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x) {
+    return at::_ops::special_spherical_bessel_j0_out::call(x, out);
+}
+// aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out) {
+    return at::_ops::special_spherical_bessel_j0_out::call(x, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8072a5e2162cd710496324f406f020a3df4efb0b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_spherical_bessel_j0(const at::Tensor & x);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b0b0d0f47e82dc7b808d3161bb180ae1e3010af5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_spherical_bessel_j0(const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e169b04ca6da38adaee1311f5dac61e8777b529d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_spherical_bessel_j0(const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..53db55b7031105be8d010ea6e3a8ef5fcf6bdb9a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_spherical_bessel_j0 : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4264836072be955d962661e47f684bce7a8ef49
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_spherical_bessel_j0(const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_out(at::Tensor & out, const at::Tensor & x);
+TORCH_API at::Tensor & special_spherical_bessel_j0_outf(const at::Tensor & x, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..31ef5d6fb71f772eadf1f2ef76b7b45859af8a06
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_spherical_bessel_j0_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_spherical_bessel_j0_out : public at::meta::structured_special_spherical_bessel_j0 {
+void impl(const at::Tensor & x, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf3a59c3b5a1f0fbd9abafd59104a532660a502e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_spherical_bessel_j0_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_spherical_bessel_j0 {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_spherical_bessel_j0";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_spherical_bessel_j0(Tensor x) -> Tensor";
+  static at::Tensor call(const at::Tensor & x);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x);
+};
+
+struct TORCH_API special_spherical_bessel_j0_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_spherical_bessel_j0";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & x, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py.h
new file mode 100644
index 0000000000000000000000000000000000000000..96c3dd17edcd423fe4fdd6a77ad8db11489afd2d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_xlog1py_ops.h>
+
+namespace at {
+
+
+// aten::special_xlog1py(Tensor self, Tensor other) -> Tensor
+inline at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_xlog1py::call(self, other);
+}
+
+// aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor
+inline at::Tensor special_xlog1py(const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_xlog1py_self_scalar::call(self, other);
+}
+
+// aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor special_xlog1py(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_xlog1py_other_scalar::call(self, other);
+}
+
+// aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_xlog1py_out::call(self, other, out);
+}
+// aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_xlog1py_out::call(self, other, out);
+}
+
+// aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_xlog1py_self_scalar_out::call(self, other, out);
+}
+// aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_xlog1py_self_scalar_out::call(self, other, out);
+}
+
+// aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_xlog1py_other_scalar_out::call(self, other, out);
+}
+// aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
+    return at::_ops::special_xlog1py_other_scalar_out::call(self, other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f30b04b8ecd84418d0566fccc332b55b3d662687
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_xlog1py(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..496e84b27a3095bbe1431706adb08800465eaa30
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..aab514614b0fd695284dcceb50e1b1b9dc537c5f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..112df862ff51facbb20e6925329f59bbc5687d17
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f43603eed27900b4c285d2789fe0073f63d88c9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_xlog1py : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a9e1aee35c33e28250bf79860dddef8fcde2dd8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa7ec4cccdb037ded79d45a4d0b77e08f9fa9926
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_xlog1py_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_xlog1py_out : public at::meta::structured_special_xlog1py {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_xlog1py(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlog1py_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d3a16ce7ce20e7c51ed881fcd9595bfc08735b22
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlog1py_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_xlog1py {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_xlog1py(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_xlog1py_self_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "self_scalar";
+  static constexpr const char* schema_str = "special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_xlog1py_other_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "other_scalar";
+  static constexpr const char* schema_str = "special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API special_xlog1py_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_xlog1py_self_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "self_scalar_out";
+  static constexpr const char* schema_str = "special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_xlog1py_other_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlog1py";
+  static constexpr const char* overload_name = "other_scalar_out";
+  static constexpr const char* schema_str = "special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy.h
new file mode 100644
index 0000000000000000000000000000000000000000..de04a7d8db014881a59f76e486505386cbcca1f9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_xlogy_ops.h>
+
+namespace at {
+
+
+// aten::special_xlogy(Tensor self, Tensor other) -> Tensor
+inline at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_xlogy::call(self, other);
+}
+
+// aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor
+inline at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_xlogy_self_scalar::call(self, other);
+}
+
+// aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_xlogy_other_scalar::call(self, other);
+}
+
+// aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_xlogy_out::call(self, other, out);
+}
+// aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_xlogy_out::call(self, other, out);
+}
+
+// aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_xlogy_self_scalar_out::call(self, other, out);
+}
+// aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_xlogy_self_scalar_out::call(self, other, out);
+}
+
+// aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_xlogy_other_scalar_out::call(self, other, out);
+}
+// aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
+    return at::_ops::special_xlogy_other_scalar_out::call(self, other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d81fe60e3167d8c37e9fd3d8e12945b7881fe3d8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,31 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2e42fd18640399d9513ce1a6a958f4b9bffc21dc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlogy_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..75c527395e50d93d051598909149e6daeab9ca6b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_xlogy_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_xlogy {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_xlogy(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_xlogy_self_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "self_scalar";
+  static constexpr const char* schema_str = "special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_xlogy_other_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "other_scalar";
+  static constexpr const char* schema_str = "special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API special_xlogy_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_xlogy_self_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "self_scalar_out";
+  static constexpr const char* schema_str = "special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_xlogy_other_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_xlogy";
+  static constexpr const char* overload_name = "other_scalar_out";
+  static constexpr const char* schema_str = "special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta.h
new file mode 100644
index 0000000000000000000000000000000000000000..b34f5351554f185cee4b36b419ecb4f95536a8bf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/special_zeta_ops.h>
+
+namespace at {
+
+
+// aten::special_zeta(Tensor self, Tensor other) -> Tensor
+inline at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_zeta::call(self, other);
+}
+
+// aten::special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor
+inline at::Tensor special_zeta(const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_zeta_self_scalar::call(self, other);
+}
+
+// aten::special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor special_zeta(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_zeta_other_scalar::call(self, other);
+}
+
+// aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::special_zeta_out::call(self, other, out);
+}
+// aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_zeta_out::call(self, other, out);
+}
+
+// aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::special_zeta_self_scalar_out::call(self, other, out);
+}
+// aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::special_zeta_self_scalar_out::call(self, other, out);
+}
+
+// aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::special_zeta_other_scalar_out::call(self, other, out);
+}
+// aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
+    return at::_ops::special_zeta_other_scalar_out::call(self, other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7e1630d3dcd22d77d4426a14533266d64651539
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor special_zeta(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dbaa2ea7751a49ac5fe43b690948b675d02bdadc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..862c8ee75d249bbdd7f511b190cb07a244feb207
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..047a25d5eeed6446e96653f7ab461f18c8bee9d2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..146e648fdbe2279624b516e442315abae1805c8c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_zeta : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..193fa7399cef3d0d8380e65226095e4ee67c525a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..32add5ba51978fce85be40d5bf846d82d0cc8f87
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/special_zeta_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_special_zeta_out : public at::meta::structured_special_zeta {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor special_zeta(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_zeta_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_zeta(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_zeta_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2e4c901dcb5ad75464bea5e6b715956e9084ec47
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/special_zeta_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_zeta {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "special_zeta(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_zeta_self_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "self_scalar";
+  static constexpr const char* schema_str = "special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API special_zeta_other_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "other_scalar";
+  static constexpr const char* schema_str = "special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API special_zeta_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_zeta_self_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "self_scalar_out";
+  static constexpr const char* schema_str = "special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API special_zeta_other_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::special_zeta";
+  static constexpr const char* overload_name = "other_scalar_out";
+  static constexpr const char* schema_str = "special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split.h
new file mode 100644
index 0000000000000000000000000000000000000000..2873d2d01b25e19a966ef3d374b8ad2bd6599548
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split.h
@@ -0,0 +1,69 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/split_ops.h>
+
+namespace at {
+
+
+// aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0) {
+    return at::_ops::split_sizes::call(self, c10::fromIntArrayRefSlow(split_size), dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0) {
+    return at::_ops::split_sizes::call(self, c10::fromIntArrayRefSlow(split_size), dim);
+  }
+}
+
+// aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0) {
+    return at::_ops::split_sizes::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> split(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0) {
+    return at::_ops::split_sizes::call(self, split_size, dim);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..18c8f6dacc4fa3cbb397ba8ce343d4d9623ab8ea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bbda90516c9ffafaeffbee9d01be274a1f045dab
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..e70bed95ab68f62979b745aad259d0e2f48166cb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/split_copy_ops.h>
+
+namespace at {
+
+
+// aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> split_copy_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> split_copy(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void split_copy_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  void split_copy_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void split_copy_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  void split_copy_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void split_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  void split_copy_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void split_copy_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  void split_copy_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..117be69cc3651cd6eb47ad13e020be29a8535655
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void split_copy_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API void split_copy_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out);
+TORCH_API void split_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+TORCH_API void split_copy_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c5b8b5fa81b9aeddcc134e21adbe11fa11d2f8e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::vector<at::Tensor> split_copy(const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_copy_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8fd78d6bf7a0a34c4d42164577bc2865e6f904a1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void split_copy_Tensor_out(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out);
+TORCH_API ::std::vector<at::Tensor> split_copy_Tensor_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7cc21b27e483a3651321d0ba32102fb53cff318d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API split_copy_Tensor {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymInt, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split_copy";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+};
+
+struct TORCH_API split_copy_Tensor_out {
+  using schema = void (const at::Tensor &, c10::SymInt, int64_t, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split_copy";
+  static constexpr const char* overload_name = "Tensor_out";
+  static constexpr const char* schema_str = "split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()";
+  static void call(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7d65f01e40fd5a2a127915e323eabf6850dbdbd1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> split(const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_symint(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d58f334b5057a9870acc683983d93de33e61923
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API split_Tensor {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymInt, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+};
+
+struct TORCH_API split_sizes {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split";
+  static constexpr const char* overload_name = "sizes";
+  static constexpr const char* schema_str = "split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce689153c37f57d389804be7a218ff03af62a069
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/split_with_sizes_ops.h>
+
+namespace at {
+
+
+// aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
+    return at::_ops::split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) {
+    return at::_ops::split_with_sizes::call(self, c10::fromIntArrayRefSlow(split_sizes), dim);
+  }
+}
+
+// aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> split_with_sizes_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
+    return at::_ops::split_with_sizes::call(self, split_sizes, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) {
+    return at::_ops::split_with_sizes::call(self, split_sizes, dim);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a89b80d7d030d2e10abebac15462bb8d9d786f0e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_with_sizes_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..08fb8e3d81af625a846cae37be570bcf7ef31470
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void split_with_sizes_copy_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void split_with_sizes_copy_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out);
+TORCH_API void split_with_sizes_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void split_with_sizes_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..941b904eff3fcc72b50c30c7b82f2ff8e8f20dc6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::vector<at::Tensor> split_with_sizes_copy(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_with_sizes_copy_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d41fc5896ea79126b7b82c9f236f4991ac927077
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API void split_with_sizes_copy_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void split_with_sizes_copy_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out);
+TORCH_API void split_with_sizes_copy_symint_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void split_with_sizes_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..d94afcbf35f6486d78e3759bb7c8221393b8b706
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void split_with_sizes_copy_out(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out);
+TORCH_API void split_with_sizes_copy_out_cuda(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out);
+TORCH_API ::std::vector<at::Tensor> split_with_sizes_copy_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe19c6124ccc6edef073aa8cc29875a50082103d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API split_with_sizes_copy {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split_with_sizes_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+};
+
+struct TORCH_API split_with_sizes_copy_out {
+  using schema = void (const at::Tensor &, c10::SymIntArrayRef, int64_t, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split_with_sizes_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()";
+  static void call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..59afb3c010e4c88649765fab454f1d8776f82e0a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> split_with_sizes_nested(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..43b430ab68892abe4701bfc424fca653f54f19b6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/split_with_sizes_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API split_with_sizes {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::split_with_sizes";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt.h
new file mode 100644
index 0000000000000000000000000000000000000000..432568ef4f494f6ad132e4bafbfbea43c6c5bc28
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sqrt_ops.h>
+
+namespace at {
+
+
+// aten::sqrt(Tensor self) -> Tensor
+inline at::Tensor sqrt(const at::Tensor & self) {
+    return at::_ops::sqrt::call(self);
+}
+
+// aten::sqrt_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & sqrt_(at::Tensor & self) {
+    return at::_ops::sqrt_::call(self);
+}
+
+// aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::sqrt_out::call(self, out);
+}
+// aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::sqrt_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5c78f87d09af69fad98ed358c18bb47c61f7a84d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor sqrt(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..de5d530a8cf30e1c82665705ee5d25de1647bc4b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor sqrt(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sqrt_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff00aaa2c54ea729e7115e4febd79b130801ac2a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor sqrt(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sqrt_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..edbccc9ac75338754b1e26e9dc92d3766a95e9a6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_sqrt : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..90848bd1d220e529fce895697ba36a6dc5463b51
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor sqrt(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sqrt_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca56d2562f33f91c9cd3ea5a7283b07006b625cc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_native.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/sqrt_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_sqrt_out : public at::meta::structured_sqrt {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+TORCH_API at::Tensor NestedTensor_sqrt(const at::Tensor & self);
+TORCH_API at::Tensor sqrt_sparse(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_sparse_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sqrt_sparse_(at::Tensor & self);
+TORCH_API at::Tensor sqrt_sparse_csr(const at::Tensor & self);
+TORCH_API at::Tensor & sqrt_sparse_csr_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sqrt_sparse_csr_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d98b5cf060123b4aa1700b4f1d5bbc222e28fd23
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sqrt_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sqrt {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sqrt";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sqrt(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API sqrt_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sqrt_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sqrt_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API sqrt_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sqrt";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/square.h b/lib/python3.11/site-packages/torch/include/ATen/ops/square.h
new file mode 100644
index 0000000000000000000000000000000000000000..851c233ae88aa74495f0586c6f361b91b4387790
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/square.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/square_ops.h>
+
+namespace at {
+
+
+// aten::square(Tensor self) -> Tensor
+inline at::Tensor square(const at::Tensor & self) {
+    return at::_ops::square::call(self);
+}
+
+// aten::square_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & square_(at::Tensor & self) {
+    return at::_ops::square_::call(self);
+}
+
+// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & square_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::square_out::call(self, out);
+}
+// aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & square_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::square_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/square_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/square_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..82e8a9f3909816ffca6d42ea8319030a2f9064b8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/square_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor square(const at::Tensor & self);
+TORCH_API at::Tensor & square_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & square_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & square_(at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/square_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/square_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..95d6c1f7ad700a1f6939f6060d7e0e91d5ec6ed1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/square_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor square(const at::Tensor & self);
+TORCH_API at::Tensor & square_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & square_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/square_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/square_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f64ae750b98d8a575e670f266fc9ccb1e61d9662
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/square_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API square {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::square";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "square(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API square_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::square_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "square_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API square_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::square";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze.h
new file mode 100644
index 0000000000000000000000000000000000000000..88c55f7b69f7bc9e7963a33edecf0eb9e8dddc41
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze.h
@@ -0,0 +1,45 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/squeeze_ops.h>
+
+namespace at {
+
+
+// aten::squeeze(Tensor(a) self) -> Tensor(a)
+inline at::Tensor squeeze(const at::Tensor & self) {
+    return at::_ops::squeeze::call(self);
+}
+
+// aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)
+inline at::Tensor squeeze(const at::Tensor & self, int64_t dim) {
+    return at::_ops::squeeze_dim::call(self, dim);
+}
+
+// aten::squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)
+inline at::Tensor squeeze(const at::Tensor & self, at::Dimname dim) {
+    return at::_ops::squeeze_dimname::call(self, dim);
+}
+
+// aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)
+inline at::Tensor squeeze(const at::Tensor & self, at::IntArrayRef dim) {
+    return at::_ops::squeeze_dims::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c5fec0e472580578b4e64f015e0b7a179cc079c4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor squeeze(const at::Tensor & self);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self);
+TORCH_API at::Tensor squeeze(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor squeeze(const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, at::IntArrayRef dim);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..00ca6dfc42be8d1c47723a96f188a37f2a0d3d41
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor squeeze(const at::Tensor & self, at::Dimname dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, at::Dimname dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff4f9298dac95005da509d3b38aa479f1e4071cf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/squeeze_copy_ops.h>
+
+namespace at {
+
+
+// aten::squeeze_copy(Tensor self) -> Tensor
+inline at::Tensor squeeze_copy(const at::Tensor & self) {
+    return at::_ops::squeeze_copy::call(self);
+}
+
+// aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor
+inline at::Tensor squeeze_copy(const at::Tensor & self, int64_t dim) {
+    return at::_ops::squeeze_copy_dim::call(self, dim);
+}
+
+// aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor
+inline at::Tensor squeeze_copy(const at::Tensor & self, at::IntArrayRef dim) {
+    return at::_ops::squeeze_copy_dims::call(self, dim);
+}
+
+// aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::squeeze_copy_out::call(self, out);
+}
+// aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::squeeze_copy_out::call(self, out);
+}
+
+// aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
+    return at::_ops::squeeze_copy_dim_out::call(self, dim, out);
+}
+// aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
+    return at::_ops::squeeze_copy_dim_out::call(self, dim, out);
+}
+
+// aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) {
+    return at::_ops::squeeze_copy_dims_out::call(self, dim, out);
+}
+// aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) {
+    return at::_ops::squeeze_copy_dims_out::call(self, dim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1f1cc35e69e9ce77635c63edb91700aea56f270
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & squeeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out);
+TORCH_API at::Tensor & squeeze_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor & squeeze_copy_outf(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b7eb181051fc30d3e5e8745be9e1b718999e292
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor squeeze_copy(const at::Tensor & self);
+TORCH_API at::Tensor squeeze_copy(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor squeeze_copy(const at::Tensor & self, at::IntArrayRef dim);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee37226e6e711b0d5d8f58ccdc0b3f320a0f50a6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & squeeze_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy(const at::Tensor & self);
+TORCH_API at::Tensor & squeeze_copy_dim_out(const at::Tensor & self, int64_t dim, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy_dim(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & squeeze_copy_dims_out(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy_dims(const at::Tensor & self, at::IntArrayRef dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..9552cf530ab98a76ffc3172d6f2c68d6766f701c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_copy_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API squeeze_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "squeeze_copy(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API squeeze_copy_dim {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "squeeze_copy.dim(Tensor self, int dim) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API squeeze_copy_dims {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "dims";
+  static constexpr const char* schema_str = "squeeze_copy.dims(Tensor self, int[] dim) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim);
+};
+
+struct TORCH_API squeeze_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+struct TORCH_API squeeze_copy_dim_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "dim_out";
+  static constexpr const char* schema_str = "squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out);
+};
+
+struct TORCH_API squeeze_copy_dims_out {
+  using schema = at::Tensor & (const at::Tensor &, at::IntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_copy";
+  static constexpr const char* overload_name = "dims_out";
+  static constexpr const char* schema_str = "squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c6c761606015dd2a6167c4e653fa6a52a90393bf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_native.h
@@ -0,0 +1,34 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor squeeze(const at::Tensor & self);
+TORCH_API at::Tensor squeeze_nested(const at::Tensor & self);
+TORCH_API at::Tensor squeeze_quantized(const at::Tensor & self);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self);
+TORCH_API at::Tensor squeeze(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor squeeze_dim_nested(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor squeeze_quantized(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor squeeze(const at::Tensor & self, at::Dimname dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, at::Dimname dim);
+TORCH_API at::Tensor squeeze(const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor squeeze_dim_nested(const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor squeeze_quantized(const at::Tensor & self, at::IntArrayRef dim);
+TORCH_API at::Tensor & squeeze_(at::Tensor & self, at::IntArrayRef dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb06aa0ddd46c6d7d4e59b13b765b7ae0e7a62d0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/squeeze_ops.h
@@ -0,0 +1,105 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API squeeze {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "squeeze(Tensor(a) self) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API squeeze_dim {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API squeeze_dimname {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze";
+  static constexpr const char* overload_name = "dimname";
+  static constexpr const char* schema_str = "squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::Dimname dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim);
+};
+
+struct TORCH_API squeeze_dims {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze";
+  static constexpr const char* overload_name = "dims";
+  static constexpr const char* schema_str = "squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim);
+};
+
+struct TORCH_API squeeze_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "squeeze_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API squeeze__dim {
+  using schema = at::Tensor & (at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t dim);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API squeeze__dims {
+  using schema = at::Tensor & (at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_";
+  static constexpr const char* overload_name = "dims";
+  static constexpr const char* schema_str = "squeeze_.dims(Tensor(a!) self, int[] dim) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, at::IntArrayRef dim);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::IntArrayRef dim);
+};
+
+struct TORCH_API squeeze__dimname {
+  using schema = at::Tensor & (at::Tensor &, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::squeeze_";
+  static constexpr const char* overload_name = "dimname";
+  static constexpr const char* schema_str = "squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, at::Dimname dim);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm.h
new file mode 100644
index 0000000000000000000000000000000000000000..d0d42132b318da628cea85c23320a45f13b66ae8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sspaddmm_ops.h>
+
+namespace at {
+
+
+// aten::sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor
+inline at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
+    return at::_ops::sspaddmm::call(self, mat1, mat2, beta, alpha);
+}
+
+// aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sspaddmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) {
+    return at::_ops::sspaddmm_out::call(self, mat1, mat2, beta, alpha, out);
+}
+// aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sspaddmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::sspaddmm_out::call(self, mat1, mat2, beta, alpha, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..567b38160dd4f59d63f9081e3f01ffe9aec28409
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a30dda6cc94c7e9aa95d7d83934cf4fb327d8a5f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & sspaddmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sspaddmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9578f0a1921ab0568ad152d1ac17b6c30be10a42
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & sspaddmm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sspaddmm_outf(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..04c4c6418496d52d6f15a6ed15b521c2701ec04c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor sspaddmm(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & _sspaddmm_out_only_sparse(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & _sspaddmm_out_only_sparse_cuda(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & _sspaddmm_out_cpu(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & _sspaddmm_out_cuda(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e46103b52d22df506c477f9b36190589c9b71c6c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sspaddmm_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sspaddmm {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sspaddmm";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha);
+};
+
+struct TORCH_API sspaddmm_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Scalar &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sspaddmm";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stack.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stack.h
new file mode 100644
index 0000000000000000000000000000000000000000..575b938c54e78678d90ef7e905a11a0a549f8c74
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stack.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/stack_ops.h>
+
+namespace at {
+
+
+// aten::stack(Tensor[] tensors, int dim=0) -> Tensor
+inline at::Tensor stack(at::TensorList tensors, int64_t dim=0) {
+    return at::_ops::stack::call(tensors, dim);
+}
+
+// aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & stack_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0) {
+    return at::_ops::stack_out::call(tensors, dim, out);
+}
+// aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & stack_outf(at::TensorList tensors, int64_t dim, at::Tensor & out) {
+    return at::_ops::stack_out::call(tensors, dim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stack_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..473a1f66b86af8d9cba645b5f1a9a696e2969f3e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor stack(at::TensorList tensors, int64_t dim=0);
+TORCH_API at::Tensor & stack_out(at::Tensor & out, at::TensorList tensors, int64_t dim=0);
+TORCH_API at::Tensor & stack_outf(at::TensorList tensors, int64_t dim, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stack_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b04702351bdc36b44d86d3e78d6a2e25fb9c44f6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor stack(at::TensorList tensors, int64_t dim=0);
+TORCH_API at::Tensor & stack_out(at::TensorList tensors, int64_t dim, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stack_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..28301f2ea8add5e37a738bfd5583052ac48b1b04
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stack_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API stack {
+  using schema = at::Tensor (at::TensorList, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stack";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "stack(Tensor[] tensors, int dim=0) -> Tensor";
+  static at::Tensor call(at::TensorList tensors, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim);
+};
+
+struct TORCH_API stack_out {
+  using schema = at::Tensor & (at::TensorList, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stack";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(at::TensorList tensors, int64_t dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std.h
new file mode 100644
index 0000000000000000000000000000000000000000..b89052a70605fe9364f3c677d1858fc693814b62
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std.h
@@ -0,0 +1,86 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/std_ops.h>
+
+namespace at {
+
+
+// aten::std(Tensor self, bool unbiased=True) -> Tensor
+inline at::Tensor std(const at::Tensor & self, bool unbiased) {
+    return at::_ops::std::call(self, unbiased);
+}
+
+// aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
+inline at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor
+inline at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_correction::call(self, dim, correction, keepdim);
+}
+
+// aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_out::call(self, dim, unbiased, keepdim, out);
+}
+// aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) {
+    return at::_ops::std_out::call(self, dim, unbiased, keepdim, out);
+}
+
+// aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_correction_out::call(self, dim, correction, keepdim, out);
+}
+// aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
+    return at::_ops::std_correction_out::call(self, dim, correction, keepdim, out);
+}
+
+// aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
+inline at::Tensor std(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_names_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_names_out::call(self, dim, unbiased, keepdim, out);
+}
+// aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) {
+    return at::_ops::std_names_out::call(self, dim, unbiased, keepdim, out);
+}
+
+// aten::std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor
+inline at::Tensor std(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_correction_names::call(self, dim, correction, keepdim);
+}
+
+// aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_correction_names_out::call(self, dim, correction, keepdim, out);
+}
+// aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
+    return at::_ops::std_correction_names_out::call(self, dim, correction, keepdim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4093da2d17600cf14c9845b5c1be670d11a66cc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,32 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor std(const at::Tensor & self, bool unbiased);
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ead92c4e782fa43da8357e777b7fea6dda547a24
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..57afa6d2238732558b388d6be8e2471d7f34eb4f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean.h
new file mode 100644
index 0000000000000000000000000000000000000000..638b8b53914f8b3889debd0c0a71470f739458e8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean.h
@@ -0,0 +1,59 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/std_mean_ops.h>
+
+namespace at {
+
+
+// aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, bool unbiased) {
+    return at::_ops::std_mean::call(self, unbiased);
+}
+
+// aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_mean_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_mean_correction::call(self, dim, correction, keepdim);
+}
+
+// aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::std_mean_names_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_mean_correction_names::call(self, dim, correction, keepdim);
+}
+
+// aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> std_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::std_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
+}
+// aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> std_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) {
+    return at::_ops::std_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..674693ac41fdb5605adb1de851a302e3b07f0a36
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> std_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> std_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..06f20fb8e9c828956a02477dc488a31a43601b07
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, bool unbiased);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..aaa80b050636f079046c81217df42c89cff2704e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..94862248084b3008aafcbc0b4c9d3c41affa7a0b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e419fb96e69eb443b7b8244a87ade719ee8c5a93
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, bool unbiased=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased=true, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> std_mean_correction_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased=true, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> std_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d59163f2384297fc1f3b0fb363b59850a64892f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_mean_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API std_mean {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, bool unbiased);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased);
+};
+
+struct TORCH_API std_mean_dim {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::OptionalIntArrayRef, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API std_mean_correction {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "correction";
+  static constexpr const char* schema_str = "std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API std_mean_names_dim {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::DimnameList, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "names_dim";
+  static constexpr const char* schema_str = "std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API std_mean_correction_names {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "correction_names";
+  static constexpr const char* schema_str = "std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API std_mean_correction_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &> (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std_mean";
+  static constexpr const char* overload_name = "correction_out";
+  static constexpr const char* schema_str = "std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))";
+  static ::std::tuple<at::Tensor &,at::Tensor &> call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+  static ::std::tuple<at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f224606a81d984798a49152878c699b2f18a92c0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_native.h
@@ -0,0 +1,31 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor std(const at::Tensor & self, bool unbiased=true);
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased=true, bool keepdim=false);
+TORCH_API at::Tensor & std_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std_quantized_cpu(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out_quantized_cpu(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, bool unbiased=true, bool keepdim=false);
+TORCH_API at::Tensor & std_out(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/std_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/std_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..70f99175ab9c1ce5382948607ee52b037d6d76a0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/std_ops.h
@@ -0,0 +1,116 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API std {
+  using schema = at::Tensor (const at::Tensor &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "std(Tensor self, bool unbiased=True) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, bool unbiased);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased);
+};
+
+struct TORCH_API std_dim {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API std_correction {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "correction";
+  static constexpr const char* schema_str = "std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API std_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalIntArrayRef, bool, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API std_correction_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "correction_out";
+  static constexpr const char* schema_str = "std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API std_names_dim {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "names_dim";
+  static constexpr const char* schema_str = "std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API std_names_out {
+  using schema = at::Tensor & (const at::Tensor &, at::DimnameList, bool, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "names_out";
+  static constexpr const char* schema_str = "std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API std_correction_names {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "correction_names";
+  static constexpr const char* schema_str = "std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API std_correction_names_out {
+  using schema = at::Tensor & (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::std";
+  static constexpr const char* overload_name = "correction_names_out";
+  static constexpr const char* schema_str = "std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stft.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stft.h
new file mode 100644
index 0000000000000000000000000000000000000000..694eb0fda6980ef378a4fe6b39ef337d8546339f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stft.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/stft_ops.h>
+
+namespace at {
+
+
+// aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
+inline at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt) {
+    return at::_ops::stft::call(self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex);
+}
+
+// aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor
+inline at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool center=true, c10::string_view pad_mode="reflect", bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt) {
+    return at::_ops::stft_center::call(self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stft_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9ffcda86147e98018ed59b030bbfb80a7490f288
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt);
+TORCH_API at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool center=true, c10::string_view pad_mode="reflect", bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stft_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..85597e0c8e7d772d9a2c6d1ca82a470ca3805ebb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt);
+TORCH_API at::Tensor stft(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length=::std::nullopt, ::std::optional<int64_t> win_length=::std::nullopt, const ::std::optional<at::Tensor> & window={}, bool center=true, c10::string_view pad_mode="reflect", bool normalized=false, ::std::optional<bool> onesided=::std::nullopt, ::std::optional<bool> return_complex=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stft_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e9016d21b7dfa37dcdb061a57c93d31071c48f6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stft_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API stft {
+  using schema = at::Tensor (const at::Tensor &, int64_t, ::std::optional<int64_t>, ::std::optional<int64_t>, const ::std::optional<at::Tensor> &, bool, ::std::optional<bool>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stft";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex);
+};
+
+struct TORCH_API stft_center {
+  using schema = at::Tensor (const at::Tensor &, int64_t, ::std::optional<int64_t>, ::std::optional<int64_t>, const ::std::optional<at::Tensor> &, bool, c10::string_view, bool, ::std::optional<bool>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stft";
+  static constexpr const char* overload_name = "center";
+  static constexpr const char* schema_str = "stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode=\"reflect\", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool center, c10::string_view pad_mode, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n_fft, ::std::optional<int64_t> hop_length, ::std::optional<int64_t> win_length, const ::std::optional<at::Tensor> & window, bool center, c10::string_view pad_mode, bool normalized, ::std::optional<bool> onesided, ::std::optional<bool> return_complex);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stride.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stride.h
new file mode 100644
index 0000000000000000000000000000000000000000..3861df3bcf90e6539aa24b44f732d0a5e817e897
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stride.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/stride_ops.h>
+
+namespace at {
+
+
+// aten::stride.int(Tensor self, int dim) -> int
+inline int64_t __dispatch_stride(const at::Tensor & self, int64_t dim) {
+    return at::_ops::stride_int::call(self, dim);
+}
+
+// aten::stride.Dimname(Tensor self, Dimname dim) -> int
+inline int64_t stride(const at::Tensor & self, at::Dimname dim) {
+    return at::_ops::stride_Dimname::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stride_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9914ee31e9de530d9a6dcf8e993d76055a925089
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API int64_t stride(const at::Tensor & self, int64_t dim);
+TORCH_API int64_t stride(const at::Tensor & self, at::Dimname dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stride_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4f39154a6b864c53b967283a8006c08e4fd0b7c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API int64_t stride(const at::Tensor & self, int64_t dim);
+TORCH_API int64_t stride(const at::Tensor & self, at::Dimname dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/stride_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4cdc458c078e11fe6685868df0f15b68fa09b51
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/stride_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API stride_int {
+  using schema = int64_t (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stride";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "stride.int(Tensor self, int dim) -> int";
+  static int64_t call(const at::Tensor & self, int64_t dim);
+  static int64_t redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API stride_Dimname {
+  using schema = int64_t (const at::Tensor &, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::stride";
+  static constexpr const char* overload_name = "Dimname";
+  static constexpr const char* schema_str = "stride.Dimname(Tensor self, Dimname dim) -> int";
+  static int64_t call(const at::Tensor & self, at::Dimname dim);
+  static int64_t redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e0615306998f01a745a5e34d528920a30bb05f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub.h
@@ -0,0 +1,53 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sub_ops.h>
+
+namespace at {
+
+
+// aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::sub_out::call(self, other, alpha, out);
+}
+// aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::sub_out::call(self, other, alpha, out);
+}
+
+// aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
+inline at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::sub_Tensor::call(self, other, alpha);
+}
+
+// aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
+inline at::Tensor sub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::sub_Scalar::call(self, other, alpha);
+}
+
+// aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::sub_Scalar_out::call(self, other, alpha, out);
+}
+// aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sub_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::sub_Scalar_out::call(self, other, alpha, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff484b19f1bf1e3cea32192a2e4cad37162776ed
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_outf(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e31e2acb97558d0a82e17f65a84c510c6bfa94a4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..db527c71af8f49fa8957519e238e7f1772964eaf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..295e9658d8ffe3b812227696874143f853593190
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6deeff8fc63e8e01cc8c663344a2a5afe72d85c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_sub_Tensor : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..073a975dc47b02606018ea63e65c3125d8cb4c36
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..577056bd84523b19e5621885928e12a59c170a33
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_native.h
@@ -0,0 +1,31 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/sub_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_sub_out : public at::meta::structured_sub_Tensor {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, const at::Tensor & out);
+};
+TORCH_API at::Tensor NestedTensor_sub_Tensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor sub_sparse(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_out_sparse(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_sparse_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor sub_zerotensor(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor sub(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & sub_Scalar_out(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & sub_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sub_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f4cefe3391ab62e6b83f937fcccfb90e421ad9f5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sub_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sub_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+};
+
+struct TORCH_API sub_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API sub__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub_";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API sub_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API sub__Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub_";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API sub_Scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sub";
+  static constexpr const char* overload_name = "Scalar_out";
+  static constexpr const char* schema_str = "sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/subtract.h b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc7c67711c12fc03a572aa9492bcc84e2611527f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/subtract_ops.h>
+
+namespace at {
+
+
+// aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & subtract_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::subtract_out::call(self, other, alpha, out);
+}
+// aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & subtract_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) {
+    return at::_ops::subtract_out::call(self, other, alpha, out);
+}
+
+// aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor
+inline at::Tensor subtract(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) {
+    return at::_ops::subtract_Tensor::call(self, other, alpha);
+}
+
+// aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor
+inline at::Tensor subtract(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) {
+    return at::_ops::subtract_Scalar::call(self, other, alpha);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..45aaf96b1fa3d61e3c67e4c80a42e8ba431442cc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor subtract(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & subtract_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & subtract_outf(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & subtract_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor subtract(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & subtract_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac5d6ecf2d6d293815c852b49eb877b84d9aae93
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor subtract(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & subtract_out(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+TORCH_API at::Tensor & subtract_(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor subtract(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+TORCH_API at::Tensor & subtract_(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ead3808ba9047c07d6d308e96569bb3695f3c80b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/subtract_ops.h
@@ -0,0 +1,72 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API subtract_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::subtract";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out);
+};
+
+struct TORCH_API subtract_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::subtract";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API subtract__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::subtract_";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API subtract_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::subtract";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+};
+
+struct TORCH_API subtract__Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::subtract_";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bca11851f694d0231655960d229f0892834c8d0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum.h
@@ -0,0 +1,67 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sum_ops.h>
+
+namespace at {
+
+
+// aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor sum(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum::call(self, dtype);
+}
+
+// aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum_dim_IntList::call(self, dim, keepdim, dtype);
+}
+
+// aten::sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
+inline at::Tensor sum(const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum_dim_DimnameList::call(self, dim, keepdim, dtype);
+}
+
+// aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum_IntList_out::call(self, dim, keepdim, dtype, out);
+}
+// aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::sum_IntList_out::call(self, dim, keepdim, dtype, out);
+}
+
+// aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum_DimnameList_out::call(self, dim, keepdim, dtype, out);
+}
+// aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_outf(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::sum_DimnameList_out::call(self, dim, keepdim, dtype, out);
+}
+
+// aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::sum_out::call(self, dtype, out);
+}
+// aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sum_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::sum_out::call(self, dtype, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..22795dcb4bb2a517a83ba061c613599a96d6f149
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a1e048b351e33580fa4a68397d8eb1cc3cc86abe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a449f4425c7e5aeac0079f2d89b8e0f6c34596e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_outf(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..101cc64608265a155a041a804684a4d58ec658cb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9af8e2db94e8df30fe643848fc7c3d7cdebc5b07
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ccd8d51b526318de67fe8ccd0a9a71b3903d5813
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_sum_dim_IntList : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dfbac17bd7943b1023ffc400c3c11a29fcd59b02
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor sum(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..168e9d94eb7d26c80a1c18a25e77ec9d141d4a0a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_native.h
@@ -0,0 +1,32 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/sum_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor sum(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+TORCH_API at::Tensor sum_coo(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor sum_csr(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+struct TORCH_API structured_sum_out : public at::meta::structured_sum_dim_IntList {
+void impl(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, const at::Tensor & out);
+};
+TORCH_API at::Tensor NestedTensor_sum_dim_CPU(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor sum_sparse_coo(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor sum_sparse_compressed(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor sum(const at::Tensor & self, at::DimnameList dim, bool keepdim=false, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & sum_out(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..026049a694327b2f6d3c7c413dc38c7770d6fd29
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sum {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sum(Tensor self, *, ScalarType? dtype=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API sum_dim_IntList {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef, bool, ::std::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "dim_IntList";
+  static constexpr const char* schema_str = "sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API sum_dim_DimnameList {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, bool, ::std::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "dim_DimnameList";
+  static constexpr const char* schema_str = "sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API sum_IntList_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalIntArrayRef, bool, ::std::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "IntList_out";
+  static constexpr const char* schema_str = "sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+struct TORCH_API sum_DimnameList_out {
+  using schema = at::Tensor & (const at::Tensor &, at::DimnameList, bool, ::std::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "DimnameList_out";
+  static constexpr const char* schema_str = "sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+struct TORCH_API sum_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size.h
new file mode 100644
index 0000000000000000000000000000000000000000..9ff1cf19a45ec710a8a24e2ec67fc56d8ab8ca7d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sum_to_size_ops.h>
+
+namespace at {
+
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor sum_to_size(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::sum_to_size::call(self, c10::fromIntArrayRefSlow(size));
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor sum_to_size(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::sum_to_size::call(self, size);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe1a50bad0646c1b36a54ce2e4f630b32ab5deb9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor sum_to_size(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor sum_to_size_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e4cff25988654205f6146062a4e72593fd62f13
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor sum_to_size_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..02ba24eaef2fcb975b197c7e26ad4ca446addb62
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sum_to_size_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sum_to_size {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sum_to_size";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sum_to_size(Tensor self, SymInt[] size) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/svd.h b/lib/python3.11/site-packages/torch/include/ATen/ops/svd.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd066913b1c40a2982afe7a5c60a41452e912512
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/svd.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/svd_ops.h>
+
+namespace at {
+
+
+// aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some=true, bool compute_uv=true) {
+    return at::_ops::svd_U::call(self, some, compute_uv, U, S, V);
+}
+// aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_outf(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V) {
+    return at::_ops::svd_U::call(self, some, compute_uv, U, S, V);
+}
+
+// aten::svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true) {
+    return at::_ops::svd::call(self, some, compute_uv);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/svd_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ded1579c82c630e6e7c4ce60e063f4e32795844
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_outf(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/svd_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..162ac14e767af4cf3bf25cf558c21a279cbafe7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> svd(const at::Tensor & self, bool some=true, bool compute_uv=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> svd_out(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/svd_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4d97f4b4d697c9f1ec6cad18a8affe8c3da8748a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/svd_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API svd_U {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (const at::Tensor &, bool, bool, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::svd";
+  static constexpr const char* overload_name = "U";
+  static constexpr const char* schema_str = "svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)";
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> call(const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V);
+};
+
+struct TORCH_API svd {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor> (const at::Tensor &, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::svd";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V)";
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> call(const at::Tensor & self, bool some, bool compute_uv);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, bool compute_uv);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes.h
new file mode 100644
index 0000000000000000000000000000000000000000..7927ef54846bd06550082f1e557550bc41b37148
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/swapaxes_ops.h>
+
+namespace at {
+
+
+// aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)
+inline at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1) {
+    return at::_ops::swapaxes::call(self, axis0, axis1);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f05972c32a4a7aa87f61f55cf2c2bbbd8d7301a2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1);
+TORCH_API at::Tensor & swapaxes_(at::Tensor & self, int64_t axis0, int64_t axis1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4d9bb3420426cd54bfdfbf3abe9c3e73468e2386
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor swapaxes(const at::Tensor & self, int64_t axis0, int64_t axis1);
+TORCH_API at::Tensor & swapaxes_(at::Tensor & self, int64_t axis0, int64_t axis1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..85a459ebdd11ecad0cacb90b4a99d214701a4060
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapaxes_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API swapaxes {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::swapaxes";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t axis0, int64_t axis1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t axis0, int64_t axis1);
+};
+
+struct TORCH_API swapaxes_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::swapaxes_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t axis0, int64_t axis1);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t axis0, int64_t axis1);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims.h
new file mode 100644
index 0000000000000000000000000000000000000000..f75fc73538f9717aba4355889fecbba53255860f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/swapdims_ops.h>
+
+namespace at {
+
+
+// aten::swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
+inline at::Tensor swapdims(const at::Tensor & self, int64_t dim0, int64_t dim1) {
+    return at::_ops::swapdims::call(self, dim0, dim1);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f28095cff8865335712fdbfcdd4c06dfc57e5543
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor swapdims(const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor & swapdims_(at::Tensor & self, int64_t dim0, int64_t dim1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..facf4c218af9a0f7f73d1e688f1c497fd9df43d2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor swapdims(const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor & swapdims_(at::Tensor & self, int64_t dim0, int64_t dim1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..179bb5a73ed6c9d91898be74a25fbebec9c32be1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/swapdims_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API swapdims {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::swapdims";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+struct TORCH_API swapdims_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::swapdims_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range.h
new file mode 100644
index 0000000000000000000000000000000000000000..0c89bea227cb953521a6a295976cd6e50dd723f1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_constrain_range_ops.h>
+
+namespace at {
+
+
+// aten::sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> ()
+inline void sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt) {
+    return at::_ops::sym_constrain_range::call(size, min, max);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7fb9079767da061f9669f21ba8f201fb5945385
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size.h
new file mode 100644
index 0000000000000000000000000000000000000000..078f9a53f7d8d34dfc678e0fe2bd0e1f76ead97a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_constrain_range_for_size_ops.h>
+
+namespace at {
+
+
+// aten::sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> ()
+inline void sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt) {
+    return at::_ops::sym_constrain_range_for_size::call(size, min, max);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b62385cffc25d3b9a99643a640c35b527bfc3e8f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..35e4aea282c752a41e69cf1102d8fd8413a65a9a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void sym_constrain_range_for_size(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..94834f3b52f20ecee9d45e87f6e7e482f2e350e1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_for_size_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_constrain_range_for_size {
+  using schema = void (const at::Scalar &, ::std::optional<int64_t>, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_constrain_range_for_size";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> ()";
+  static void call(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b32708c109bde4f086ab0a248c44cf630ef4ea73
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void sym_constrain_range(const at::Scalar & size, ::std::optional<int64_t> min=::std::nullopt, ::std::optional<int64_t> max=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f268a89ad14c5208ba33b6867e1cb2a95cbfc096
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_constrain_range_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_constrain_range {
+  using schema = void (const at::Scalar &, ::std::optional<int64_t>, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_constrain_range";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> ()";
+  static void call(const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, ::std::optional<int64_t> min, ::std::optional<int64_t> max);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf5e612f9b14791f9ace54ed8041a34c682ad990
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_numel_ops.h>
+
+namespace at {
+
+
+// aten::sym_numel(Tensor self) -> SymInt
+inline c10::SymInt __dispatch_sym_numel(const at::Tensor & self) {
+    return at::_ops::sym_numel::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7ff7c4857a5a55fd9151c6afd0dfa200e63ce09
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_numel(const at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5f7c26ce279300a161f052e574f55cb4fad2d53f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API c10::SymInt sym_numel(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..900036b1b879b050a622994d7642fe348ff4d978
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_numel_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_numel {
+  using schema = c10::SymInt (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_numel";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sym_numel(Tensor self) -> SymInt";
+  static c10::SymInt call(const at::Tensor & self);
+  static c10::SymInt redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b00a560f0fa5c076970eba45b306ebedc515af8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_size_ops.h>
+
+namespace at {
+
+
+// aten::sym_size.int(Tensor self, int dim) -> SymInt
+inline c10::SymInt __dispatch_sym_size(const at::Tensor & self, int64_t dim) {
+    return at::_ops::sym_size_int::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..44634d72c4541d512729ac492766758ccfc3c657
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_size(const at::Tensor & self, int64_t dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a9b32bac1ed713cd4149d1cbebe181d6a5f8cb4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API c10::SymInt sym_size(const at::Tensor & self, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b39fdbf7edd9698a33bdabf8df01b5a3d61e9759
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_size_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_size_int {
+  using schema = c10::SymInt (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_size";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "sym_size.int(Tensor self, int dim) -> SymInt";
+  static c10::SymInt call(const at::Tensor & self, int64_t dim);
+  static c10::SymInt redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e08db26022623701257833fb7b4ea1fff7f4bdb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_storage_offset_ops.h>
+
+namespace at {
+
+
+// aten::sym_storage_offset(Tensor self) -> SymInt
+inline c10::SymInt __dispatch_sym_storage_offset(const at::Tensor & self) {
+    return at::_ops::sym_storage_offset::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..56bb86216b37b67225000ae99e652919f75b38bf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_storage_offset(const at::Tensor & self);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..09821bb9b5604dda09d7e2d46b336f3b5f722a2a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API c10::SymInt sym_storage_offset(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a18e3e0e6d55c9828af45d045f61172c496cdce
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_storage_offset_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_storage_offset {
+  using schema = c10::SymInt (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_storage_offset";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "sym_storage_offset(Tensor self) -> SymInt";
+  static c10::SymInt call(const at::Tensor & self);
+  static c10::SymInt redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride.h
new file mode 100644
index 0000000000000000000000000000000000000000..13c9edd8304a654a1ad78d50ef166a44628f6755
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/sym_stride_ops.h>
+
+namespace at {
+
+
+// aten::sym_stride.int(Tensor self, int dim) -> SymInt
+inline c10::SymInt __dispatch_sym_stride(const at::Tensor & self, int64_t dim) {
+    return at::_ops::sym_stride_int::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2db949a65823a66ebab3bb304a9e946d2f51c286
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_stride(const at::Tensor & self, int64_t dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae9fac9b5e5e987624937a712bab6f9060eaf639
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API c10::SymInt sym_stride(const at::Tensor & self, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a2b672c3098e00cff077ec57b7797a743d458c91
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/sym_stride_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sym_stride_int {
+  using schema = c10::SymInt (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::sym_stride";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "sym_stride.int(Tensor self, int dim) -> SymInt";
+  static c10::SymInt call(const at::Tensor & self, int64_t dim);
+  static c10::SymInt redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t.h
new file mode 100644
index 0000000000000000000000000000000000000000..05c4d9d5b802d7d6371bde31f3973a6eb29eda61
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/t_ops.h>
+
+namespace at {
+
+
+// aten::t(Tensor(a) self) -> Tensor(a)
+inline at::Tensor t(const at::Tensor & self) {
+    return at::_ops::t::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f001ff5cbb6e8980286929dcc5ec848006c5767
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor t(const at::Tensor & self);
+TORCH_API at::Tensor & t_(at::Tensor & self);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..e562aef2b818e8418664c6e06031a3f2f76acf46
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/t_copy_ops.h>
+
+namespace at {
+
+
+// aten::t_copy(Tensor self) -> Tensor
+inline at::Tensor t_copy(const at::Tensor & self) {
+    return at::_ops::t_copy::call(self);
+}
+
+// aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & t_copy_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::t_copy_out::call(self, out);
+}
+// aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & t_copy_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::t_copy_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..49d4f876de01faf504d80cb6137b7429830c553b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & t_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & t_copy_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b4c63fe18fbe439b7be67c05ff099dc96fd0846
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor t_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ad66a88027ebb856621d604f4991ddbd4d225b0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & t_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor t_copy(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..eb790f58a630a99f35cc8aba8eb24c5b1a34f8b8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API t_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::t_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "t_copy(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API t_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::t_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..94c9acbe526ef0c48e83f69bd62a44600d9c8f0b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor t(const at::Tensor & self);
+TORCH_API at::Tensor & t_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/t_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/t_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3f837f8405777f507bf1230660c36a8cfac1c22f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/t_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API t {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::t";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "t(Tensor(a) self) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API t_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::t_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "t_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take.h
new file mode 100644
index 0000000000000000000000000000000000000000..aab62f0f3f8c0ba2d1636a67237b2baf124a6156
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/take_ops.h>
+
+namespace at {
+
+
+// aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & take_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index) {
+    return at::_ops::take_out::call(self, index, out);
+}
+// aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & take_outf(const at::Tensor & self, const at::Tensor & index, at::Tensor & out) {
+    return at::_ops::take_out::call(self, index, out);
+}
+
+// aten::take(Tensor self, Tensor index) -> Tensor
+inline at::Tensor take(const at::Tensor & self, const at::Tensor & index) {
+    return at::_ops::take::call(self, index);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim.h
new file mode 100644
index 0000000000000000000000000000000000000000..1d1437349dfb4f16bfde9db4578dc13ccd363d9a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/take_along_dim_ops.h>
+
+namespace at {
+
+
+// aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & take_along_dim_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt) {
+    return at::_ops::take_along_dim_out::call(self, indices, dim, out);
+}
+// aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & take_along_dim_outf(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out) {
+    return at::_ops::take_along_dim_out::call(self, indices, dim, out);
+}
+
+// aten::take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor
+inline at::Tensor take_along_dim(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt) {
+    return at::_ops::take_along_dim::call(self, indices, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7b4dcbd18c74ee71abc570b39c68411e29d7202
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor take_along_dim(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt);
+TORCH_API at::Tensor & take_along_dim_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt);
+TORCH_API at::Tensor & take_along_dim_outf(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9480d140983945162956e000063a77c90b21c55a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor take_along_dim(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim=::std::nullopt);
+TORCH_API at::Tensor & take_along_dim_out(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..622a9474fa4da394944a61600bcb0b17a9f67350
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_along_dim_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API take_along_dim_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, ::std::optional<int64_t>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::take_along_dim";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim, at::Tensor & out);
+};
+
+struct TORCH_API take_along_dim {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::take_along_dim";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, ::std::optional<int64_t> dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..980c00e7f4bf16c41d87992e2571790f0b35d47a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor take(const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_outf(const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..68e3727e44bb341665c6218bdab80302b2a2a6e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor take(const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_outf(const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa2268e3b90622ead58bb904c3ca516127c24d84
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor take(const at::Tensor & self, const at::Tensor & index);
+TORCH_API at::Tensor & take_out(const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/take_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/take_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba83ab32e7e100bb11a0f359aecf9f32468c88b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/take_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API take_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::take";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, at::Tensor & out);
+};
+
+struct TORCH_API take {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::take";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "take(Tensor self, Tensor index) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & index);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan.h
new file mode 100644
index 0000000000000000000000000000000000000000..a0ecc11ed11ab9da0fee5349175b69db144d8b8f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tan_ops.h>
+
+namespace at {
+
+
+// aten::tan(Tensor self) -> Tensor
+inline at::Tensor tan(const at::Tensor & self) {
+    return at::_ops::tan::call(self);
+}
+
+// aten::tan_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & tan_(at::Tensor & self) {
+    return at::_ops::tan_::call(self);
+}
+
+// aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::tan_out::call(self, out);
+}
+// aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::tan_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b84dd7d62e6b8f00b45c5de9dc37b1906d78da79
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor tan(const at::Tensor & self);
+TORCH_API at::Tensor & tan_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9b5e6278988dd1e97c94729d1d27974986eba1f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor tan(const at::Tensor & self);
+TORCH_API at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tan_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..18daddc556443931ec489041fcf5c5481b1b74f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor tan(const at::Tensor & self);
+TORCH_API at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tan_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..53fdaa0cec47cf35d457ba60cc49aca7c51f0d75
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_tan : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ab10b6feef4def8c7125c3430f05ded3b563144
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor tan(const at::Tensor & self);
+TORCH_API at::Tensor & tan_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tan_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tan_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..bef7c9b2fff5e27233960edecfd2f9ddf6ac0c0e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/tan_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_tan_out : public at::meta::structured_tan {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+TORCH_API at::Tensor tan_sparse(const at::Tensor & self);
+TORCH_API at::Tensor & tan_sparse_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tan_sparse_(at::Tensor & self);
+TORCH_API at::Tensor tan_sparse_csr(const at::Tensor & self);
+TORCH_API at::Tensor & tan_sparse_csr_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tan_sparse_csr_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tan_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..434f388551288bf661a9909815d1669f86ca92d8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tan_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tan {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tan";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tan(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API tan_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tan_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tan_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API tan_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tan";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a5022de6d7429d7a74daaf8053da877f3b1cc7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tanh_ops.h>
+
+namespace at {
+
+
+// aten::tanh(Tensor self) -> Tensor
+inline at::Tensor tanh(const at::Tensor & self) {
+    return at::_ops::tanh::call(self);
+}
+
+// aten::tanh_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & tanh_(at::Tensor & self) {
+    return at::_ops::tanh_::call(self);
+}
+
+// aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::tanh_out::call(self, out);
+}
+// aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::tanh_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..6dbf344d62adfba82f0b5c393bb80ad7933048bf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tanh_backward_ops.h>
+
+namespace at {
+
+
+// aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) {
+    return at::_ops::tanh_backward_grad_input::call(grad_output, output, grad_input);
+}
+// aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) {
+    return at::_ops::tanh_backward_grad_input::call(grad_output, output, grad_input);
+}
+
+// aten::tanh_backward(Tensor grad_output, Tensor output) -> Tensor
+inline at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output) {
+    return at::_ops::tanh_backward::call(grad_output, output);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6753baba53931e77faee19f7a7f197229656b1c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..83dc8433ec76324f0b4174e828a8abaa31880a34
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b9ca029d3f2ea1e530a33a5b5b14c2d77ce8c539
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..8239bf39dd71281ecab9407282d89696377bfc68
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_tanh_backward : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & grad_output, const at::Tensor & output);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e278ce95589d108c109e31000961a2fd57ceeeb1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor tanh_backward(const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output);
+TORCH_API at::Tensor & tanh_backward_outf(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e4092d377a55121da6ddb38cf1dbe4e80af238c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/tanh_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_tanh_backward_out : public at::meta::structured_tanh_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..70894ef354c114109e9bc739caf573e54f96e9f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tanh_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tanh_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input);
+};
+
+struct TORCH_API tanh_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tanh_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tanh_backward(Tensor grad_output, Tensor output) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & output);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..044d12b73b4e6647c1a5a1b055b03e3ac1d70373
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor tanh(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b96e1b399c5f2f2d37fb003cb0bf4a71570ed17
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor tanh(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tanh_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..729c490a0328035dea4e34daaf771e63954c842a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor tanh(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tanh_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..1f504edb2a97eacd9bfbae61854092080181dc3e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_tanh : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..222cbb467d5c5f2772afed9916c0a9bfd55ec994
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor tanh(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & tanh_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tanh_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..67a8f5d3e2a1a54d502e235b67c5e5d82da7eda4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_native.h
@@ -0,0 +1,34 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/tanh_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_tanh_out : public at::meta::structured_tanh {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+TORCH_API at::Tensor NestedTensor_tanh(const at::Tensor & self);
+TORCH_API at::Tensor & NestedTensor_tanh_(at::Tensor & self);
+TORCH_API at::Tensor tanh_sparse(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_sparse_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tanh_sparse_(at::Tensor & self);
+TORCH_API at::Tensor tanh_sparse_csr(const at::Tensor & self);
+TORCH_API at::Tensor & tanh_sparse_csr_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & tanh_sparse_csr_(at::Tensor & self);
+TORCH_API at::Tensor mkldnn_tanh(const at::Tensor & self);
+TORCH_API at::Tensor & mkldnn_tanh_(at::Tensor & self);
+TORCH_API at::Tensor tanh_quantized_cpu(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..5513a15f68e33d327e4647fef91c5f67a3d83e7c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tanh_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tanh {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tanh";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tanh(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API tanh_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tanh_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tanh_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API tanh_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tanh";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensor.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..2f72b7ef02637983d35f5049bdb6d1bd613cd2bd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor.h
@@ -0,0 +1,30 @@
+#pragma once
+#include <ATen/core/Tensor.h>
+#include <c10/core/ScalarType.h>
+
+namespace at {
+
+// These functions are defined in ATen/Utils.cpp.
+#define TENSOR(T, S)                                                          \
+  TORCH_API Tensor tensor(ArrayRef<T> values, const TensorOptions& options);  \
+  inline Tensor tensor(                                                       \
+      std::initializer_list<T> values, const TensorOptions& options) {        \
+    return at::tensor(ArrayRef<T>(values), options);                          \
+  }                                                                           \
+  inline Tensor tensor(T value, const TensorOptions& options) {               \
+    return at::tensor(ArrayRef<T>(value), options);                           \
+  }                                                                           \
+  inline Tensor tensor(ArrayRef<T> values) {                                  \
+    return at::tensor(std::move(values), at::dtype(k##S));                    \
+  }                                                                           \
+  inline Tensor tensor(std::initializer_list<T> values) {                     \
+    return at::tensor(ArrayRef<T>(values));                                   \
+  }                                                                           \
+  inline Tensor tensor(T value) {                                             \
+    return at::tensor(ArrayRef<T>(value));                                    \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TENSOR)
+AT_FORALL_COMPLEX_TYPES(TENSOR)
+#undef TENSOR
+
+}  // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e13ef3bd8abdb41e7bb9e3b8e1a320cc516b176
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split.h
@@ -0,0 +1,74 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tensor_split_ops.h>
+
+namespace at {
+
+
+// aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, int64_t sections, int64_t dim=0) {
+    return at::_ops::tensor_split_sections::call(self, sections, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, int64_t sections, int64_t dim=0) {
+    return at::_ops::tensor_split_sections::call(self, sections, dim);
+  }
+}
+
+// aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymInt sections, int64_t dim=0) {
+    return at::_ops::tensor_split_sections::call(self, sections, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymInt sections, int64_t dim=0) {
+    return at::_ops::tensor_split_sections::call(self, sections, dim);
+  }
+}
+
+// aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0) {
+    return at::_ops::tensor_split_indices::call(self, c10::fromIntArrayRefSlow(indices), dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0) {
+    return at::_ops::tensor_split_indices::call(self, c10::fromIntArrayRefSlow(indices), dim);
+  }
+}
+
+// aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0) {
+    return at::_ops::tensor_split_indices::call(self, indices, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0) {
+    return at::_ops::tensor_split_indices::call(self, indices, dim);
+  }
+}
+
+// aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim=0) {
+    return at::_ops::tensor_split_tensor_indices_or_sections::call(self, tensor_indices_or_sections, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..53b572dd4dcdd6d9b1c584c250c3ea2b989fb7e2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,27 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, int64_t sections, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymInt sections, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split_symint(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim=0);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..207ea050e6b2fe7b83c18a7c818b2e042bea2aae
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> tensor_split_sections_symint(const at::Tensor & self, c10::SymInt sections, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split_indices_symint(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> tensor_split(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e1c56e7cecd7bd27c857e8dd71e24dd1e1f9db3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensor_split_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tensor_split_sections {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymInt, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tensor_split";
+  static constexpr const char* overload_name = "sections";
+  static constexpr const char* schema_str = "tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymInt sections, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt sections, int64_t dim);
+};
+
+struct TORCH_API tensor_split_indices {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tensor_split";
+  static constexpr const char* overload_name = "indices";
+  static constexpr const char* schema_str = "tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim);
+};
+
+struct TORCH_API tensor_split_tensor_indices_or_sections {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tensor_split";
+  static constexpr const char* overload_name = "tensor_indices_or_sections";
+  static constexpr const char* schema_str = "tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot.h
new file mode 100644
index 0000000000000000000000000000000000000000..d883b84a5ff3a79a33c0242407d2dbef256074bd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tensordot_ops.h>
+
+namespace at {
+
+
+// aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor
+inline at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) {
+    return at::_ops::tensordot::call(self, other, dims_self, dims_other);
+}
+
+// aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tensordot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) {
+    return at::_ops::tensordot_out::call(self, other, dims_self, dims_other, out);
+}
+// aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tensordot_outf(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) {
+    return at::_ops::tensordot_out::call(self, other, dims_self, dims_other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d62319161eaf12e4f7958fd3ef0f220c5ce13d8a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other);
+TORCH_API at::Tensor & tensordot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other);
+TORCH_API at::Tensor & tensordot_outf(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..dab4bd136dc56f166bd44f6d6b2b920da4539aa0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor tensordot(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other);
+TORCH_API at::Tensor & tensordot_out(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f7dbd3bff8d2bd71f19e4da96d324c50b2e040a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tensordot_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tensordot {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, at::IntArrayRef, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tensordot";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other);
+};
+
+struct TORCH_API tensordot_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::IntArrayRef, at::IntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tensordot";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..7dc681bc3ed1f02be3c68274c06eabbe2cae8065
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/thnn_conv2d_ops.h>
+
+namespace at {
+
+
+// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & thnn_conv2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
+  }
+}
+
+// aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & thnn_conv2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::thnn_conv2d_out::call(self, weight, kernel_size, bias, stride, padding, out);
+  }
+}
+
+// aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor
+inline at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
+    return at::_ops::thnn_conv2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) {
+    return at::_ops::thnn_conv2d::call(self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding));
+  }
+}
+
+// aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor
+inline at::Tensor thnn_conv2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
+    return at::_ops::thnn_conv2d::call(self, weight, kernel_size, bias, stride, padding);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) {
+    return at::_ops::thnn_conv2d::call(self, weight, kernel_size, bias, stride, padding);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff20733445d05424ae492bab0c23ee84bcb095f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0);
+TORCH_API at::Tensor thnn_conv2d_symint(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0));
+TORCH_API at::Tensor & thnn_conv2d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0);
+TORCH_API at::Tensor & thnn_conv2d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out);
+TORCH_API at::Tensor & thnn_conv2d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0));
+TORCH_API at::Tensor & thnn_conv2d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c0bf64141e5cfdf016835cf77e20216b55bf30ad
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor thnn_conv2d(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0);
+TORCH_API at::Tensor & thnn_conv2d_out(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b769b6843e5217d5375c988113d2b1fb3fb90b1b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/thnn_conv2d_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API thnn_conv2d_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, c10::SymIntArrayRef, const ::std::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::thnn_conv2d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out);
+};
+
+struct TORCH_API thnn_conv2d {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::SymIntArrayRef, const ::std::optional<at::Tensor> &, c10::SymIntArrayRef, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::thnn_conv2d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const ::std::optional<at::Tensor> & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold.h
new file mode 100644
index 0000000000000000000000000000000000000000..e1a8e367c7d9ea1e9e76e822c731dc584bda3d5a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/threshold_ops.h>
+
+namespace at {
+
+
+// aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor
+inline at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
+    return at::_ops::threshold::call(self, threshold, value);
+}
+
+// aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)
+inline at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
+    return at::_ops::threshold_::call(self, threshold, value);
+}
+
+// aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) {
+    return at::_ops::threshold_out::call(self, threshold, value, out);
+}
+// aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) {
+    return at::_ops::threshold_out::call(self, threshold, value, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..866ee5a122e71937482d2244afbfe61532efa278
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/threshold_backward_ops.h>
+
+namespace at {
+
+
+// aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) {
+    return at::_ops::threshold_backward_grad_input::call(grad_output, self, threshold, grad_input);
+}
+// aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input) {
+    return at::_ops::threshold_backward_grad_input::call(grad_output, self, threshold, grad_input);
+}
+
+// aten::threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor
+inline at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) {
+    return at::_ops::threshold_backward::call(grad_output, self, threshold);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad9b24df15c7d6cba076fb028d130ec5c4e1c6f3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f21ec7f55aa70ca94e6c8c20ed185e905a06de24
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..73b0386d74677a2ac9e33e880a00de6fdd8d786d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ab079e83e095246db861715d499cd06d822df9eb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_threshold_backward : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0c7c028abef7cfa46f034f769207f833e4f48562
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor threshold_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..6430b8023e05eae06ddd1aa38c95c3ee715d87c1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/threshold_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_threshold_backward_out : public at::meta::structured_threshold_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, const at::Tensor & grad_input);
+};
+TORCH_API at::Tensor threshold_backwards_nested(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor threshold_backward_sparse(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_sparse_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+TORCH_API at::Tensor threshold_backward_sparse_compressed(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+TORCH_API at::Tensor & threshold_backward_sparse_compressed_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+TORCH_API at::Tensor mkldnn_relu_backward(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c7fb8419ba197b71ef5c4968bd3416a108abd2e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API threshold_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::threshold_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input);
+};
+
+struct TORCH_API threshold_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::threshold_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8c9deb6649bbb129b2c7f0f99a8398f0dd07ff09
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..72e37ccd2b5ca05e96d857585f13b3789e4be55f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out);
+TORCH_API at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8445a7e500683a99d64d60e761dd0d963b18e43f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out);
+TORCH_API at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..17f263cf7853d94049a661e8d29741e5e8ecc073
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_threshold : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b3e1f7f8d37888d037869f3bcf8ee62ae9310ff4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor threshold(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+TORCH_API at::Tensor & threshold_outf(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out);
+TORCH_API at::Tensor & threshold_(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..af97f5bfa3af722ab3fb1b36133401092aaf16b3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/threshold_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_threshold_out : public at::meta::structured_threshold {
+void impl(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, const at::Tensor & out);
+};
+TORCH_API at::Tensor threshold_quantized_cpu(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1fce230534fd09cb626a3421297b1e9c3e44007
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/threshold_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API threshold {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::threshold";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+};
+
+struct TORCH_API threshold_ {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::threshold_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value);
+};
+
+struct TORCH_API threshold_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::threshold";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tile.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tile.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ee6cc8f5d15c81ef7b0d643e59967ba74da1d1a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tile.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tile_ops.h>
+
+namespace at {
+
+
+// aten::tile(Tensor self, SymInt[] dims) -> Tensor
+inline at::Tensor tile(const at::Tensor & self, at::IntArrayRef dims) {
+    return at::_ops::tile::call(self, c10::fromIntArrayRefSlow(dims));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor tile(const at::Tensor & self, at::IntArrayRef dims) {
+    return at::_ops::tile::call(self, c10::fromIntArrayRefSlow(dims));
+  }
+}
+
+// aten::tile(Tensor self, SymInt[] dims) -> Tensor
+inline at::Tensor tile_symint(const at::Tensor & self, c10::SymIntArrayRef dims) {
+    return at::_ops::tile::call(self, dims);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor tile(const at::Tensor & self, c10::SymIntArrayRef dims) {
+    return at::_ops::tile::call(self, dims);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tile_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b8abd19c3ada7bd8d9488ff8095d249d98c423f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor tile(const at::Tensor & self, at::IntArrayRef dims);
+TORCH_API at::Tensor tile_symint(const at::Tensor & self, c10::SymIntArrayRef dims);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tile_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7dbfc94f11cd5e082202daf3584a15312b7d1a93
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor tile_symint(const at::Tensor & self, c10::SymIntArrayRef dims);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tile_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f16f59c8650080065edfd7b47f1def5484ac0923
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tile_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tile {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tile";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tile(Tensor self, SymInt[] dims) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef dims);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dims);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d02b52f1992faa1be9845a68ca1439d509c77b4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7eca3cc4533110f00e651f5263c797caac4bbf22
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,27 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to(const at::Tensor & self, at::TensorOptions options={}, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor to(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, at::ScalarType dtype, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, const at::Tensor & other, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense.h
new file mode 100644
index 0000000000000000000000000000000000000000..679356784436e1cea2a80bedb0c6ebddb6ef9f38
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_dense_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..0e423e6ab65ec60ca9b63846812e1a5011c3be76
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_dense_backward_ops.h>
+
+namespace at {
+
+
+// aten::to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor
+inline at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad=::std::nullopt) {
+    return at::_ops::to_dense_backward::call(grad, input, masked_grad);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..42448f7a26d87b603faae30cc23a54da014f21e0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a6527d51fc80eefd9c3a5829874ae15bd478a7de
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..5d306c3a4ed7ea5b4dcc699786710bf9f2bb3a3f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_backward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_dense_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_dense_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, ::std::optional<bool> masked_grad);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7df4f394d49c76892999d84785404490f3a1e907
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_dense(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt, ::std::optional<bool> masked_grad=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5e511949ae2167381fe7c4b279180f879174b494
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_dense(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt, ::std::optional<bool> masked_grad=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..df3b9cc316430cb7fe9b2dd458db192e8a2fb1f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_dense_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_dense {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_dense";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_dense(Tensor self, ScalarType? dtype=None, *, bool? masked_grad=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<bool> masked_grad);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn.h
new file mode 100644
index 0000000000000000000000000000000000000000..d27072602afe5585d11308f5af1539429335a6cf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn.h
@@ -0,0 +1,34 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_mkldnn_ops.h>
+
+namespace at {
+
+
+// aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_mkldnn_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt) {
+    return at::_ops::to_mkldnn_out::call(self, dtype, out);
+}
+// aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_mkldnn_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out) {
+    return at::_ops::to_mkldnn_out::call(self, dtype, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb734e9a2074ec9b8c56717d12afc84dee02aef9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_mkldnn_backward_ops.h>
+
+namespace at {
+
+
+// aten::to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor
+inline at::Tensor to_mkldnn_backward(const at::Tensor & grad, const at::Tensor & input) {
+    return at::_ops::to_mkldnn_backward::call(grad, input);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c8817c5667b9f0b77bcb9b3ee7e731947bfb9ea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_mkldnn_backward(const at::Tensor & grad, const at::Tensor & input);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4e7d221203e58040da48e652ed4a599b47abe912
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_mkldnn_backward(const at::Tensor & grad, const at::Tensor & input);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1a7290d5af58d57aa63d47a93811ae324a19ccc4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_backward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_mkldnn_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_mkldnn_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad, const at::Tensor & input);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0bc1ed76067d98651cac409fad1575824cfd8e7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & to_mkldnn_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+TORCH_API at::Tensor & to_mkldnn_outf(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0fbe8446e56d16acb7f3e34dd2213e601a6a6f33
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor to_mkldnn(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d2a595c5c8b4d6f232b0a459fb78f3bb597ad44
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & to_mkldnn_out(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+TORCH_API at::Tensor dense_to_mkldnn(const at::Tensor & self, ::std::optional<at::ScalarType> dtype=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d288767d9f30e1eab4baabc88f57e609e7766246
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_mkldnn_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_mkldnn {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_mkldnn";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype);
+};
+
+struct TORCH_API to_mkldnn_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::ScalarType>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_mkldnn";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..846407c1496d225daea07094888f9fda97ea3925
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to(const at::Tensor & self, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, at::ScalarType dtype, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor to(const at::Tensor & self, const at::Tensor & other, bool non_blocking=false, bool copy=false, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8022f194f6f1d49f523f5134ee61b5e2d8bdc12c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_dtype_layout {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, bool, bool, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to";
+  static constexpr const char* overload_name = "dtype_layout";
+  static constexpr const char* schema_str = "to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API to_device {
+  using schema = at::Tensor (const at::Tensor &, at::Device, at::ScalarType, bool, bool, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to";
+  static constexpr const char* overload_name = "device";
+  static constexpr const char* schema_str = "to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API to_dtype {
+  using schema = at::Tensor (const at::Tensor &, at::ScalarType, bool, bool, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to";
+  static constexpr const char* overload_name = "dtype";
+  static constexpr const char* schema_str = "to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API to_other {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, bool, bool, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to";
+  static constexpr const char* overload_name = "other";
+  static constexpr const char* schema_str = "to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..e918c18f51a01755d53c1130ac02897565d24a0e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_padded_tensor_ops.h>
+
+namespace at {
+
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor to_padded_tensor(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt);
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor to_padded_tensor(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor::call(self, padding, output_size);
+  }
+}
+
+// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
+  }
+}
+
+// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, out);
+  }
+}
+
+// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_padded_tensor_symint_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
+  }
+}
+
+// aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & to_padded_tensor_symint_outf(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) {
+    return at::_ops::to_padded_tensor_out::call(self, padding, output_size, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..33813782147e753fa4fb05702939d39220ee80bc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & to_padded_tensor_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt);
+TORCH_API at::Tensor & to_padded_tensor_outf(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out);
+TORCH_API at::Tensor & to_padded_tensor_symint_out(at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=::std::nullopt);
+TORCH_API at::Tensor & to_padded_tensor_symint_outf(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2cb21f7c4499ac46c37ceadd769a5337324c2cd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & to_padded_tensor_out_symint(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out);
+TORCH_API at::Tensor NestedTensor_to_padded_tensor_generic(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt);
+TORCH_API at::Tensor NestedTensor_to_padded_tensor_cuda(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..c573c4bca6930edf754b2fcc3f6fc54b587a796a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_padded_tensor_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_padded_tensor {
+  using schema = at::Tensor (const at::Tensor &, double, at::OptionalSymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_padded_tensor";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size);
+};
+
+struct TORCH_API to_padded_tensor_out {
+  using schema = at::Tensor & (const at::Tensor &, double, at::OptionalSymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_padded_tensor";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse.h
new file mode 100644
index 0000000000000000000000000000000000000000..08901e92de45ce8a9b0b90dbe96280123700ec9d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_sparse_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc.h
new file mode 100644
index 0000000000000000000000000000000000000000..b6dfc30a0d6eac1fe74f3ca7b2f513efff7c5c4f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_sparse_bsc_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd3bb6e65b2e298a0f63bdbe56eab239485cbcc7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..010ad08a4113a20b58986789f0b1a4017828c2b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_sparse_bsc(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b442b28cd44422cba57a2f28673153a660878e0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsc_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_sparse_bsc {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse_bsc";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr.h
new file mode 100644
index 0000000000000000000000000000000000000000..23ae503dec5451a648b1b90071f7640e3f6e6ed4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_sparse_bsr_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..854371b04f6edd84a2106bddbccfda4c86836315
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..113a55068fb5dfb1592dddec7319c027206244fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_sparse_bsr(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e0d18ccf5114a44b4fc852798f4aaaf3b6b2605c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_bsr_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_sparse_bsr {
+  using schema = at::Tensor (const at::Tensor &, at::IntArrayRef, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse_bsr";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..497d56ea431c89721a8af7046cc4e06045fbc82a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_sparse(const at::Tensor & self, int64_t sparse_dim);
+TORCH_API at::Tensor to_sparse(const at::Tensor & self, ::std::optional<at::Layout> layout=::std::nullopt, at::OptionalIntArrayRef blocksize=::std::nullopt, ::std::optional<int64_t> dense_dim=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba185f299ceb066ca3e34958f32cc8f6363d5e56
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_sparse_csc_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb276b6c73c907fcb1cacddeddb9a8f712eab17e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_sparse_csc(const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4d70f49333a0dbc3c0caadb0dbee43b8d2104e73
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_sparse_csc(const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc99723e37ce603fa0b812009c634315af7edeb3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csc_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_sparse_csc {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse_csc";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<int64_t> dense_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<int64_t> dense_dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr.h
new file mode 100644
index 0000000000000000000000000000000000000000..f946a169b2b4cf105df93d6d6ac5c7648c07a402
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/to_sparse_csr_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4be401666a1d610ea7b20ff0f5e4108c1ac28448
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor to_sparse_csr(const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8584e9beb8632230facb2b31ae9b1352014d9fb2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_sparse_csr(const at::Tensor & self, ::std::optional<int64_t> dense_dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e74c6a311349b1c72cca07580098f9e2394c312a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_csr_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_sparse_csr {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse_csr";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<int64_t> dense_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<int64_t> dense_dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae45fb4d72460b100e16be3a71c8a0de830bcd13
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_sparse(const at::Tensor & self, int64_t sparse_dim);
+TORCH_API at::Tensor to_sparse(const at::Tensor & self, ::std::optional<at::Layout> layout=::std::nullopt, at::OptionalIntArrayRef blocksize=::std::nullopt, ::std::optional<int64_t> dense_dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6cc1ca42cc9e5294db246c209e0f102e7b11c9ac
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/to_sparse_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_sparse_sparse_dim {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse";
+  static constexpr const char* overload_name = "sparse_dim";
+  static constexpr const char* schema_str = "to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t sparse_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sparse_dim);
+};
+
+struct TORCH_API to_sparse {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::Layout>, at::OptionalIntArrayRef, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::to_sparse";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::Layout> layout, at::OptionalIntArrayRef blocksize, ::std::optional<int64_t> dense_dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk.h
new file mode 100644
index 0000000000000000000000000000000000000000..f34fab4ed2b6e8b25c82cf57ae22c28d2ec67613
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/topk_ops.h>
+
+namespace at {
+
+
+// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
+inline ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+  }
+}
+
+// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
+inline ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+  }
+}
+
+// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
+inline ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+  }
+}
+
+// aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)
+inline ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) {
+    return at::_ops::topk_values::call(self, k, dim, largest, sorted, values, indices);
+  }
+}
+
+// aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
+inline ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk::call(self, k, dim, largest, sorted);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk::call(self, k, dim, largest, sorted);
+  }
+}
+
+// aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
+inline ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk::call(self, k, dim, largest, sorted);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) {
+    return at::_ops::topk::call(self, k, dim, largest, sorted);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..fe9167f80f33edb9dc1d897e0a2053a6eee6deee
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce1ef1ebe2d41f868691fb044a5514cee9baf660
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b4c4f5d5e18220c58c72d8c5b8947d0a9022c8d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..17ab5ff1c6f160e386194924927a215b89a15e23
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_topk : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..484d95c32872e5d10ead57b5aea9ed548c53c272
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk_symint(const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_outf(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> topk_symint_outf(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f808898ae57d9f374dcd7820c0eddea3b2cb4f8d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/topk_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_topk_out_cpu : public at::meta::structured_topk {
+void impl(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, const at::Tensor & values, const at::Tensor & indices);
+};
+struct TORCH_API structured_topk_out_cuda : public at::meta::structured_topk {
+void impl(const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, const at::Tensor & values, const at::Tensor & indices);
+};
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> topk_quantized_cpu(const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/topk_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ad3086257b2f1e663b407ca5e9551fb5fbf2933
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/topk_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API topk_values {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &> (const at::Tensor &, c10::SymInt, int64_t, bool, bool, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::topk";
+  static constexpr const char* overload_name = "values";
+  static constexpr const char* schema_str = "topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)";
+  static ::std::tuple<at::Tensor &,at::Tensor &> call(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+  static ::std::tuple<at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices);
+};
+
+struct TORCH_API topk {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, c10::SymInt, int64_t, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::topk";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace.h
new file mode 100644
index 0000000000000000000000000000000000000000..07ba52f86ead41ea07032dc0968b1cf649e30f36
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/trace_ops.h>
+
+namespace at {
+
+
+// aten::trace(Tensor self) -> Tensor
+inline at::Tensor trace(const at::Tensor & self) {
+    return at::_ops::trace::call(self);
+}
+
+// aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & trace_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::trace_out::call(self, out);
+}
+// aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & trace_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::trace_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..5bb440ba8256047abd82c568b4ceae37b5204053
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/trace_backward_ops.h>
+
+namespace at {
+
+
+// aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor
+inline at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes) {
+    return at::_ops::trace_backward::call(grad, c10::fromIntArrayRefSlow(sizes));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes) {
+    return at::_ops::trace_backward::call(grad, c10::fromIntArrayRefSlow(sizes));
+  }
+}
+
+// aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor
+inline at::Tensor trace_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef sizes) {
+    return at::_ops::trace_backward::call(grad, sizes);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor trace_backward(const at::Tensor & grad, c10::SymIntArrayRef sizes) {
+    return at::_ops::trace_backward::call(grad, sizes);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5e95242aa2af6716eb71736e1bc4211e34276502
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor trace_backward(const at::Tensor & grad, at::IntArrayRef sizes);
+TORCH_API at::Tensor trace_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef sizes);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb8066ac50c9c6ee8d2282caf1c8502259e1a29a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor trace_backward_symint(const at::Tensor & grad, c10::SymIntArrayRef sizes);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..441ef7596aa17e7e99ac6f7696c88aca61884ff7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_backward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trace_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trace_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "trace_backward(Tensor grad, SymInt[] sizes) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad, c10::SymIntArrayRef sizes);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, c10::SymIntArrayRef sizes);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..108adcdabf633c78fa8191faadf2edea6a4fd1b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & trace_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & trace_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1d87e5101da56c8087d383f7ba76981d8c2284f6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor trace(const at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ca8fd616d3310fc59bb75cb6e39a14252390bf4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor trace(const at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2c1ff07047bc553741cbc90511ba7a9f518aa24a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & trace_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor trace_cpu(const at::Tensor & self);
+TORCH_API at::Tensor trace_cuda(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trace_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8bf1881b7732ce8b991d71d615bc8aece997ff6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trace_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trace {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trace";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "trace(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API trace_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trace";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose.h
new file mode 100644
index 0000000000000000000000000000000000000000..550d075e668965c9e3a45e2e071edc7a37b173ed
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/transpose_ops.h>
+
+namespace at {
+
+
+// aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)
+inline at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1) {
+    return at::_ops::transpose_int::call(self, dim0, dim1);
+}
+
+// aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)
+inline at::Tensor transpose(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) {
+    return at::_ops::transpose_Dimname::call(self, dim0, dim1);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc30daf79f5429d8e8f7bcaa1bb562b08d7f484d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor & transpose_(at::Tensor & self, int64_t dim0, int64_t dim1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..30fe07181bba8cd6093401257cb5664f86e9ca6d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor transpose(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..777ff5ef356952c9591f752aa5abdbdcf80d34d4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/transpose_copy_ops.h>
+
+namespace at {
+
+
+// aten::transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor
+inline at::Tensor transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1) {
+    return at::_ops::transpose_copy_int::call(self, dim0, dim1);
+}
+
+// aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & transpose_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1) {
+    return at::_ops::transpose_copy_int_out::call(self, dim0, dim1, out);
+}
+// aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & transpose_copy_outf(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) {
+    return at::_ops::transpose_copy_int_out::call(self, dim0, dim1, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..973e68ce6c1f7b0de1d60b7ee7aa7aba025e215f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & transpose_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor & transpose_copy_outf(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f6b25fb8f14bba9b852a8d277939ae225a89085d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor transpose_copy(const at::Tensor & self, int64_t dim0, int64_t dim1);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9601aed21044fda2adacc5d5b0b53436e690ee8c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & transpose_copy_int_out(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out);
+TORCH_API at::Tensor transpose_copy_int(const at::Tensor & self, int64_t dim0, int64_t dim1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f398444d40ad8f0039741cf9d7f4e3605c879bbe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API transpose_copy_int {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::transpose_copy";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+struct TORCH_API transpose_copy_int_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::transpose_copy";
+  static constexpr const char* overload_name = "int_out";
+  static constexpr const char* schema_str = "transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e72f7a8b1ec1cc415a40ae4e276b60140b332325
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor transpose(const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor transpose_nested(const at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor & transpose_(at::Tensor & self, int64_t dim0, int64_t dim1);
+TORCH_API at::Tensor transpose(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..312950f3b5abf6584b549a663e565eb84bf2bf4f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/transpose_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API transpose_int {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::transpose";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+struct TORCH_API transpose_Dimname {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::transpose";
+  static constexpr const char* overload_name = "Dimname";
+  static constexpr const char* schema_str = "transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim0, at::Dimname dim1);
+};
+
+struct TORCH_API transpose_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::transpose_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t dim0, int64_t dim1);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid.h
new file mode 100644
index 0000000000000000000000000000000000000000..d397589ded6dd2d59c60aba62335d0a849e63a58
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/trapezoid_ops.h>
+
+namespace at {
+
+
+// aten::trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
+inline at::Tensor trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
+    return at::_ops::trapezoid_x::call(y, x, dim);
+}
+
+// aten::trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor
+inline at::Tensor trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) {
+    return at::_ops::trapezoid_dx::call(y, dx, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c2f7f8765e3b6e9c56a306dc9f9e6c57fea6f874
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1);
+TORCH_API at::Tensor trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..741b94b3cdd90d6a2026ca513137bf8bc11b532e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor trapezoid(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1);
+TORCH_API at::Tensor trapezoid(const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5f5a562b677f4aaa7197e4e5ff0ec754fb473ea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapezoid_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trapezoid_x {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trapezoid";
+  static constexpr const char* overload_name = "x";
+  static constexpr const char* schema_str = "trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor";
+  static at::Tensor call(const at::Tensor & y, const at::Tensor & x, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim);
+};
+
+struct TORCH_API trapezoid_dx {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trapezoid";
+  static constexpr const char* overload_name = "dx";
+  static constexpr const char* schema_str = "trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor";
+  static at::Tensor call(const at::Tensor & y, const at::Scalar & dx, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Scalar & dx, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapz.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz.h
new file mode 100644
index 0000000000000000000000000000000000000000..7546724b412b034ec4b4d35ddd9116e2f865878b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/trapz_ops.h>
+
+namespace at {
+
+
+// aten::trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor
+inline at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) {
+    return at::_ops::trapz_x::call(y, x, dim);
+}
+
+// aten::trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor
+inline at::Tensor trapz(const at::Tensor & y, double dx=1, int64_t dim=-1) {
+    return at::_ops::trapz_dx::call(y, dx, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dfe5056e4d41771e59fa82ad47cdd68456127f1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1);
+TORCH_API at::Tensor trapz(const at::Tensor & y, double dx=1, int64_t dim=-1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9dc40b45cecc91a6bef3bc064a6c6ed7f482829d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor trapz(const at::Tensor & y, const at::Tensor & x, int64_t dim=-1);
+TORCH_API at::Tensor trapz(const at::Tensor & y, double dx=1, int64_t dim=-1);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..64ba658f8d79955836043c18c99fc927f9d24426
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trapz_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trapz_x {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trapz";
+  static constexpr const char* overload_name = "x";
+  static constexpr const char* schema_str = "trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor";
+  static at::Tensor call(const at::Tensor & y, const at::Tensor & x, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim);
+};
+
+struct TORCH_API trapz_dx {
+  using schema = at::Tensor (const at::Tensor &, double, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trapz";
+  static constexpr const char* overload_name = "dx";
+  static constexpr const char* schema_str = "trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor";
+  static at::Tensor call(const at::Tensor & y, double dx, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, double dx, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve.h
new file mode 100644
index 0000000000000000000000000000000000000000..edd605d1f1bdc336b73bd42bac84b14fb6446816
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/triangular_solve_ops.h>
+
+namespace at {
+
+
+// aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)
+inline ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) {
+    return at::_ops::triangular_solve_X::call(self, A, upper, transpose, unitriangular, X, M);
+}
+// aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)
+inline ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) {
+    return at::_ops::triangular_solve_X::call(self, A, upper, transpose, unitriangular, X, M);
+}
+
+// aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)
+inline ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) {
+    return at::_ops::triangular_solve::call(self, A, upper, transpose, unitriangular);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e2669da969287c79c9ecca69399b692a3d2ca1b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ea6b3f2c2acb5adf3e3b72e192265ff8d11ccd1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf2f57766b0c156aae72d26d376d7d22733ddba8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..f37d60a8886ec32e503da18afb0626dcbf9e2339
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_triangular_solve : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..615866269f1ff66aae727d7e4c392bf1d2c18a05
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> triangular_solve(const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out(at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_outf(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..df0ede9e827d73c1319f98c5f3bc88c3bf4a919d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/triangular_solve_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_triangular_solve_out : public at::meta::structured_triangular_solve {
+void impl(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, const at::Tensor & X, const at::Tensor & M);
+};
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out_sparse_csr_cpu(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> triangular_solve_out_sparse_csr_cuda(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..12d0c544b43d1434968c5c222110be8d87063a2e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triangular_solve_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API triangular_solve_X {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &> (const at::Tensor &, const at::Tensor &, bool, bool, bool, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triangular_solve";
+  static constexpr const char* overload_name = "X";
+  static constexpr const char* schema_str = "triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)";
+  static ::std::tuple<at::Tensor &,at::Tensor &> call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+  static ::std::tuple<at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M);
+};
+
+struct TORCH_API triangular_solve {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, const at::Tensor &, bool, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triangular_solve";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril.h
new file mode 100644
index 0000000000000000000000000000000000000000..6d63fcb9febacd7c05f4a56c6b10e4df8ea4feec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tril_ops.h>
+
+namespace at {
+
+
+// aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) {
+    return at::_ops::tril_out::call(self, diagonal, out);
+}
+// aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out) {
+    return at::_ops::tril_out::call(self, diagonal, out);
+}
+
+// aten::tril(Tensor self, int diagonal=0) -> Tensor
+inline at::Tensor tril(const at::Tensor & self, int64_t diagonal=0) {
+    return at::_ops::tril::call(self, diagonal);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3dee6f46b9ab4f6ebaa7afcc0d22a98cd66d42d4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor tril(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1b9e1fe036a0b8c9e09aa1050bf4c192cb9c7742
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor tril(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & tril_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0fbfeb0c4047681ade10cf61c1408749b6fdece2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor tril(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & tril_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices.h
new file mode 100644
index 0000000000000000000000000000000000000000..679abf2f9cd1a31e45bf27c3cd5382ebe47408d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices.h
@@ -0,0 +1,43 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/tril_indices_ops.h>
+
+namespace at {
+
+
+// aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) {
+    return at::_ops::tril_indices::call(row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::tril_indices::call(row, col, offset, dtype, layout, device, pin_memory);
+}
+
+// aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tril_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) {
+    return at::_ops::tril_indices_out::call(row, col, offset, out);
+}
+// aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & tril_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out) {
+    return at::_ops::tril_indices_out::call(row, col, offset, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..72827b1e7a2cc9c1c4d695035fb622f310f29cdf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & tril_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0);
+TORCH_API at::Tensor & tril_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e000b65aa077989b915be4c733b3c34b82f3691e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..22378108e53f1463b36bbc19037c9a63e23a396e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor tril_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a46e9bb9856e20879034a78364f466718d45b8d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & tril_indices_out(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+TORCH_API at::Tensor tril_indices_cpu(int64_t row, int64_t col, int64_t offset=0, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor tril_indices_cuda(int64_t row, int64_t col, int64_t offset=0, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..27da6f6d9ace1178f75d7c3f24337d0f3ae72be0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_indices_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tril_indices {
+  using schema = at::Tensor (int64_t, int64_t, int64_t, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tril_indices";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API tril_indices_out {
+  using schema = at::Tensor & (int64_t, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tril_indices";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..714abca1ee0fb90bb08dd07a2442bf42639e9f30
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_tril : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, int64_t diagonal);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..73546783d411935e2414458e84da3d473d05c509
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor tril(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & tril_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & tril_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a1dea6f0207fbe2ed9ddf9490f6c2d6d0b1ae616
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/tril_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_tril_cpu : public at::meta::structured_tril {
+void impl(const at::Tensor & self, int64_t diagonal, const at::Tensor & out);
+};
+struct TORCH_API structured_tril_cuda : public at::meta::structured_tril {
+void impl(const at::Tensor & self, int64_t diagonal, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/tril_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..c16340c9c5eaeb9e942d1e0a3c4543270299f3c8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/tril_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API tril_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tril_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tril_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t diagonal);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal);
+};
+
+struct TORCH_API tril_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tril";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+};
+
+struct TORCH_API tril {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::tril";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "tril(Tensor self, int diagonal=0) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t diagonal);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss.h
new file mode 100644
index 0000000000000000000000000000000000000000..3f0e42367a2f4cdf4d49cce535470a897102cff0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/triplet_margin_loss_ops.h>
+
+namespace at {
+
+
+// aten::triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor
+inline at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean) {
+    return at::_ops::triplet_margin_loss::call(anchor, positive, negative, margin, p, eps, swap, reduction);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e55bb6fc7c2369c4bd2fd380be128d99dc61be2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..30854f311cf7b5301a0a7caf128497707e230b77
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triplet_margin_loss_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor triplet_margin_loss(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dac4e757fbccaf75d7094e5e88e8faefffa0b689
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor triu(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..51606a56d17333d1fdb437f094b8a46119bca58b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor triu(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & triu_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3a1d23e298a1571c122ee3400c39e54d17f55167
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor triu(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & triu_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices.h
new file mode 100644
index 0000000000000000000000000000000000000000..4d7c38292a32af26eede4908b7979104b8e6bea9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices.h
@@ -0,0 +1,43 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/triu_indices_ops.h>
+
+namespace at {
+
+
+// aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) {
+    return at::_ops::triu_indices::call(row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::triu_indices::call(row, col, offset, dtype, layout, device, pin_memory);
+}
+
+// aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & triu_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) {
+    return at::_ops::triu_indices_out::call(row, col, offset, out);
+}
+// aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & triu_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out) {
+    return at::_ops::triu_indices_out::call(row, col, offset, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f8b922fd1a2d4b97b8322328461c2b5dd528dcc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & triu_indices_out(at::Tensor & out, int64_t row, int64_t col, int64_t offset=0);
+TORCH_API at::Tensor & triu_indices_outf(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..aedc8efc7e51b841a7ed0630efd72e353da1e0e4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6841e751ead8d06f66cd8d282bd1185d08524126
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong);
+TORCH_API at::Tensor triu_indices(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5086cc91f95039dfda0ea4f8a02db62176a33fee
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & triu_indices_out(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+TORCH_API at::Tensor triu_indices_cpu(int64_t row, int64_t col, int64_t offset=0, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor triu_indices_cuda(int64_t row, int64_t col, int64_t offset=0, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e0ab2480c20f92cca01b0e84b3aa44b23a25a51
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_indices_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API triu_indices {
+  using schema = at::Tensor (int64_t, int64_t, int64_t, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triu_indices";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API triu_indices_out {
+  using schema = at::Tensor & (int64_t, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triu_indices";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..c0195c7af485a1226c4dc05e5408e066a7aae49b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_triu : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, int64_t diagonal);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..287d5d336a5a40a60bb1cf54a0174cd2c3296b39
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor triu(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_out(at::Tensor & out, const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_outf(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+TORCH_API at::Tensor & triu_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..630d3b0de6ba95926b77a7037d2509424afe2084
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/triu_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_triu_cpu : public at::meta::structured_triu {
+void impl(const at::Tensor & self, int64_t diagonal, const at::Tensor & out);
+};
+struct TORCH_API structured_triu_cuda : public at::meta::structured_triu {
+void impl(const at::Tensor & self, int64_t diagonal, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/triu_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..309907e8063cd5a5f3fba686f1b1350c2123efb0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/triu_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API triu_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triu_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t diagonal);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal);
+};
+
+struct TORCH_API triu_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triu";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out);
+};
+
+struct TORCH_API triu {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::triu";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "triu(Tensor self, int diagonal=0) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t diagonal);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide.h b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide.h
new file mode 100644
index 0000000000000000000000000000000000000000..88967493dc2b0bb22de83b30d380ae96211600f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/true_divide_ops.h>
+
+namespace at {
+
+
+// aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::true_divide_Tensor::call(self, other);
+}
+
+// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & true_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::true_divide_out::call(self, other, out);
+}
+// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & true_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::true_divide_out::call(self, other, out);
+}
+
+// aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::true_divide_Scalar::call(self, other);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3672187aeb576cf669fa4e8ceecd45a5dbec356f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & true_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & true_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & true_divide_(at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & true_divide_(at::Tensor & self, const at::Scalar & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c12e36d69f97bf66028207ec67e9b77fd4074f57
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & true_divide_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & true_divide_(at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & true_divide_(at::Tensor & self, const at::Scalar & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..44cd9b10068a1580e229e4ca5817d4545d951418
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/true_divide_ops.h
@@ -0,0 +1,72 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API true_divide_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::true_divide";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "true_divide.Tensor(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API true_divide__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::true_divide_";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API true_divide_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::true_divide";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API true_divide_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::true_divide";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "true_divide.Scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API true_divide__Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::true_divide_";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc.h
new file mode 100644
index 0000000000000000000000000000000000000000..d26e444dd45baa12bea7e109589b61f19addd36d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/trunc_ops.h>
+
+namespace at {
+
+
+// aten::trunc(Tensor self) -> Tensor
+inline at::Tensor trunc(const at::Tensor & self) {
+    return at::_ops::trunc::call(self);
+}
+
+// aten::trunc_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & trunc_(at::Tensor & self) {
+    return at::_ops::trunc_::call(self);
+}
+
+// aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::trunc_out::call(self, out);
+}
+// aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::trunc_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..88d74afe665d89c2cf18bc0207ac89ce202f6892
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor trunc(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_(at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a165c17de22fdf03517f50674497d7f52dcbd1f5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor trunc(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..81f92307c4aead7aa1f466dd68962c87a2383794
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor trunc(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1e2726a564464e636dc395a2576ba62c848550a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_trunc : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c36aad0d9191c31e729785b461ceb6e2293b7774
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor trunc(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & trunc_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b6714af114464354e693cf2715e10941f8ed5bd6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/trunc_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_trunc_out : public at::meta::structured_trunc {
+void impl(const at::Tensor & self, const at::Tensor & out);
+};
+TORCH_API at::Tensor trunc_sparse(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_sparse_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_sparse_(at::Tensor & self);
+TORCH_API at::Tensor trunc_sparse_csr(const at::Tensor & self);
+TORCH_API at::Tensor & trunc_sparse_csr_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & trunc_sparse_csr_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..98d6565fec5dc546da71878a74d1e8c7b8966355
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/trunc_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API trunc {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trunc";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "trunc(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API trunc_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trunc_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "trunc_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API trunc_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::trunc";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/type_as.h b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as.h
new file mode 100644
index 0000000000000000000000000000000000000000..e7059f6ad498fd96fbf5b1e1e13eae0667a283d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/type_as_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c3f58b82ca0ad2b82bb4589277ab1d4711729c7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor type_as(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3bd6e42629e633015935c62aa33e3fffdd6a457b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor type_as(const at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8963442a17b8f434bbfda91b2daa01c87ef57175
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/type_as_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API type_as {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::type_as";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "type_as(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind.h
new file mode 100644
index 0000000000000000000000000000000000000000..1efbabb692837aaf170e392c7ad9ec9a86710c04
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unbind_ops.h>
+
+namespace at {
+
+
+// aten::unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim=0) {
+    return at::_ops::unbind_int::call(self, dim);
+}
+
+// aten::unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> unbind(const at::Tensor & self, at::Dimname dim) {
+    return at::_ops::unbind_Dimname::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..771af8c02bbbf3a0da35a7f8128c1630af6c12e0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim=0);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a43b0dc24081fedc1f04edac2e4b7b521fd40f34
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unbind(const at::Tensor & self, at::Dimname dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..c898ea3b60ab5b7d7530185190e6cafd06bca053
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unbind_copy_ops.h>
+
+namespace at {
+
+
+// aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> unbind_copy(const at::Tensor & self, int64_t dim=0) {
+    return at::_ops::unbind_copy_int::call(self, dim);
+}
+
+// aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unbind_copy_out(at::TensorList out, const at::Tensor & self, int64_t dim=0) {
+    return at::_ops::unbind_copy_int_out::call(self, dim, out);
+}
+// aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unbind_copy_outf(const at::Tensor & self, int64_t dim, at::TensorList out) {
+    return at::_ops::unbind_copy_int_out::call(self, dim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..66be9d3fabf684b97acf382b385ba54d205dec8e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API void unbind_copy_out(at::TensorList out, const at::Tensor & self, int64_t dim=0);
+TORCH_API void unbind_copy_outf(const at::Tensor & self, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..51983d8d2a7c11aabef3b42cfc868dd86797bd62
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::vector<at::Tensor> unbind_copy(const at::Tensor & self, int64_t dim=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c0668551664fab77506a4e89fcf45a8a55fecd6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API void unbind_copy_int_out(const at::Tensor & self, int64_t dim, at::TensorList out);
+TORCH_API ::std::vector<at::Tensor> unbind_copy_int(const at::Tensor & self, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..66b4d71aad935576f9965abd138a11e27de07f29
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unbind_copy_int {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unbind_copy";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "unbind_copy.int(Tensor self, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API unbind_copy_int_out {
+  using schema = void (const at::Tensor &, int64_t, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unbind_copy";
+  static constexpr const char* overload_name = "int_out";
+  static constexpr const char* schema_str = "unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> ()";
+  static void call(const at::Tensor & self, int64_t dim, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..0ede026e9553cd66daa88b1d6fc57d0f8375f250
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> unbind(const at::Tensor & self, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> NestedTensor_unbind(const at::Tensor & self, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> unbind(const at::Tensor & self, at::Dimname dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..c5ce341fc81c5cdca9b837f6fb04ce5b204e30c0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unbind_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unbind_int {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unbind";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API unbind_Dimname {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unbind";
+  static constexpr const char* overload_name = "Dimname";
+  static constexpr const char* schema_str = "unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, at::Dimname dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten.h
new file mode 100644
index 0000000000000000000000000000000000000000..f60d9a30cab45be5d154565835676813531dbc61
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten.h
@@ -0,0 +1,69 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unflatten_ops.h>
+
+namespace at {
+
+
+// aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)
+inline at::Tensor unflatten(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes) {
+    return at::_ops::unflatten_int::call(self, dim, c10::fromIntArrayRefSlow(sizes));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor unflatten(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes) {
+    return at::_ops::unflatten_int::call(self, dim, c10::fromIntArrayRefSlow(sizes));
+  }
+}
+
+// aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)
+inline at::Tensor unflatten_symint(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) {
+    return at::_ops::unflatten_int::call(self, dim, sizes);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor unflatten(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) {
+    return at::_ops::unflatten_int::call(self, dim, sizes);
+  }
+}
+
+// aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a)
+inline at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names) {
+    return at::_ops::unflatten_Dimname::call(self, dim, c10::fromIntArrayRefSlow(sizes), names);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names) {
+    return at::_ops::unflatten_Dimname::call(self, dim, c10::fromIntArrayRefSlow(sizes), names);
+  }
+}
+
+// aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a)
+inline at::Tensor unflatten_symint(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) {
+    return at::_ops::unflatten_Dimname::call(self, dim, sizes, names);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) {
+    return at::_ops::unflatten_Dimname::call(self, dim, sizes, names);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..983504911a84558096fc5ecb3562aa910cb7b6f3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor unflatten(const at::Tensor & self, int64_t dim, at::IntArrayRef sizes);
+TORCH_API at::Tensor unflatten_symint(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes);
+TORCH_API at::Tensor unflatten(const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names);
+TORCH_API at::Tensor unflatten_symint(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors.h
new file mode 100644
index 0000000000000000000000000000000000000000..844c6f546f50d8cddcf4701e49d6b01141bed04b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unflatten_dense_tensors_ops.h>
+
+namespace at {
+
+
+// aten::unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[]
+inline ::std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor & flat, at::TensorList tensors) {
+    return at::_ops::unflatten_dense_tensors::call(flat, tensors);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e4f17fae2975f8b39749708e7ec0fdb386a5b1d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor & flat, at::TensorList tensors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..286c395c698d4d559c6316eb2fef957093304e7d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> unflatten_dense_tensors(const at::Tensor & flat, at::TensorList tensors);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..32ee48bc287b3f6e07f38e334655118179b8c995
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_dense_tensors_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unflatten_dense_tensors {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unflatten_dense_tensors";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & flat, at::TensorList tensors);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & flat, at::TensorList tensors);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bcb61bf9222dbb724b12d8263408f3fcb1adad6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor unflatten_symint(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes);
+TORCH_API at::Tensor unflatten_dimname_symint(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..140d6eb98ed1962f39528fdceff44475f062a6df
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unflatten_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unflatten_int {
+  using schema = at::Tensor (const at::Tensor &, int64_t, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unflatten";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes);
+};
+
+struct TORCH_API unflatten_Dimname {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname, c10::SymIntArrayRef, at::DimnameList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unflatten";
+  static constexpr const char* overload_name = "Dimname";
+  static constexpr const char* schema_str = "unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold.h
new file mode 100644
index 0000000000000000000000000000000000000000..f139e8d0ef08640c1ab2e3c9a475b7c3c2731eec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unfold_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..4863f41d12c1f9be6004a0527fdcddf5fddae776
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unfold_backward_ops.h>
+
+namespace at {
+
+
+// aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
+inline at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step);
+  }
+}
+
+// aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor
+inline at::Tensor unfold_backward_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward::call(grad_in, input_sizes, dim, size, step);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor unfold_backward(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward::call(grad_in, input_sizes, dim, size, step);
+  }
+}
+
+// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
+  }
+}
+
+// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
+    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
+    return at::_ops::unfold_backward_out::call(grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out);
+  }
+}
+
+// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_backward_symint_out(at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) {
+    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
+  }
+}
+
+// aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_backward_symint_outf(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
+    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) {
+    return at::_ops::unfold_backward_out::call(grad_in, input_sizes, dim, size, step, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae4c086404ab3421c8655897022b565b2b8781a0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & unfold_backward_out(at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+TORCH_API at::Tensor & unfold_backward_outf(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out);
+TORCH_API at::Tensor & unfold_backward_symint_out(at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+TORCH_API at::Tensor & unfold_backward_symint_outf(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7df0e250ab0bf736eaf102a677f00bb04a10ba15
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+TORCH_API at::Tensor unfold_backward_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cc60e3df1507abb4cbd9e14fec6e484540eb44e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+TORCH_API at::Tensor unfold_backward_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c0f54ba6f4458e7d5171d53431f2da54147d70f6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & unfold_backward_out_symint(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out);
+TORCH_API at::Tensor unfold_backward(const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d23fd54e44f2aa28d99878e7dd94fe523373ecec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unfold_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, int64_t, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unfold_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step);
+};
+
+struct TORCH_API unfold_backward_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, int64_t, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unfold_backward";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..60d35df2a54ad64f5cc3cd87b3c2fbe6b33171db
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unfold_copy_ops.h>
+
+namespace at {
+
+
+// aten::unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor
+inline at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) {
+    return at::_ops::unfold_copy::call(self, dimension, size, step);
+}
+
+// aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) {
+    return at::_ops::unfold_copy_out::call(self, dimension, size, step, out);
+}
+// aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unfold_copy_outf(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out) {
+    return at::_ops::unfold_copy_out::call(self, dimension, size, step, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d2b2d72d1002bb2d6cbc98e11f3d9e0c87eb29c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & unfold_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+TORCH_API at::Tensor & unfold_copy_outf(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..83b8cc4e62b2f1f40aa4004d123027ae1222bdfc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c2c88cc905fb12a37e99be0c1fbb05fcdfa662e5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & unfold_copy_out(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out);
+TORCH_API at::Tensor unfold_copy(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..484b628d0452492619f4113337cf2ca3050c744c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unfold_copy {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unfold_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+};
+
+struct TORCH_API unfold_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unfold_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b6940f845cd2ba9d0300001907e23ab542bf56d3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..79ef868b9e7808ad0c0a83e9dc210cdf1d39c8ed
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..568495b7acbb85a6c03bdacc70da5259f14368ef
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_meta_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c442664bdeadb62319c74c64667bbfa03fb37e1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor unfold(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf5d80ef8e3eacbba121d94b15ec7381f0d0f5da
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unfold_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unfold {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unfold";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform.h
new file mode 100644
index 0000000000000000000000000000000000000000..2e4d020e693d8462a3a923f8ff74992b8c3efa2a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/uniform_ops.h>
+
+namespace at {
+
+
+// aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & uniform_out(at::Tensor & out, const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::uniform_out::call(self, from, to, generator, out);
+}
+// aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & uniform_outf(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out) {
+    return at::_ops::uniform_out::call(self, from, to, generator, out);
+}
+
+// aten::uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor
+inline at::Tensor uniform(const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt) {
+    return at::_ops::uniform::call(self, from, to, generator);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d320c6372d59bb94bf150f396d6636172a26ea6d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor uniform(const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & uniform_out(at::Tensor & out, const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & uniform_outf(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e17a1e82433565be67befe802a9d2b24d073a821
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & uniform_(at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b003b202de45f0a2e30782cd1394df41b18bfb2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & uniform_(at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..45d14193bf0691b0560665a18ef806eef408d909
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_meta_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & uniform_(at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2d831295d7ca7babdc51a8b918284605b832410
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor uniform(const at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & uniform_out(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out);
+TORCH_API at::Tensor & uniform_(at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+TORCH_API at::Tensor & uniform_meta_(at::Tensor & self, double from=0, double to=1, ::std::optional<at::Generator> generator=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..25f7887730c7fabd74cef55878412f188e841595
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/uniform_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API uniform_ {
+  using schema = at::Tensor & (at::Tensor &, double, double, ::std::optional<at::Generator>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::uniform_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator);
+};
+
+struct TORCH_API uniform_out {
+  using schema = at::Tensor & (const at::Tensor &, double, double, ::std::optional<at::Generator>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::uniform";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator, at::Tensor & out);
+};
+
+struct TORCH_API uniform {
+  using schema = at::Tensor (const at::Tensor &, double, double, ::std::optional<at::Generator>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::uniform";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, ::std::optional<at::Generator> generator);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive.h
new file mode 100644
index 0000000000000000000000000000000000000000..d476d777bbb6fe31b75d8dbfce643b857b5cce80
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unique_consecutive_ops.h>
+
+namespace at {
+
+
+// aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt) {
+    return at::_ops::unique_consecutive::call(self, return_inverse, return_counts, dim);
+}
+
+// aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt) {
+    return at::_ops::unique_consecutive_out::call(self, return_inverse, return_counts, dim, out0, out1, out2);
+}
+// aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_outf(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
+    return at::_ops::unique_consecutive_out::call(self, return_inverse, return_counts, dim, out0, out1, out2);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..39c82074467bed3a40b5cc9749e35f175f3b9e53
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_outf(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..267dd270495973e3309eec8506ee0831c455dab3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..485a121dcbf04b8d22a7b1f977d5d38144408ecd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..1211192e69dc094621d39f628113176afba0f9d6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_consecutive_out(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive_cpu(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_consecutive_cuda(const at::Tensor & self, bool return_inverse=false, bool return_counts=false, ::std::optional<int64_t> dim=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..44b3786e5146f7ba482495c916a8f8b2fb878b64
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_consecutive_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unique_consecutive {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor> (const at::Tensor &, bool, bool, ::std::optional<int64_t>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_consecutive";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> call(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim);
+};
+
+struct TORCH_API unique_consecutive_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (const at::Tensor &, bool, bool, ::std::optional<int64_t>, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_consecutive";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))";
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> call(const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, ::std::optional<int64_t> dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim.h
new file mode 100644
index 0000000000000000000000000000000000000000..8aa76268652b76bb8a1feea0c28d4a7eb8bd9f5b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unique_dim_ops.h>
+
+namespace at {
+
+
+// aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
+    return at::_ops::unique_dim::call(self, dim, sorted, return_inverse, return_counts);
+}
+
+// aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) {
+    return at::_ops::unique_dim_out::call(self, dim, sorted, return_inverse, return_counts, out0, out1, out2);
+}
+// aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_outf(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
+    return at::_ops::unique_dim_out::call(self, dim, sorted, return_inverse, return_counts, out0, out1, out2);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cfd1ca3d1de705406ebb785410716dc9ea861d9d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_outf(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive.h
new file mode 100644
index 0000000000000000000000000000000000000000..fda1d7c1aa3218e0db5a1ecb71845d52a8d8adbe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unique_dim_consecutive_ops.h>
+
+namespace at {
+
+
+// aten::unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) {
+    return at::_ops::unique_dim_consecutive::call(self, dim, return_inverse, return_counts);
+}
+
+// aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) {
+    return at::_ops::unique_dim_consecutive_out::call(self, dim, return_inverse, return_counts, out0, out1, out2);
+}
+// aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))
+inline ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_outf(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) {
+    return at::_ops::unique_dim_consecutive_out::call(self, dim, return_inverse, return_counts, out0, out1, out2);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ac20e4a3b77b9b5e2ff35de21e45b84ea0c24c1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_outf(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..100f6b30a01dd648ea02d67d20b3ffd20a7e25e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..490355337a43062f3a94e191f301819104d09f22
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..499281b18be8a63da9d64d23d4bd8d2fc97c59ff
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_consecutive_out(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive_cpu(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_consecutive_cuda(const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a3457ce861e0374a77e9365db41a3cef592af3ab
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_consecutive_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unique_dim_consecutive {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor> (const at::Tensor &, int64_t, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_dim_consecutive";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> call(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts);
+};
+
+struct TORCH_API unique_dim_consecutive_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (const at::Tensor &, int64_t, bool, bool, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_dim_consecutive";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))";
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> call(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e737debc10d916e66acc6a9e83aeb467bba118b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b15fb6cf8e47283065235db5c564afaa8810043
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a2391f1d6d820792bad0f2f1f9915667c017645
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> unique_dim_out(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_cpu(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor,at::Tensor> unique_dim_cuda(const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..79af29201ddddd3fba8e44ecd255c2fec6c00fbb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unique_dim_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unique_dim {
+  using schema = ::std::tuple<at::Tensor,at::Tensor,at::Tensor> (const at::Tensor &, int64_t, bool, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_dim";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> call(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts);
+  static ::std::tuple<at::Tensor,at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts);
+};
+
+struct TORCH_API unique_dim_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> (const at::Tensor &, int64_t, bool, bool, bool, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unique_dim";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))";
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> call(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+  static ::std::tuple<at::Tensor &,at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1401f17e063930ea1ceebfcae004ccf47d2eede
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unsafe_chunk_ops.h>
+
+namespace at {
+
+
+// aten::unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> unsafe_chunk(const at::Tensor & self, int64_t chunks, int64_t dim=0) {
+    return at::_ops::unsafe_chunk::call(self, chunks, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ead0429070f1744dca3fdce954e8dc2ab07d1eb0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unsafe_chunk(const at::Tensor & self, int64_t chunks, int64_t dim=0);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..788911841b79beeb919586941d98ce0260789494
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> unsafe_chunk(const at::Tensor & self, int64_t chunks, int64_t dim=0);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..59237640b52df89817c081764bb55de4d56b98ec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_chunk_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unsafe_chunk {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsafe_chunk";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, int64_t chunks, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t chunks, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd6572434a67d1d7bd17db3a37f1d9cf8c00d581
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unsafe_split_ops.h>
+
+namespace at {
+
+
+// aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]
+inline ::std::vector<at::Tensor> unsafe_split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor::call(self, split_size, dim);
+  }
+}
+
+// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unsafe_split_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  void unsafe_split_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+// aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()
+inline void unsafe_split_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  void unsafe_split_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) {
+    return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d153a4ac2f8b3fbaab972c6b98d258fec8900331
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> unsafe_split_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+TORCH_API void unsafe_split_out(at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API void unsafe_split_outf(const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out);
+TORCH_API void unsafe_split_symint_out(at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0);
+TORCH_API void unsafe_split_symint_outf(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2aeae6dca18266a9cdcb8b50d6bb748ca3a1fb35
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> unsafe_split(const at::Tensor & self, int64_t split_size, int64_t dim=0);
+TORCH_API void unsafe_split_Tensor_out_symint(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff116d9d2dc47f9e66140345ee4798e5d2befea9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unsafe_split_Tensor {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymInt, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsafe_split";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim);
+};
+
+struct TORCH_API unsafe_split_Tensor_out {
+  using schema = void (const at::Tensor &, c10::SymInt, int64_t, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsafe_split";
+  static constexpr const char* overload_name = "Tensor_out";
+  static constexpr const char* schema_str = "unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> ()";
+  static void call(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f7c9db51ef65c0c349c512fa6c77e2c74d68ed1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API ::std::vector<at::Tensor> unsafe_split_with_sizes_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void unsafe_split_with_sizes_out(at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void unsafe_split_with_sizes_outf(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out);
+TORCH_API void unsafe_split_with_sizes_symint_out(at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void unsafe_split_with_sizes_symint_outf(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b753a5053ff82950361378e77c992b4bf42bab2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> unsafe_split_with_sizes(const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0);
+TORCH_API void unsafe_split_with_sizes_out_symint(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac7ec21702b473c729f60313a5d78f9a22ba379a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsafe_split_with_sizes_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unsafe_split_with_sizes {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, c10::SymIntArrayRef, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsafe_split_with_sizes";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim);
+};
+
+struct TORCH_API unsafe_split_with_sizes_out {
+  using schema = void (const at::Tensor &, c10::SymIntArrayRef, int64_t, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsafe_split_with_sizes";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()";
+  static void call(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze.h
new file mode 100644
index 0000000000000000000000000000000000000000..5abbe8932433d8b6c0e1b1d358b1644dfc8f5e78
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unsqueeze_ops.h>
+
+namespace at {
+
+
+// aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a)
+inline at::Tensor unsqueeze(const at::Tensor & self, int64_t dim) {
+    return at::_ops::unsqueeze::call(self, dim);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0fcea185ba7b6577fa61e1a23b084f5a0ebe6dc6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor unsqueeze(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & unsqueeze_(at::Tensor & self, int64_t dim);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..25de76f916be8aae14d7a2176a74e9668b8b2f4a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/unsqueeze_copy_ops.h>
+
+namespace at {
+
+
+// aten::unsqueeze_copy(Tensor self, int dim) -> Tensor
+inline at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim) {
+    return at::_ops::unsqueeze_copy::call(self, dim);
+}
+
+// aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unsqueeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim) {
+    return at::_ops::unsqueeze_copy_out::call(self, dim, out);
+}
+// aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & unsqueeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out) {
+    return at::_ops::unsqueeze_copy_out::call(self, dim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1792b2da9fcb5c12b812b19eba1a0ec119781413
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & unsqueeze_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & unsqueeze_copy_outf(const at::Tensor & self, int64_t dim, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cca8aab39827c6524f4742bb07c0c9a378848b0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..45b4240bf5f32ad22773c96e4a3ed01febf8e02e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & unsqueeze_copy_out(const at::Tensor & self, int64_t dim, at::Tensor & out);
+TORCH_API at::Tensor unsqueeze_copy(const at::Tensor & self, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b502abc2f188d1075c33bf8db695060d3c3fe7ce
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unsqueeze_copy {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsqueeze_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unsqueeze_copy(Tensor self, int dim) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API unsqueeze_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsqueeze_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, int64_t dim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a68b7e371dbd706c3e33477cee531ce0b5b40221
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor unsqueeze(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_nested(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_sparse(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor unsqueeze_quantized(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & unsqueeze_(at::Tensor & self, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..95b131948afee6fa3e2a708d6dab5ebcef2a49e6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/unsqueeze_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API unsqueeze {
+  using schema = at::Tensor (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsqueeze";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unsqueeze(Tensor(a) self, int dim) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, int64_t dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim);
+};
+
+struct TORCH_API unsqueeze_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::unsqueeze_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, int64_t dim);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb1eee8307ef430a7f247b25f26ae62755ddfbd4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_bicubic2d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bicubic2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bicubic2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_bicubic2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bicubic2d_vec::call(input, output_size, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bicubic2d_vec::call(input, output_size, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bicubic2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bicubic2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d::call(self, output_size, align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bicubic2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d::call(self, output_size, align_corners, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..a76c8840cdef39362dff5922d06cf603e4a9a34a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_bicubic2d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bicubic2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bicubic2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..16da4dddacebefb525c6b5e397cb0c679348e2bc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..992dd566bc7bc0d9f04c42d19b862eaeb275d52b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c0b3875745736ab57ba36f7528095156ccf7585
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb80feff695b033d80fe02ab2219e96781356e58
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_bicubic2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5c7631d959379b79e62aab544b2303a8fab5fdb5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_bicubic2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9232b313ef51ad00e0dfe56d4fab1f94ab2ccb9a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_bicubic2d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_bicubic2d_backward_out_cpu : public at::meta::structured_upsample_bicubic2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_bicubic2d_backward_out_cuda : public at::meta::structured_upsample_bicubic2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..21d40396362b7d12a744f341b78c61ef284ec412
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_bicubic2d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bicubic2d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_bicubic2d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bicubic2d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1ffe7c05d8f72f36d0dba85a34c664ee42a2e3d6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5851937d23e51f53094862f9cedaf329bc832675
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_bicubic2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b23dd0ad436db725f2a42f3e2f8a8f3a3cf97dd7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9fd6424235f370d0201706626414b1b13bf92116
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..bfa2d4e83c12d2b48e15f8fd0c66be1b8c3f944a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_bicubic2d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..62d48b676ab4eee38f2f0ea3dc695a4c8fbb3f05
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bicubic2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bicubic2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b242af63eaae01fc3272a3eb66cc0acf5c85b532
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_bicubic2d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_bicubic2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_bicubic2d_out_cpu : public at::meta::structured_upsample_bicubic2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_bicubic2d_out_cuda : public at::meta::structured_upsample_bicubic2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb4659ffaacf394f1542997a5f60d4b7ac87470c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bicubic2d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_bicubic2d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, bool, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bicubic2d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_bicubic2d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bicubic2d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+};
+
+struct TORCH_API upsample_bicubic2d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bicubic2d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..03a39d6e261f6ce98efcbb04b3fd9602b715b633
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d.h
@@ -0,0 +1,157 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_bilinear2d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_bilinear2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec::call(input, output_size, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec::call(input, output_size, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_out::call(self, output_size, align_corners, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d::call(self, output_size, align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bilinear2d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d::call(self, output_size, align_corners, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors, out);
+  }
+}
+
+// aten::upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors, out);
+  }
+}
+
+// aten::upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size, align_corners, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size, align_corners, scale_factors, out);
+  }
+}
+
+// aten::upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size, align_corners, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_bilinear2d_vec_out::call(input, output_size, align_corners, scale_factors, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..01fc8c3e8247c25d8763869b39f9948eba3157e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_bilinear2d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_bilinear2d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_bilinear2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..536ad148b79600c4c66af966688b68707f788cea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4e153303d59c0276bec51570be7e9d74869d71d8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4acae6da3f40c38cae62fa37a8e1fbd76c2cee8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..5bd39e525b92ec75e5b3a517a8718db041bf4d01
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_bilinear2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..36241b0998bb205ed76e8aec9cf811cc48df1162
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_bilinear2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..464203bad925381ca42b4925be2d2acd88d7ea0f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_bilinear2d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_bilinear2d_backward_out_cpu : public at::meta::structured_upsample_bilinear2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_bilinear2d_backward_out_cuda : public at::meta::structured_upsample_bilinear2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b640a9cf261836ae173e26b47644e99447c6350c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_bilinear2d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_bilinear2d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..40c4cd3d9aaf06042e47171aa7bc3510d68f8f2c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_bilinear2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0799f171959c6300582acbf1e1574b793eaaf34f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c554723846bfe5e9321d68bc3e16297445a291f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2a932bc5a695bb616182eaccfd8dc98a602b6ba
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..69461603fb7e765f8a0edabaf363bf5149da6f65
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..8a98e713625b350f4e40e453f24795bd052eaed1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_bilinear2d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..5ac6d48d792628539a827a53616b233e50de5c19
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_bilinear2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_bilinear2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..cfaeac343d469f2f1c3f484aeaa1c8ddc754754d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_bilinear2d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_bilinear2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_bilinear2d_vec_out_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+struct TORCH_API structured_upsample_bilinear2d_out_cpu : public at::meta::structured_upsample_bilinear2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_bilinear2d_out_cuda : public at::meta::structured_upsample_bilinear2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+TORCH_API at::Tensor upsample_bilinear2d_quantized_cpu(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..baa1d1571ea7ec377db1f3b33bc4bed44dc1e5f3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_bilinear2d_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_bilinear2d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, bool, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_bilinear2d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+};
+
+struct TORCH_API upsample_bilinear2d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+struct TORCH_API upsample_bilinear2d_vec_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalSymIntArrayRef, bool, ::std::optional<at::ArrayRef<double>>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_bilinear2d";
+  static constexpr const char* overload_name = "vec_out";
+  static constexpr const char* schema_str = "upsample_bilinear2d.vec_out(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d.h
new file mode 100644
index 0000000000000000000000000000000000000000..70a9306600de7a04491a4163eba0314913eea764
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_linear1d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_linear1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_linear1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_linear1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_linear1d_vec::call(input, output_size, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_linear1d_vec::call(input, output_size, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
+  }
+}
+
+// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_linear1d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out);
+  }
+}
+
+// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
+  }
+}
+
+// aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_linear1d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_linear1d_out::call(self, output_size, align_corners, scales, out);
+  }
+}
+
+// aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor
+inline at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales);
+  }
+}
+
+// aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor
+inline at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d::call(self, output_size, align_corners, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_linear1d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d::call(self, output_size, align_corners, scales);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b5d5abaf1cd29ca28a25bda0daf46127ae346d1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_linear1d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
+  }
+}
+
+// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input);
+  }
+}
+
+// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
+  }
+}
+
+// aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_linear1d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales, grad_input);
+  }
+}
+
+// aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor
+inline at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales);
+  }
+}
+
+// aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor
+inline at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward::call(grad_output, output_size, input_size, align_corners, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_linear1d_backward::call(grad_output, output_size, input_size, align_corners, scales);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..cba02f4ef660188d87be3c9e1e0b851db31af365
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..358bde45b61020eca95f706a0115d27ead433a1c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7cfb3fe4ab165242a91b9448bc5501c60ef45b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..a554043f3121c9eeffae0f0e505ffa260d0db779
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_linear1d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e3ead5c0a8396806c54f852d8b6659f7307a129
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_linear1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7d5194edd437f4a797c274299cf54af745f1d49c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_linear1d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_linear1d_backward_out_cpu : public at::meta::structured_upsample_linear1d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_linear1d_backward_out_cuda : public at::meta::structured_upsample_linear1d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5ef04bfd8f3468aa02ebbd8080636abf125ae14
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_linear1d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_linear1d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_linear1d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_linear1d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d190c903ba8e22cf2984ce43d954c7b741bddf67
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..64362c9433849e1832bbbbdd11465a4c9edb1ace
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..59853c019aaa861c659b5d0c2356dc22017b8d69
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b4e694d15735e753858f120a81483940287526e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..28d018db8cefb748b6a478feb1e69c7997981b77
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_linear1d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c9db4ba170ee972bc09ba190ba5732ae6ac22c8b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_linear1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_linear1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_linear1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c413a2b881a99980203f6827ddc498d2fb48ece9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_linear1d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_linear1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_linear1d_out_cpu : public at::meta::structured_upsample_linear1d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_linear1d_out_cuda : public at::meta::structured_upsample_linear1d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..c0479bdb4bdba4980bc1b960d158f4fa7d586a2c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_linear1d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_linear1d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, bool, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_linear1d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_linear1d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_linear1d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales, at::Tensor & out);
+};
+
+struct TORCH_API upsample_linear1d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_linear1d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d.h
new file mode 100644
index 0000000000000000000000000000000000000000..ab48b1c42d1eecd687a38aa3db5287f10e79e86c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest1d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest1d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+  }
+}
+
+// aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest1d_vec::call(input, output_size, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest1d_vec::call(input, output_size, scale_factors);
+  }
+}
+
+// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
+  }
+}
+
+// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_nearest1d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales, out);
+  }
+}
+
+// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
+  }
+}
+
+// aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out) {
+    return at::_ops::upsample_nearest1d_out::call(self, output_size, scales, out);
+  }
+}
+
+// aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
+inline at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d::call(self, c10::fromIntArrayRefSlow(output_size), scales);
+  }
+}
+
+// aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor
+inline at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d::call(self, output_size, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest1d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d::call(self, output_size, scales);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..f37bd6281c507a321c2e406ccaded0bd85a8ec6e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest1d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
+  }
+}
+
+// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input);
+  }
+}
+
+// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
+  }
+}
+
+// aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest1d_backward_grad_input::call(grad_output, output_size, input_size, scales, grad_input);
+  }
+}
+
+// aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
+inline at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales);
+  }
+}
+
+// aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor
+inline at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward::call(grad_output, output_size, input_size, scales);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt) {
+    return at::_ops::upsample_nearest1d_backward::call(grad_output, output_size, input_size, scales);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4ea664f3c1403ff9bc1422ed865f97165ae4a461
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..841421c492d004ea9786f6e908415dc5ec1558e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3abf0e46b6abb69f195a51eafcfe1f25f3891814
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9e9c4aa45ef85b7efa7b9e844589920249a3101
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest1d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2cf8b84150f16c1893a76b7432a07e1710b18925
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest1d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3f9e935f7067ae16062a452a09b79fd90483ba99
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest1d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_nearest1d_backward_out_cpu : public at::meta::structured_upsample_nearest1d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_nearest1d_backward_out_cuda : public at::meta::structured_upsample_nearest1d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..dfab904aae4d57875570e732afe7159f66869025
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest1d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest1d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_nearest1d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest1d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4107b971754f92e7791e97bc0ba311f90bb817f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..47f7ce375d97755dcd05bf29504e5d8a9e4b4715
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_nearest1d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..32733249fff8d3385b3580105c14005bcf5378a4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..05a24f2a16c7fc79ef26790c05d171e08c6ecdde
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..081206503a2a205d68b4429a34850044c753fbf7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest1d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..15230d9c745f5eeb418d300f3e8cfb06ce6e942b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest1d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest1d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest1d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9299cf0ca07da478570148fcb52e72371a40f512
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest1d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_nearest1d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_nearest1d_out_cpu : public at::meta::structured_upsample_nearest1d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_nearest1d_out_cuda : public at::meta::structured_upsample_nearest1d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f86005e6b8f49d6b129213796184d53a8e53bc7f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest1d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest1d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest1d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_nearest1d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest1d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales, at::Tensor & out);
+};
+
+struct TORCH_API upsample_nearest1d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest1d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..d0375ee76fcf10cde91991a4b777eba2e2723d76
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d.h
@@ -0,0 +1,157 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest2d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+  }
+}
+
+// aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec::call(input, output_size, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec::call(input, output_size, scale_factors);
+  }
+}
+
+// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_out::call(self, output_size, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d::call(self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w);
+  }
+}
+
+// aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d::call(self, output_size, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest2d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d::call(self, output_size, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors, out);
+  }
+}
+
+// aten::upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors, out);
+  }
+}
+
+// aten::upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size, scale_factors, out);
+  }
+}
+
+// aten::upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size, scale_factors, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out) {
+    return at::_ops::upsample_nearest2d_vec_out::call(input, output_size, scale_factors, out);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..a4b70d4fd306c58c3c62a2534901673d692a0b04
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest2d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest2d_backward_grad_input::call(grad_output, output_size, input_size, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w);
+  }
+}
+
+// aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2841679aa6f2baac5cf618cd9cf445c5eeeda961
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d66a08147b49855c31afa9b5e6663dd80b11d2e2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dffe70d946d53cc2110a59b66e30cd22b4c9ff9c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..eef0d3653a9d984189567f04a52c95e2ed984891
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest2d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..90c9637be08428bdedb37fe43c408bd6b852e358
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest2d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ddd7f9f74632c4d2d0e7e37f78ef7002934d42e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest2d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_nearest2d_backward_out_cpu : public at::meta::structured_upsample_nearest2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_nearest2d_backward_out_cuda : public at::meta::structured_upsample_nearest2d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8209f126762c372885d3c95520ed098f1c05e3fd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest2d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_nearest2d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..51416813354bd070c952bb3683971216044b6746
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_nearest2d_outf(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f0a9bae67d83e980a965f6bc38ff770017c35020
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f3e752db2009feb80a26f9937b1cec3cffd1eb44
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b6a7bd3b3f9ed6f341a491486b7e6d7aa0346ce0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa10e8f61906e20e1cc9079f2aecdadfad3e0314
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..383ac01dd1b07200cc39f52be29c58d74a15bf92
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest2d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d03fd79c0fe51a2015afa6fc0eb2a08d793e5ec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest2d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..88d06b8141c836c60291a4493b1b9d6d9d85fa0b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest2d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_nearest2d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor & upsample_nearest2d_vec_out_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+struct TORCH_API structured_upsample_nearest2d_out_cpu : public at::meta::structured_upsample_nearest2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_nearest2d_out_cuda : public at::meta::structured_upsample_nearest2d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+TORCH_API at::Tensor upsample_nearest2d_quantized_cpu(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6d158b0bed4646768d343f5199358273e0e792e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest2d_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest2d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_nearest2d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+};
+
+struct TORCH_API upsample_nearest2d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+struct TORCH_API upsample_nearest2d_vec_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalSymIntArrayRef, ::std::optional<at::ArrayRef<double>>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest2d";
+  static constexpr const char* overload_name = "vec_out";
+  static constexpr const char* schema_str = "upsample_nearest2d.vec_out(Tensor input, SymInt[]? output_size, float[]? scale_factors, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d.h
new file mode 100644
index 0000000000000000000000000000000000000000..6066df9922aa35c04410bdb99b74873da7caf73a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest3d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, scale_factors);
+  }
+}
+
+// aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_nearest3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest3d_vec::call(input, output_size, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_nearest3d_vec::call(input, output_size, scale_factors);
+  }
+}
+
+// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest3d_out::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_nearest3d_out::call(self, output_size, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d::call(self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d::call(self, output_size, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest3d(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d::call(self, output_size, scales_d, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..574fe66dd648fc29347be459d66f0fc04904e08e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_nearest3d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_nearest3d_backward_grad_input::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_nearest3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4c4973b8a3eed7aa40215b8164c873090f315132
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..61ac4bb2861e9cc6ed0d5031c3da86e66bd39714
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..350e37d1d2c14af7fab3d6dafe2d177e9d57e397
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..1783abf51a088652550c6537290d18afcfa0f92c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest3d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8041e3a9481da2b8618b77144328ebd324b4ceb8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..8486f04595b62483366072b98de173e8312d9381
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest3d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_nearest3d_backward_out_cpu : public at::meta::structured_upsample_nearest3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_nearest3d_backward_out_cuda : public at::meta::structured_upsample_nearest3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..abd3727da5ca76405e00bc847c79efd6ecae3a11
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest3d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest3d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_nearest3d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest3d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..421c1b3d0586382ecdbb5399dd2e612324fca9a7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f293e4b932a6f9f4080504c95838c5d08b5153a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_nearest3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad33c175ef7602b0ee0cc94de6263fec63b1f0b9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..721a3e904dff21209b5a099df3e1740e0d30408a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..a57a42c5f68e90c4a4140fdac3dccc8d58551669
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest3d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..99c9518bef1e414edfa0f417dfd711b81e480a49
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_nearest3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_outf(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_nearest3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_nearest3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..d0686854fa67bc7cc96570e982e4f909e646099d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_nearest3d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_nearest3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_nearest3d_out_cpu : public at::meta::structured_upsample_nearest3d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_nearest3d_out_cuda : public at::meta::structured_upsample_nearest3d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+TORCH_API at::Tensor upsample_nearest3d_quantized_cpu(const at::Tensor & self, at::IntArrayRef output_size, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..76b2356b35061f2d79ad42a97114b096d4cc5202
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_nearest3d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_nearest3d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest3d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_nearest3d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest3d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+};
+
+struct TORCH_API upsample_nearest3d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_nearest3d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d.h
new file mode 100644
index 0000000000000000000000000000000000000000..9144187e33db55488abc0a45675f4e4d96eee9a2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d.h
@@ -0,0 +1,113 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_trilinear3d_ops.h>
+
+namespace at {
+
+
+// aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_trilinear3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_trilinear3d_vec::call(input, output_size.has_value() ? ::std::make_optional(c10::fromIntArrayRefSlow(*output_size)) : ::std::nullopt, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor
+inline at::Tensor upsample_trilinear3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_trilinear3d_vec::call(input, output_size, align_corners, scale_factors);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors) {
+    return at::_ops::upsample_trilinear3d_vec::call(input, output_size, align_corners, scale_factors);
+  }
+}
+
+// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_trilinear3d_out::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out) {
+    return at::_ops::upsample_trilinear3d_out::call(self, output_size, align_corners, scales_d, scales_h, scales_w, out);
+  }
+}
+
+// aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d::call(self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d::call(self, output_size, align_corners, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_trilinear3d(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d::call(self, output_size, align_corners, scales_d, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..f1a05658f487c891675b917231983149fb8feba4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/upsample_trilinear3d_backward_ops.h>
+
+namespace at {
+
+
+// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)
+inline at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input) {
+    return at::_ops::upsample_trilinear3d_backward_grad_input::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input);
+  }
+}
+
+// aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward::call(grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w);
+  }
+}
+
+// aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor
+inline at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt) {
+    return at::_ops::upsample_trilinear3d_backward::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..15798dafcb59a3912a04b5f779ab9e1ae80b37e6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4da0ac52555529307131ca16ac0314ce04b04f73
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d610ab6119e77b0754f510896e475af82b52c8e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9e46f5ce0f419469c67e58222d5360a73c8b96b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_trilinear3d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..516c778dc2ae1c517e4e25a58db4b541dfc3ffda
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_trilinear3d_backward(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_backward_symint(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_outf(const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_backward_symint_outf(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..30f6bc43a3b2ee2ec4f10a4f102cd8c981d508f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_trilinear3d_backward_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_trilinear3d_backward_out_cpu : public at::meta::structured_upsample_trilinear3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_trilinear3d_backward_out_cuda : public at::meta::structured_upsample_trilinear3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef<int64_t> output_size, at::ArrayRef<int64_t> input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..0620718f04f000daabe0aaa8f92c279394c9d838
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_trilinear3d_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_trilinear3d_backward";
+  static constexpr const char* overload_name = "grad_input";
+  static constexpr const char* schema_str = "upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & grad_input);
+};
+
+struct TORCH_API upsample_trilinear3d_backward {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_trilinear3d_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf71935ae9b2077edeb547d037f36e98d2a00602
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e569b24e061c856e36d5c86a2e0f533448ea41c7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+TORCH_API at::Tensor upsample_trilinear3d_symint(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f085020b38f330728f9012051b52549f895556e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cpu_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..267212712c74ecd8181030f46345db862714699e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_cuda_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..9180f967c26f0712c0c4651c15cd495111a58f4d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_trilinear3d : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c120647c37b3a0c11b5f2b8a839c5d3b76bcdefa
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_meta_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor upsample_trilinear3d_symint(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_outf(const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d=::std::nullopt, ::std::optional<double> scales_h=::std::nullopt, ::std::optional<double> scales_w=::std::nullopt);
+TORCH_API at::Tensor & upsample_trilinear3d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..acd815cd4a5014621650998ddf30e86db0869362
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_native.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/upsample_trilinear3d_meta.h>
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor upsample_trilinear3d(const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+struct TORCH_API structured_upsample_trilinear3d_out_cpu : public at::meta::structured_upsample_trilinear3d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+struct TORCH_API structured_upsample_trilinear3d_out_cuda : public at::meta::structured_upsample_trilinear3d {
+void impl(const at::Tensor & self, at::ArrayRef<int64_t> output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7c4c3c2d4e7de0c97cf38a1304c46fb769404a5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/upsample_trilinear3d_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API upsample_trilinear3d_vec {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalSymIntArrayRef, bool, ::std::optional<at::ArrayRef<double>>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_trilinear3d";
+  static constexpr const char* overload_name = "vec";
+  static constexpr const char* schema_str = "upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor";
+  static at::Tensor call(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, ::std::optional<at::ArrayRef<double>> scale_factors);
+};
+
+struct TORCH_API upsample_trilinear3d_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_trilinear3d";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w, at::Tensor & out);
+};
+
+struct TORCH_API upsample_trilinear3d {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, bool, ::std::optional<double>, ::std::optional<double>, ::std::optional<double>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::upsample_trilinear3d";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, ::std::optional<double> scales_d, ::std::optional<double> scales_h, ::std::optional<double> scales_w);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward.h b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8b9edee0fdcc30470c27fe4b198d8fe955c9cbc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward.h
@@ -0,0 +1,47 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/value_selecting_reduction_backward_ops.h>
+
+namespace at {
+
+
+// aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor
+inline at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim) {
+    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, c10::fromIntArrayRefSlow(sizes), keepdim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim) {
+    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, c10::fromIntArrayRefSlow(sizes), keepdim);
+  }
+}
+
+// aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor
+inline at::Tensor value_selecting_reduction_backward_symint(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) {
+    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, sizes, keepdim);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) {
+    return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, sizes, keepdim);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f52eeb3902f3a06f8e2e2494c2f6848d2f398f0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor value_selecting_reduction_backward(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim);
+TORCH_API at::Tensor value_selecting_reduction_backward_symint(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..84acbe98c86531b1a01878f6d06cf95bdaeb334b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor value_selecting_reduction_backward_symint(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a46d939951a1560a7114a6afa98b342de8019543
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/value_selecting_reduction_backward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API value_selecting_reduction_backward {
+  using schema = at::Tensor (const at::Tensor &, int64_t, const at::Tensor &, c10::SymIntArrayRef, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::value_selecting_reduction_backward";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor";
+  static at::Tensor call(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values.h
new file mode 100644
index 0000000000000000000000000000000000000000..83cacbf8df7d0e767a7d39bd2234184bb815da95
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/values_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..64ba2c58b943040969483e98cbe02758c48cbb09
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor values(const at::Tensor & self);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..2204d2748c8230962e5e97549533962a43ce2e62
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/values_copy_ops.h>
+
+namespace at {
+
+
+// aten::values_copy(Tensor self) -> Tensor
+inline at::Tensor values_copy(const at::Tensor & self) {
+    return at::_ops::values_copy::call(self);
+}
+
+// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & values_copy_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::values_copy_out::call(self, out);
+}
+// aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & values_copy_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::values_copy_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1a2d836f39ae8b6cd48e32fde91d07b2c7e1e4d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & values_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & values_copy_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c27111f8c7dc8d214a2e52524a35953bae6defaf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor values_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..d9d3c8b445915bbe1fe805bc505d084567199a21
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & values_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor values_copy(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..aeb6c2a3d01d4adf096c91718058c73a42026902
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API values_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::values_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "values_copy(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API values_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::values_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b829db4d1ab77b73faa710d4639d6f060f5ccc54
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor values_default(const at::Tensor & self);
+TORCH_API at::Tensor values_nested(const at::Tensor & self);
+TORCH_API at::Tensor values_sparse(const at::Tensor & self);
+TORCH_API at::Tensor values_sparse_csr(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/values_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/values_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7934f1d95d4357b27c7251e325133c35eacc3202
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/values_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API values {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::values";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "values(Tensor(a) self) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vander.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vander.h
new file mode 100644
index 0000000000000000000000000000000000000000..86ffc2295a3e0238ba425ed201b979bb606beb29
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vander.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/vander_ops.h>
+
+namespace at {
+
+
+// aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor
+inline at::Tensor vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt, bool increasing=false) {
+    return at::_ops::vander::call(x, N, increasing);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vander_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f27e327fe80e869547790be0f7b14717736f485a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt, bool increasing=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vander_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..96255830849d21a5c14b1651cdf37d4ff44a52ca
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor vander(const at::Tensor & x, ::std::optional<int64_t> N=::std::nullopt, bool increasing=false);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vander_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa4a2d5a34183444bbfa30e4b21fcba8d3a8a362
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vander_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vander {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<int64_t>, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vander";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "vander(Tensor x, int? N=None, bool increasing=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & x, ::std::optional<int64_t> N, bool increasing);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, ::std::optional<int64_t> N, bool increasing);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var.h
new file mode 100644
index 0000000000000000000000000000000000000000..40e84d52e7c76dc1b51a7539f26fc433b5d1c97f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var.h
@@ -0,0 +1,86 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/var_ops.h>
+
+namespace at {
+
+
+// aten::var(Tensor self, bool unbiased=True) -> Tensor
+inline at::Tensor var(const at::Tensor & self, bool unbiased) {
+    return at::_ops::var::call(self, unbiased);
+}
+
+// aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor
+inline at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor
+inline at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_correction::call(self, dim, correction, keepdim);
+}
+
+// aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_out::call(self, dim, unbiased, keepdim, out);
+}
+// aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) {
+    return at::_ops::var_out::call(self, dim, unbiased, keepdim, out);
+}
+
+// aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_correction_out::call(self, dim, correction, keepdim, out);
+}
+// aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
+    return at::_ops::var_correction_out::call(self, dim, correction, keepdim, out);
+}
+
+// aten::var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor
+inline at::Tensor var(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_names_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_names_out::call(self, dim, unbiased, keepdim, out);
+}
+// aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) {
+    return at::_ops::var_names_out::call(self, dim, unbiased, keepdim, out);
+}
+
+// aten::var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor
+inline at::Tensor var(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_correction_names::call(self, dim, correction, keepdim);
+}
+
+// aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_correction_names_out::call(self, dim, correction, keepdim, out);
+}
+// aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out) {
+    return at::_ops::var_correction_names_out::call(self, dim, correction, keepdim, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6c49899dffbe1da58a06c37eebb281330c31a2ef
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,32 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor var(const at::Tensor & self, bool unbiased);
+TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_outf(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b373a33d14e67826f2495c1a76afbfc91ccafaf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9911703cedd65fd025deb33ef0f6fb8f7d42f1b3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa66e67d500d3e7df899d15f9d818ad14d0bd0ba
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean.h
@@ -0,0 +1,59 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/var_mean_ops.h>
+
+namespace at {
+
+
+// aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, bool unbiased) {
+    return at::_ops::var_mean::call(self, unbiased);
+}
+
+// aten::var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_mean_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_mean_correction::call(self, dim, correction, keepdim);
+}
+
+// aten::var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) {
+    return at::_ops::var_mean_names_dim::call(self, dim, unbiased, keepdim);
+}
+
+// aten::var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)
+inline ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_mean_correction_names::call(self, dim, correction, keepdim);
+}
+
+// aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> var_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false) {
+    return at::_ops::var_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
+}
+// aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))
+inline ::std::tuple<at::Tensor &,at::Tensor &> var_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) {
+    return at::_ops::var_mean_correction_out::call(self, dim, correction, keepdim, out0, out1);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..70971e8c8d4c8a2782c4cbcad23167dc49c9edfb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> var_mean_out(at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> var_mean_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..79bfb527b1b18a044615d78edab44d1d57afc3ea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, bool unbiased);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f95f48ebc19c5d4fe01699220a69d4881658e8c4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d192faea11b55997b805bc29f972e7a2bbed0b5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..cdd0a1da3a5964fe796589f33802fe810b711bce
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, bool unbiased=true);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased=true, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor &,at::Tensor &> var_mean_correction_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, bool unbiased=true, bool keepdim=false);
+TORCH_API ::std::tuple<at::Tensor,at::Tensor> var_mean(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..f730dfac0d0100a9ab16b7d7aa3ff2984051bbdc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_mean_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API var_mean {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, bool unbiased);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased);
+};
+
+struct TORCH_API var_mean_dim {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::OptionalIntArrayRef, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API var_mean_correction {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "correction";
+  static constexpr const char* schema_str = "var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API var_mean_names_dim {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::DimnameList, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "names_dim";
+  static constexpr const char* schema_str = "var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API var_mean_correction_names {
+  using schema = ::std::tuple<at::Tensor,at::Tensor> (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "correction_names";
+  static constexpr const char* schema_str = "var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor)";
+  static ::std::tuple<at::Tensor,at::Tensor> call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static ::std::tuple<at::Tensor,at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API var_mean_correction_out {
+  using schema = ::std::tuple<at::Tensor &,at::Tensor &> (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var_mean";
+  static constexpr const char* overload_name = "correction_out";
+  static constexpr const char* schema_str = "var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))";
+  static ::std::tuple<at::Tensor &,at::Tensor &> call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+  static ::std::tuple<at::Tensor &,at::Tensor &> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..6905808d3303718098c30622c3a37c368ee878a7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_native.h
@@ -0,0 +1,29 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor var(const at::Tensor & self, bool unbiased=true);
+TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased=true, bool keepdim=false);
+TORCH_API at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor var(const at::Tensor & self, at::OptionalIntArrayRef dim=::std::nullopt, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_out(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, bool unbiased=true, bool keepdim=false);
+TORCH_API at::Tensor & var_out(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor var(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction=::std::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & var_out(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/var_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/var_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc59ac3a402b95328733a44ea9e068bb1e82bd31
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/var_ops.h
@@ -0,0 +1,116 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API var {
+  using schema = at::Tensor (const at::Tensor &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "var(Tensor self, bool unbiased=True) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, bool unbiased);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased);
+};
+
+struct TORCH_API var_dim {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "dim";
+  static constexpr const char* schema_str = "var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API var_correction {
+  using schema = at::Tensor (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "correction";
+  static constexpr const char* schema_str = "var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API var_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalIntArrayRef, bool, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API var_correction_out {
+  using schema = at::Tensor & (const at::Tensor &, at::OptionalIntArrayRef, const ::std::optional<at::Scalar> &, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "correction_out";
+  static constexpr const char* schema_str = "var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API var_names_dim {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, bool, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "names_dim";
+  static constexpr const char* schema_str = "var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim);
+};
+
+struct TORCH_API var_names_out {
+  using schema = at::Tensor & (const at::Tensor &, at::DimnameList, bool, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "names_out";
+  static constexpr const char* schema_str = "var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+};
+
+struct TORCH_API var_correction_names {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "correction_names";
+  static constexpr const char* schema_str = "var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim);
+};
+
+struct TORCH_API var_correction_names_out {
+  using schema = at::Tensor & (const at::Tensor &, at::DimnameList, const ::std::optional<at::Scalar> &, bool, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::var";
+  static constexpr const char* overload_name = "correction_names_out";
+  static constexpr const char* schema_str = "var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const ::std::optional<at::Scalar> & correction, bool keepdim, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot.h
new file mode 100644
index 0000000000000000000000000000000000000000..c34232a9a26082bf1ab04b93d3ae19d58f3eedbd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/vdot_ops.h>
+
+namespace at {
+
+
+// aten::vdot(Tensor self, Tensor other) -> Tensor
+inline at::Tensor vdot(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::vdot::call(self, other);
+}
+
+// aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vdot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::vdot_out::call(self, other, out);
+}
+// aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vdot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::vdot_out::call(self, other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1049b7b4874e7f50311bef4b819d0f79a3bf15fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & vdot_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & vdot_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..29eadc66409f11511d66c544efca700d6bf35980
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor vdot(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..323c083f5b5346aac90afbb5582c2c8f68aab0de
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor vdot(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ef17f706383898f30d381b839b4a7a1e80fed378
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & vdot_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor vdot(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor vdot_cuda(const at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd6265f94a7dd8ac1cd79fd5d47c74d545a83ae4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vdot_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vdot {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vdot";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "vdot(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API vdot_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vdot";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view.h
new file mode 100644
index 0000000000000000000000000000000000000000..6872cc05e6a7d3d4fa409ff6ab69af1c581def7d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_ops.h>
+
+namespace at {
+
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor view(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::view::call(self, c10::fromIntArrayRefSlow(size));
+  }
+}
+
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor view(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::view::call(self, size);
+  }
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as.h
new file mode 100644
index 0000000000000000000000000000000000000000..687234c7317c630c44c638c01fde8d1022cb8ea2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_as_ops.h>
+
+namespace at {
+
+
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..b3f2044dd5a6fa40c5918e5d77ebd73c4f906691
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_as_complex_ops.h>
+
+namespace at {
+
+
+// aten::view_as_complex(Tensor(a) self) -> Tensor(a)
+inline at::Tensor view_as_complex(const at::Tensor & self) {
+    return at::_ops::view_as_complex::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce74580adb87825d122b2713a6fbb569f68c649f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_as_complex_copy_ops.h>
+
+namespace at {
+
+
+// aten::view_as_complex_copy(Tensor self) -> Tensor
+inline at::Tensor view_as_complex_copy(const at::Tensor & self) {
+    return at::_ops::view_as_complex_copy::call(self);
+}
+
+// aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_as_complex_copy_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::view_as_complex_copy_out::call(self, out);
+}
+// aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_as_complex_copy_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::view_as_complex_copy_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f4b30f1e4255c304de258635fb05c93ad58e7711
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & view_as_complex_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & view_as_complex_copy_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c1fac4b58f54721fccdd1678cf1a57067388a4a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor view_as_complex_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5533c1b50c95dd52f8d2b696acdd2119a53f5a5b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & view_as_complex_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor view_as_complex_copy(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd7c0daa7da13c4881a6d59f2a55df630abc3c42
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as_complex_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_complex_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_as_complex_copy(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API view_as_complex_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_complex_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b867f9f4f550b2bbb12b9b494a27af850fc87c46
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor view_as_complex(const at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f1e571fcf2c497f02a4942aea90d55dfcf34bf74
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor view_as_complex(const at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..188579f0915744a3d727213296e8785efa056711
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_meta_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor view_as_complex(const at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..5c16c2886f0937900f171c8db992520237426334
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor view_as_complex(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee592b16e2f6b5651e11bf1bada79ad5933413a4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_complex_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as_complex {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_complex";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_as_complex(Tensor(a) self) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca57511cb42bc27bd05e434a80937e49584a362c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor view_as(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..309ad0fa4f6cc402028540157be87d5270950a90
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor view_as(const at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a4c7c69451c564895461443b7d6cc3f4376c5e1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_as(Tensor(a) self, Tensor other) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real.h
new file mode 100644
index 0000000000000000000000000000000000000000..afafa806aea78cc1f485270d472a8e5d153e73f9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_as_real_ops.h>
+
+namespace at {
+
+
+// aten::view_as_real(Tensor(a) self) -> Tensor(a)
+inline at::Tensor view_as_real(const at::Tensor & self) {
+    return at::_ops::view_as_real::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d8a4d4c170751eae9fbdf7953b9699ed6eddf6a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_as_real_copy_ops.h>
+
+namespace at {
+
+
+// aten::view_as_real_copy(Tensor self) -> Tensor
+inline at::Tensor view_as_real_copy(const at::Tensor & self) {
+    return at::_ops::view_as_real_copy::call(self);
+}
+
+// aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_as_real_copy_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::view_as_real_copy_out::call(self, out);
+}
+// aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_as_real_copy_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::view_as_real_copy_out::call(self, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9054bc9e2eeb9cbabe83cd443455ad272907d533
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & view_as_real_copy_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & view_as_real_copy_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..86d644a2f214ba08720dcc0b946e9044ed2e7bd9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor view_as_real_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..bece11bea2fb2b99817c029d3445af19abbca081
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & view_as_real_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor view_as_real_copy(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..384a39e2a114d4134268f3c4924c516119c90737
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_copy_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as_real_copy {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_real_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_as_real_copy(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API view_as_real_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_real_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d830e8bb2aa6e3b6f518e3168899d50c72a416c3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor view_as_real(const at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f73e2a9b5f1aede026ad7cd32488f728cd2eb2d6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor view_as_real(const at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b21edfc305bb42905aeb9b68bb7cbeaa74fd7df
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_meta_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor view_as_real(const at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..933fd19e84b14f94abd391c28aa9a8d276e04bec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor view_as_real(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6c727a7cb045cca8c970fc47ff0126d53710220a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_as_real_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as_real {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_as_real";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_as_real(Tensor(a) self) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba8e92f6a12174ee3fbf24376b6d1b8ad81951da
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor view(const at::Tensor & self, at::ScalarType dtype);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy.h
new file mode 100644
index 0000000000000000000000000000000000000000..a63b5740d75e7c1b0970f569a89cec0bbf15302d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy.h
@@ -0,0 +1,105 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/view_copy_ops.h>
+
+namespace at {
+
+
+// aten::view_copy(Tensor self, SymInt[] size) -> Tensor
+inline at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::view_copy::call(self, c10::fromIntArrayRefSlow(size));
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::view_copy::call(self, c10::fromIntArrayRefSlow(size));
+  }
+}
+
+// aten::view_copy(Tensor self, SymInt[] size) -> Tensor
+inline at::Tensor view_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::view_copy::call(self, size);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor view_copy(const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::view_copy::call(self, size);
+  }
+}
+
+// aten::view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor
+inline at::Tensor view_copy(const at::Tensor & self, at::ScalarType dtype) {
+    return at::_ops::view_copy_dtype::call(self, dtype);
+}
+
+// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) {
+    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & view_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::view_copy_out::call(self, c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::view_copy_out::call(self, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) {
+    return at::_ops::view_copy_out::call(self, size, out);
+  }
+}
+
+// aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::view_copy_out::call(self, size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & view_copy_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::view_copy_out::call(self, size, out);
+  }
+}
+
+// aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::ScalarType dtype) {
+    return at::_ops::view_copy_dtype_out::call(self, dtype, out);
+}
+// aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & view_copy_outf(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out) {
+    return at::_ops::view_copy_dtype_out::call(self, dtype, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7827b3e7ad7c2aae9690cb28e325c447effc406b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,28 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor & view_copy_outf(const at::Tensor & self, at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & view_copy_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & view_copy_symint_outf(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & view_copy_out(at::Tensor & out, const at::Tensor & self, at::ScalarType dtype);
+TORCH_API at::Tensor & view_copy_outf(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2dd2c20b76e29daa972f8c5a6ecac15ff704e47
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor view_copy(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+TORCH_API at::Tensor view_copy(const at::Tensor & self, at::ScalarType dtype);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..181610609dd7d983ef29da296033c91cafb02b15
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & view_copy_out_symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor view_copy_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & view_copy_dtype_out(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out);
+TORCH_API at::Tensor view_copy_dtype(const at::Tensor & self, at::ScalarType dtype);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4562eae9c6a573beab9094821321a898a6a6f7da
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_copy_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_copy {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_copy";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view_copy(Tensor self, SymInt[] size) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size);
+};
+
+struct TORCH_API view_copy_dtype {
+  using schema = at::Tensor (const at::Tensor &, at::ScalarType);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_copy";
+  static constexpr const char* overload_name = "dtype";
+  static constexpr const char* schema_str = "view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, at::ScalarType dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype);
+};
+
+struct TORCH_API view_copy_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_copy";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API view_copy_dtype_out {
+  using schema = at::Tensor & (const at::Tensor &, at::ScalarType, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view_copy";
+  static constexpr const char* overload_name = "dtype_out";
+  static constexpr const char* schema_str = "view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::ScalarType dtype, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..91fec458ab91b112c78fd1fd6cb18955c469d394
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b3f3cd53a586a6dbd1954b2b0a2aaed364277ab
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a03452aa9fd2a975250c202e431fe0f21012f286
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_meta_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_symint(const at::Tensor & self, c10::SymIntArrayRef size);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2e19524228faa48136974eb7f3ec64eac22a2b7a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_nested(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor mkldnn_view(const at::Tensor & self, at::IntArrayRef size);
+TORCH_API at::Tensor view_dtype(const at::Tensor & self, at::ScalarType dtype);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/view_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/view_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..130cc1aa1957fab0dd205b2050e92bbf55e36631
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/view_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "view(Tensor(a) self, SymInt[] size) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size);
+};
+
+struct TORCH_API view_dtype {
+  using schema = at::Tensor (const at::Tensor &, at::ScalarType);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::view";
+  static constexpr const char* overload_name = "dtype";
+  static constexpr const char* schema_str = "view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a)";
+  static at::Tensor call(const at::Tensor & self, at::ScalarType dtype);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c8a1ee1be041d2a6ca710db47149c3572b4678f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/vsplit_ops.h>
+
+namespace at {
+
+
+// aten::vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections) {
+    return at::_ops::vsplit_int::call(self, sections);
+}
+
+// aten::vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]
+inline ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices) {
+    return at::_ops::vsplit_array::call(self, indices);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..248adc9ead211c73a62affee2bf6b4721a9109e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections);
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..dcc1bd9688e18eccd067a2fb6a500b3a6bb70f3b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, int64_t sections);
+TORCH_API ::std::vector<at::Tensor> vsplit(const at::Tensor & self, at::IntArrayRef indices);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4da3526b4f0436aca6fe7a2f12f72a18cd7189a0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vsplit_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vsplit_int {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vsplit";
+  static constexpr const char* overload_name = "int";
+  static constexpr const char* schema_str = "vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, int64_t sections);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections);
+};
+
+struct TORCH_API vsplit_array {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &, at::IntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vsplit";
+  static constexpr const char* overload_name = "array";
+  static constexpr const char* schema_str = "vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & self, at::IntArrayRef indices);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vstack.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack.h
new file mode 100644
index 0000000000000000000000000000000000000000..61496b6d243ef298ae496b5780f3168946b86b06
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/vstack_ops.h>
+
+namespace at {
+
+
+// aten::vstack(Tensor[] tensors) -> Tensor
+inline at::Tensor vstack(at::TensorList tensors) {
+    return at::_ops::vstack::call(tensors);
+}
+
+// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vstack_out(at::Tensor & out, at::TensorList tensors) {
+    return at::_ops::vstack_out::call(tensors, out);
+}
+// aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & vstack_outf(at::TensorList tensors, at::Tensor & out) {
+    return at::_ops::vstack_out::call(tensors, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8f829aed908d8f009cc4c69672c9b621e80d7f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor vstack(at::TensorList tensors);
+TORCH_API at::Tensor & vstack_out(at::Tensor & out, at::TensorList tensors);
+TORCH_API at::Tensor & vstack_outf(at::TensorList tensors, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..59a1f20de5857ecfdc0340da07d712620aaa8556
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor vstack(at::TensorList tensors);
+TORCH_API at::Tensor & vstack_out(at::TensorList tensors, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d855c463656fa4e5c1e99ca9dc611b18dcad6a0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/vstack_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API vstack {
+  using schema = at::Tensor (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vstack";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "vstack(Tensor[] tensors) -> Tensor";
+  static at::Tensor call(at::TensorList tensors);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors);
+};
+
+struct TORCH_API vstack_out {
+  using schema = at::Tensor & (at::TensorList, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::vstack";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(at::TensorList tensors, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where.h
new file mode 100644
index 0000000000000000000000000000000000000000..0bbc6697ff486408313962051d508256bf53ee99
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where.h
@@ -0,0 +1,59 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/where_ops.h>
+
+namespace at {
+
+
+// aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::where_self::call(condition, self, other);
+}
+
+// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::where_self_out::call(condition, self, other, out);
+}
+// aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::where_self_out::call(condition, self, other, out);
+}
+
+// aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::where_ScalarSelf::call(condition, self, other);
+}
+
+// aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::where_ScalarOther::call(condition, self, other);
+}
+
+// aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor
+inline at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) {
+    return at::_ops::where_Scalar::call(condition, self, other);
+}
+
+// aten::where(Tensor condition) -> Tensor[]
+inline ::std::vector<at::Tensor> where(const at::Tensor & condition) {
+    return at::_ops::where::call(condition);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..36e00246c6e08e9b5bcf1365eadfb8eb971fa2bc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+TORCH_API ::std::vector<at::Tensor> where(const at::Tensor & condition);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f47c36a91f2d70d6fc347e2b9940a4ab754202a7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..204567ea097a53a4dcdcecad030420508afb11b7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_out(at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_outf(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7da092cb38236ee876d7d4fed50bc3f4cd358e96
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & where_self_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor NestedTensor_where(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & NestedTensor_where_out(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor where(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+TORCH_API ::std::vector<at::Tensor> where(const at::Tensor & condition);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/where_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/where_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..5b652e7d892218e34b1cf8c920153324843361be
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/where_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API where_self {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "self";
+  static constexpr const char* schema_str = "where.self(Tensor condition, Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API where_self_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "self_out";
+  static constexpr const char* schema_str = "where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API where_ScalarSelf {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "ScalarSelf";
+  static constexpr const char* schema_str = "where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API where_ScalarOther {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "ScalarOther";
+  static constexpr const char* schema_str = "where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API where_Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other);
+};
+
+struct TORCH_API where {
+  using schema = ::std::vector<at::Tensor> (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::where";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "where(Tensor condition) -> Tensor[]";
+  static ::std::vector<at::Tensor> call(const at::Tensor & condition);
+  static ::std::vector<at::Tensor> redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy.h
new file mode 100644
index 0000000000000000000000000000000000000000..e9e22a2d848a466946b04961df178bf1cd2334af
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy.h
@@ -0,0 +1,77 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/xlogy_ops.h>
+
+namespace at {
+
+
+// aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy_Tensor::call(self, other);
+}
+
+// aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor
+inline at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::xlogy_Scalar_Self::call(self, other);
+}
+
+// aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor
+inline at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy_Scalar_Other::call(self, other);
+}
+
+// aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)
+inline at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy__Tensor::call(self, other);
+}
+
+// aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)
+inline at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy__Scalar_Other::call(self, other);
+}
+
+// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::xlogy_OutTensor::call(self, other, out);
+}
+// aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutTensor::call(self, other, out);
+}
+
+// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other) {
+    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
+}
+// aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutScalar_Self::call(self, other, out);
+}
+
+// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
+}
+// aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out) {
+    return at::_ops::xlogy_OutScalar_Other::call(self, other, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2430dac31d52acedb6f05431b878d10248666252
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,29 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautogradnonfunctional_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..44c1228a1aeca10c2716814ea609f3fe87f53df6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9010f9c905d49bf2d3438b5968b1a0db8b7db231
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bd200e821a75af658957bb5614bb442b02302065
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..6c26bb439a81181218870c97045ed0a192076db1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/TensorIterator.h>
+#include <ATen/TensorMeta.h>
+#include <tuple>
+#include <vector>
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_xlogy_Tensor : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d7ac12b254998e5d772f0fd116055e160080f0fd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_meta_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..6fae59421c1f3596efe6e4d4cecb2a0c8226565f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+#include <ATen/ops/xlogy_meta.h>
+
+namespace at {
+namespace native {
+struct TORCH_API structured_xlogy_out : public at::meta::structured_xlogy_Tensor {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & xlogy_out(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & xlogy_out(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+TORCH_API at::Tensor & xlogy_(at::Tensor & self, const at::Scalar & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..5abc30b76c6c086b6c36ed0b9f3fbf685d9bc53a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xlogy_ops.h
@@ -0,0 +1,105 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API xlogy_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "xlogy.Tensor(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy_Scalar_Self {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Scalar_Self";
+  static constexpr const char* schema_str = "xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Scalar & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy_Scalar_Other {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "Scalar_Other";
+  static constexpr const char* schema_str = "xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API xlogy__Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy_";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API xlogy__Scalar_Other {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy_";
+  static constexpr const char* overload_name = "Scalar_Other";
+  static constexpr const char* schema_str = "xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API xlogy_OutTensor {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutTensor";
+  static constexpr const char* schema_str = "xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API xlogy_OutScalar_Self {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutScalar_Self";
+  static constexpr const char* schema_str = "xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+};
+
+struct TORCH_API xlogy_OutScalar_Other {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::xlogy";
+  static constexpr const char* overload_name = "OutScalar_Other";
+  static constexpr const char* schema_str = "xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xor.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xor.h
new file mode 100644
index 0000000000000000000000000000000000000000..92074e596901388761d0de5a1f78a39d82d2744f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xor.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/xor_ops.h>
+
+namespace at {
+
+
+// aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::__xor___Scalar::call(self, other);
+}
+
+// aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::__xor___Tensor::call(self, other);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xor_compositeimplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6aab74f0bb16a4ff25ff5e8b09ea910c660d47d9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xor_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..73289b3182d5aa422910188268e280c1af4c936f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_native.h
@@ -0,0 +1,24 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor __xor__(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & __ixor__(at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/xor_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..87fc6de36dfeba1fbb0ed54063d37a30fa758072
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/xor_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API __xor___Scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__xor__";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "__xor__.Scalar(Tensor self, Scalar other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Scalar & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API __xor___Tensor {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__xor__";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "__xor__.Tensor(Tensor self, Tensor other) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & other);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other);
+};
+
+struct TORCH_API __ixor___Scalar {
+  using schema = at::Tensor & (at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__ixor__";
+  static constexpr const char* overload_name = "Scalar";
+  static constexpr const char* schema_str = "__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Scalar & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other);
+};
+
+struct TORCH_API __ixor___Tensor {
+  using schema = at::Tensor & (at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::__ixor__";
+  static constexpr const char* overload_name = "Tensor";
+  static constexpr const char* schema_str = "__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self, const at::Tensor & other);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero.h
new file mode 100644
index 0000000000000000000000000000000000000000..54938c07317dd1a18690430c6cd529919c03571a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/zero_ops.h>
+
+namespace at {
+
+
+// aten::zero_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & zero_(at::Tensor & self) {
+    return at::_ops::zero_::call(self);
+}
+
+// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zero_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::zero_out::call(self, out);
+}
+// aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zero_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::zero_out::call(self, out);
+}
+
+// aten::zero(Tensor self) -> Tensor
+inline at::Tensor zero(const at::Tensor & self) {
+    return at::_ops::zero::call(self);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f8f134bf0482956e2a688bf7f9a4a2751afa241
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zero(const at::Tensor & self);
+TORCH_API at::Tensor & zero_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & zero_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cpu_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..533113c43f0fdae65703d1221af15b4e30aa8f57
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cuda_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..2c17107c2b5a20b11960c2643c70acbd04aef241
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_meta_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..99c49dbe1ae1cb8a9d8d8274ca2c1497784d2fa9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_meta_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+
+} // namespace meta
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac496a1e0cda39be848270d5dc325afe602e9f71
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_native.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zero(const at::Tensor & self);
+TORCH_API at::Tensor & zero_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & zero_(at::Tensor & self);
+TORCH_API at::Tensor & zero_meta_(at::Tensor & self);
+TORCH_API at::Tensor & zero_nested_(at::Tensor & self);
+TORCH_API at::Tensor & zero_sparse_(at::Tensor & self);
+TORCH_API at::Tensor & zero_sparse_csr_(at::Tensor & self);
+TORCH_API at::Tensor & mkldnn_zero_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zero_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b0c40ac89872d8650aaab8a5fc9116ba750b9ec8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zero_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zero_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero_";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zero_(Tensor(a!) self) -> Tensor(a!)";
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API zero_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+struct TORCH_API zero {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zero";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zero(Tensor self) -> Tensor";
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros.h
new file mode 100644
index 0000000000000000000000000000000000000000..f51c40565e19bde5041b8748da4dc96a633035f8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros.h
@@ -0,0 +1,131 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/zeros_ops.h>
+
+namespace at {
+
+
+// aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={}) {
+    return at::_ops::zeros_names::call(size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros_names::call(size, names, dtype, layout, device, pin_memory);
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros_symint(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor zeros(c10::SymIntArrayRef size, at::TensorOptions options={}) {
+    return at::_ops::zeros::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+  }
+}
+
+// aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor zeros(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory) {
+    return at::_ops::zeros::call(size, dtype, layout, device, pin_memory);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, int64_t>>>
+  at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(c10::fromIntArrayRefSlow(size), out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_symint_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::zeros_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & zeros_out(at::Tensor & out, c10::SymIntArrayRef size) {
+    return at::_ops::zeros_out::call(size, out);
+  }
+}
+
+// aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_symint_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(size, out);
+}
+namespace symint {
+  template <typename T, typename = std::enable_if_t<std::is_same_v<T, c10::SymInt>>>
+  at::Tensor & zeros_outf(c10::SymIntArrayRef size, at::Tensor & out) {
+    return at::_ops::zeros_out::call(size, out);
+  }
+}
+
+// aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names) {
+    return at::_ops::zeros_names_out::call(size, names, out);
+}
+// aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out) {
+    return at::_ops::zeros_names_out::call(size, names, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..88da4fff1364ed77f89cca67cdd95430a61a22e7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,34 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::TensorOptions options={});
+TORCH_API at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size, ::std::optional<at::DimnameList> names);
+TORCH_API at::Tensor & zeros_outf(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor zeros(at::IntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, at::TensorOptions options={});
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+TORCH_API at::Tensor & zeros_out(at::Tensor & out, at::IntArrayRef size);
+TORCH_API at::Tensor & zeros_outf(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & zeros_symint_out(at::Tensor & out, c10::SymIntArrayRef size);
+TORCH_API at::Tensor & zeros_symint_outf(c10::SymIntArrayRef size, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like.h
new file mode 100644
index 0000000000000000000000000000000000000000..32c44a0fbb8ce5bef9d6d6a9a0c47a6af7cb9a90
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like.h
@@ -0,0 +1,43 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include <ATen/Context.h>
+#include <ATen/DeviceGuard.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/TracerMode.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+
+
+
+#include <ATen/ops/zeros_like_ops.h>
+
+namespace at {
+
+
+// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::zeros_like::call(self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
+}
+// aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor
+inline at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format) {
+    return at::_ops::zeros_like::call(self, dtype, layout, device, pin_memory, memory_format);
+}
+
+// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt) {
+    return at::_ops::zeros_like_out::call(self, memory_format, out);
+}
+// aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & zeros_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out) {
+    return at::_ops::zeros_like_out::call(self, memory_format, out);
+}
+
+}
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeexplicitautograd_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..446a2ffd34ba4c7fbf956ff09dfccbfa4e589b34
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+TORCH_API at::Tensor & zeros_like_out(at::Tensor & out, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & zeros_like_outf(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeimplicitautogradnestedtensor_dispatch.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeimplicitautogradnestedtensor_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f97dace7c589e3aded9cff601c38ab0dc345e47
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_compositeimplicitautogradnestedtensor_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/Scalar.h>
+#include <ATen/core/Reduction.h>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+
+namespace compositeimplicitautogradnestedtensor {
+
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, at::TensorOptions options={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+
+} // namespace compositeimplicitautogradnestedtensor
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..eb0d1cd08b388cc473cccf67e0bfc50f0eec0cb0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zeros_like(const at::Tensor & self, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={}, ::std::optional<at::MemoryFormat> memory_format=::std::nullopt);
+TORCH_API at::Tensor & zeros_like_out(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..9148c508ffd2b1442a873e3665a2adc967035d5e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_like_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zeros_like {
+  using schema = at::Tensor (const at::Tensor &, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>, ::std::optional<at::MemoryFormat>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros_like";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor";
+  static at::Tensor call(const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory, ::std::optional<at::MemoryFormat> memory_format);
+};
+
+struct TORCH_API zeros_like_out {
+  using schema = at::Tensor & (const at::Tensor &, ::std::optional<at::MemoryFormat>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros_like";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, ::std::optional<at::MemoryFormat> memory_format, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_native.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ab1bad3edd461fc8406b92f3450c27aefe368b54
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_native.h
@@ -0,0 +1,25 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <optional>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor zeros(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & zeros_names_out(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+TORCH_API at::Tensor zeros_symint(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype={}, ::std::optional<at::Layout> layout={}, ::std::optional<at::Device> device={}, ::std::optional<bool> pin_memory={});
+TORCH_API at::Tensor & zeros_out(at::IntArrayRef size, at::Tensor & out);
+TORCH_API at::Tensor & zeros_sparse_out(at::IntArrayRef size, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_ops.h b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1de9f9a876df0df3af0db2d1f101656dfc5a5984
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/ops/zeros_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include <tuple>
+#include <vector>
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include <ATen/core/ATen_fwd.h>
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API zeros_names {
+  using schema = at::Tensor (at::IntArrayRef, ::std::optional<at::DimnameList>, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "names";
+  static constexpr const char* schema_str = "zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, ::std::optional<at::DimnameList> names, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API zeros {
+  using schema = at::Tensor (c10::SymIntArrayRef, ::std::optional<at::ScalarType>, ::std::optional<at::Layout>, ::std::optional<at::Device>, ::std::optional<bool>);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "";
+  static constexpr const char* schema_str = "zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor";
+  static at::Tensor call(c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, ::std::optional<at::ScalarType> dtype, ::std::optional<at::Layout> layout, ::std::optional<at::Device> device, ::std::optional<bool> pin_memory);
+};
+
+struct TORCH_API zeros_out {
+  using schema = at::Tensor & (c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "out";
+  static constexpr const char* schema_str = "zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API zeros_names_out {
+  using schema = at::Tensor & (at::IntArrayRef, ::std::optional<at::DimnameList>, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  static constexpr const char* name = "aten::zeros";
+  static constexpr const char* overload_name = "names_out";
+  static constexpr const char* schema_str = "zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!)";
+  static at::Tensor & call(at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, ::std::optional<at::DimnameList> names, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/lib/python3.11/site-packages/torch/include/ATen/quantized/QTensorImpl.h b/lib/python3.11/site-packages/torch/include/ATen/quantized/QTensorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..127fa78de12d16fadf15ec9971f5f77112ffe580
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/quantized/QTensorImpl.h
@@ -0,0 +1,125 @@
+#pragma once
+
+#include <ATen/quantized/Quantizer.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/util/Exception.h>
+
+namespace at {
+
+/**
+ * QTensorImpl is a TensorImpl for Quantized Tensors, it stores Quantizer which
+ * specifies the quantization scheme and parameters, for more information please
+ * see ATen/quantized/Quantizer.h
+ *
+ * We'll use QTensor in code or documentation to refer to a Tensor with QTensorImpl.
+ */
+struct TORCH_API QTensorImpl : public c10::TensorImpl {
+ public:
+  QTensorImpl(
+      Storage&& storage,
+      DispatchKeySet key_set,
+      const caffe2::TypeMeta data_type,
+      QuantizerPtr quantizer);
+
+  // See Note [Enum ImplType]
+  QTensorImpl(
+      ImplType type,
+      Storage&& storage,
+      DispatchKeySet key_set,
+      const caffe2::TypeMeta data_type,
+      QuantizerPtr quantizer);
+
+
+  // TODO: Expose in PyTorch Frontend
+  QuantizerPtr quantizer() {
+    return quantizer_;
+  }
+
+  void set_quantizer_(QuantizerPtr quantizer) {
+    quantizer_ = quantizer;
+  }
+
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change) const override {
+    auto impl = c10::make_intrusive<QTensorImpl>(
+        Storage(storage()), key_set(), data_type_, quantizer_);
+    copy_tensor_metadata(
+      /*src_impl=*/this,
+      /*dest_impl=*/impl.get(),
+      /*version_counter=*/version_counter,
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
+    impl->refresh_numel();
+    impl->refresh_contiguous();
+    return impl;
+  }
+
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      c10::VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change) const override {
+    auto impl = c10::make_intrusive<QTensorImpl>(
+        Storage(storage()), key_set(), data_type_, quantizer_);
+    copy_tensor_metadata(
+      /*src_impl=*/this,
+      /*dest_impl=*/impl.get(),
+      /*version_counter=*/std::move(version_counter),
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
+    impl->refresh_numel();
+    impl->refresh_contiguous();
+    return impl;
+  }
+
+  /**
+   * Shallow-copies data from another TensorImpl into this TensorImpl.
+   *
+   * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override {
+    AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set()));
+    auto q_impl = static_cast<const QTensorImpl*>(impl.get());
+    copy_tensor_metadata(
+      /*src_impl=*/q_impl,
+      /*dest_impl=*/this,
+      /*version_counter=*/version_counter(),
+      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
+    refresh_numel();
+    refresh_contiguous();
+  }
+
+ private:
+  QuantizerPtr quantizer_;
+
+  const char* tensorimpl_type_name() const override;
+
+  /**
+   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset)
+   * from one TensorImpl to another TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  static void copy_tensor_metadata(
+      const QTensorImpl* src_q_impl,
+      QTensorImpl* dest_q_impl,
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change) {
+    TensorImpl::copy_tensor_metadata(src_q_impl, dest_q_impl, version_counter, allow_tensor_metadata_change);
+
+    // OpaqueTensorImpl-specific fields.
+    dest_q_impl->quantizer_ = src_q_impl->quantizer_;
+  }
+};
+
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/quantized/Quantizer.h b/lib/python3.11/site-packages/torch/include/ATen/quantized/Quantizer.h
new file mode 100644
index 0000000000000000000000000000000000000000..709e073d064b4d715c973172f778cb9b37d90515
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/quantized/Quantizer.h
@@ -0,0 +1,279 @@
+#pragma once
+
+#include <c10/core/QScheme.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/TensorOptions.h>
+
+#include <ATen/Tensor.h>
+#include <ATen/TensorUtils.h>
+
+#include <ATen/core/QuantizerBase.h>
+
+#include <cmath>
+#include <memory>
+#include <utility>
+
+namespace at {
+
+/**
+ * UnknownQuantizer is a placeholder quantizer for functions that implement
+ * quantization in a two step process.  First a tensor is allocated but with
+ * unknown quantizer, and then the quantization kernel decides what the final
+ * quantizer will be.
+ */
+struct TORCH_API UnknownQuantizer : public Quantizer {
+  explicit UnknownQuantizer(ScalarType scalar_type)
+    : Quantizer(scalar_type) {}
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+  QScheme qscheme() const override;
+  bool equalTo(QuantizerPtr other) const override;
+};
+
+/**
+ * UniformQuantizer is the parent class for all uniform quantizers.
+ * These quantization scheme will map float value uniformly to
+ * the quantized value. For example, affine quantizer is
+ * the most commonly used scheme in this category.
+ */
+struct TORCH_API UniformQuantizer : public Quantizer {
+  explicit UniformQuantizer(ScalarType scalar_type) : Quantizer(scalar_type) {}
+};
+
+/**
+ * NonUniformQuantizer is the parent class for all non-uniform quantizers.
+ * These quantization scheme may map float value non-uniformly to the quantized
+ * value. K-means quantization is a representative example in this category.
+ */
+struct TORCH_API NonUniformQuantizer : public Quantizer {
+  explicit NonUniformQuantizer(ScalarType scalar_type) : Quantizer(scalar_type) {}
+};
+
+// There is also StochasticQuantizer which is uniform but not affine
+
+/**
+ * AffineQuantizer uses affine transformation to do quantization.
+ *
+ * For quantize:
+ * Y = clamp(round(X / scale + zero_point), min, max)
+ * For dequantize:
+ * X = (Y - zero_point) * scale
+ */
+struct TORCH_API AffineQuantizer : public UniformQuantizer {
+  explicit AffineQuantizer(ScalarType scalar_type) : UniformQuantizer(scalar_type) {}
+};
+
+// Note that we will not have Symmetric Quantizer in backend to reduce
+// complications in quantized kernel implementation.
+
+/**
+ * PerTensorAffineQuantizer stores a scale and a zero_point, which is used for
+ * all the values in the Tensor.
+ */
+struct TORCH_API PerTensorAffineQuantizer : public AffineQuantizer {
+  explicit PerTensorAffineQuantizer(ScalarType scalar_type, double scale, int64_t zero_point)
+    : AffineQuantizer(scalar_type),
+        scale_(scale),
+        zero_point_(zero_point) {}
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  QScheme qscheme() const override {
+    return kPerTensorAffine;
+  }
+
+  double scale() const {
+    return scale_;
+  }
+
+  int64_t zero_point() const {
+    return zero_point_;
+  }
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerTensorAffine) {
+      return false;
+    }
+    auto* other_per_tensor_affine =
+        static_cast<PerTensorAffineQuantizer*>(other.get());
+    return scalar_type() == other_per_tensor_affine->scalar_type() &&
+        scale() == other_per_tensor_affine->scale() &&
+        zero_point() == other_per_tensor_affine->zero_point();
+  }
+
+ private:
+  const double scale_;
+  // We use int64_t for consistency with Python
+  const int64_t zero_point_;
+};
+
+/**
+ * PerChannelAffineQuantizer is the same as PerTensorAffineQuantizer
+ * except that we have an independent scale and zero_point parameter
+ * for each channel.
+ *
+ * Also note that per channel quantization is mostly applied to output channels
+ * of weights since per-input channel of weight quantization or per-channel
+ * quantization for activations can't be efficiently supported in most of
+ * processors since it requires each multiplication result within a single
+ * dot-product to have a different scale.
+ */
+struct TORCH_API PerChannelAffineQuantizer : public AffineQuantizer {
+  explicit PerChannelAffineQuantizer(
+      ScalarType scalar_type,
+      Tensor scales,
+      Tensor zero_points,
+      int64_t axis)
+      : AffineQuantizer(scalar_type),
+        scales_(std::move(scales)),
+        zero_points_(std::move(zero_points)),
+        axis_(axis) {}
+
+  QScheme qscheme() const override {
+    return kPerChannelAffine;
+  }
+
+  Tensor scales() const {
+    return scales_;
+  }
+
+  Tensor zero_points() const {
+    return zero_points_;
+  }
+
+  int64_t axis() const {
+    return axis_;
+  }
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerChannelAffine) {
+      return false;
+    }
+    auto* other_per_channel_affine =
+        static_cast<PerChannelAffineQuantizer*>(other.get());
+    return scalar_type() == other_per_channel_affine->scalar_type() &&
+        scales().equal(other_per_channel_affine->scales()) &&
+        zero_points().equal(other_per_channel_affine->zero_points()) &&
+        axis() == other_per_channel_affine->axis();
+  }
+
+ protected:
+  Tensor scales_;
+  Tensor zero_points_;
+  const int64_t axis_;
+};
+
+/**
+ * PerChannelAffineFloatQParamsQuantizer is the same as PerChannelAffineQuantizer
+ * except that it expects both scale and zero point to be floating point values.
+ *
+ * This quantizer uses the kPerChannelAffineFloatQParams qscheme which is a variant of
+ * kPerChannelAffine.
+ *
+ * The quantize equation in this case looks like -
+ * Xq = (Xf - zero_point) * inv_scale, where inv_scale = 1.0/scale
+ *
+ * Note: Usage of floating point zero point is useful in cases where 0 doesn't need to
+ * be exactly represented in the quantized space. We can get additional precision by
+ * using floating point values for zero point.
+ */
+struct TORCH_API PerChannelAffineFloatQParamsQuantizer : public PerChannelAffineQuantizer {
+  explicit PerChannelAffineFloatQParamsQuantizer(
+      ScalarType scalar_type,
+      Tensor scales,
+      Tensor zero_points,
+      int64_t axis)
+      : PerChannelAffineQuantizer(scalar_type,
+        scales,
+        zero_points,
+        axis) {}
+
+  QScheme qscheme() const override {
+    return kPerChannelAffineFloatQParams;
+  }
+
+  Tensor quantize(const Tensor& tensor) override;
+  Tensor dequantize(const Tensor& qtensor) override;
+  Tensor& dequantize_out(Tensor& rtensor, const Tensor& qtensor) override;
+
+  bool equalTo(QuantizerPtr other) const override {
+    if (!other.get() || other->qscheme() != kPerChannelAffineFloatQParams) {
+      return false;
+    }
+    auto* other_per_channel_float_qparams =
+        static_cast<PerChannelAffineFloatQParamsQuantizer*>(other.get());
+    return scalar_type() == other_per_channel_float_qparams->scalar_type() &&
+        scales().equal(other_per_channel_float_qparams->scales()) &&
+        zero_points().equal(other_per_channel_float_qparams->zero_points()) &&
+        axis() == other_per_channel_float_qparams->axis();
+  }
+};
+
+// This is an internal utility function for getting at the QTensorImpl,
+// You should only use this for writing low level
+// setters/getters for QTensorImpl fields; otherwise, you should use
+// the low level setters/getters that were implemented using this.
+// This may be called repeatedly, so make sure it's pretty cheap.
+TORCH_API QTensorImpl* get_qtensorimpl(const TensorBase& self);
+
+// double and int64_t are because of the native function API, we only have these
+// argument types right now in native functions
+TORCH_API QuantizerPtr
+make_per_tensor_affine_quantizer(
+    double scale, int64_t zero_point, ScalarType scalar_type);
+
+TORCH_API QuantizerPtr make_per_channel_affine_quantizer(
+    const Tensor& scales,
+    const Tensor& zero_points,
+    int64_t axis,
+    ScalarType scalar_type);
+
+TORCH_API QuantizerPtr make_unknown_quantizer(ScalarType scalar_type);
+
+// Create a Quantized Tensor given arguments for normal Tensor and a quantizer
+TORCH_API Tensor new_qtensor(
+    IntArrayRef sizes,
+    const TensorOptions& options,
+    QuantizerPtr quantizer);
+
+TORCH_API void set_quantizer_(const Tensor& self, ConstQuantizerPtr quantizer);
+
+TORCH_API Tensor from_blob_quantized_per_tensor_affine(
+    void* data,
+    IntArrayRef sizes,
+    IntArrayRef strides,
+    std::function<void(void*)> deleter,
+    const float scale,
+    const int64_t zeroPoint,
+    const TensorOptions& options);
+
+TORCH_API Tensor from_blob_quantized_per_tensor_affine(
+    void* data,
+    IntArrayRef sizes,
+    std::function<void(void*)> deleter,
+    const float scale,
+    const int64_t zeroPoint,
+    const TensorOptions& options);
+
+TORCH_API Tensor from_blob_quantized_per_channel_affine(
+    void* data,
+    IntArrayRef sizes,
+    std::function<void(void*)> deleter,
+    const Tensor& scales,
+    const Tensor& zero_points,
+    const int64_t axis,
+    const TensorOptions& options);
+
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/CachingHostAllocator.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/CachingHostAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a4afc0dc7486ac43c5191ec9e244dfef4a3b3a4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/CachingHostAllocator.h
@@ -0,0 +1,23 @@
+#pragma once
+
+#include <ATen/core/CachingHostAllocator.h>
+#include <ATen/xpu/XPUEvent.h>
+#include <c10/core/Allocator.h>
+#include <c10/xpu/XPUStream.h>
+
+namespace at::xpu {
+
+TORCH_XPU_API c10::Allocator* getCachingHostAllocator();
+
+TORCH_XPU_API bool CachingHostAllocator_recordEvent(
+    void* ptr,
+    void* ctx,
+    c10::xpu::XPUStream stream);
+
+TORCH_XPU_API void CachingHostAllocator_emptyCache();
+
+inline TORCH_XPU_API at::DataPtr HostAlloc(size_t size) {
+  return getCachingHostAllocator()->allocate(size);
+}
+
+} // namespace at::xpu
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/PinnedMemoryAllocator.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/PinnedMemoryAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..4a209c95524917cbb0cfd165aa061726dde03839
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/PinnedMemoryAllocator.h
@@ -0,0 +1,11 @@
+#pragma once
+
+#include <ATen/xpu/CachingHostAllocator.h>
+#include <c10/core/Allocator.h>
+
+namespace at::xpu {
+
+inline TORCH_XPU_API at::Allocator* getPinnedMemoryAllocator() {
+  return getCachingHostAllocator();
+}
+} // namespace at::xpu
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUContext.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUContext.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb8fbe9c0aa4221a3384d6eb7c457d8dad54d0f0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUContext.h
@@ -0,0 +1,20 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <c10/xpu/XPUFunctions.h>
+#include <c10/xpu/XPUStream.h>
+
+namespace at::xpu {
+
+// XPU is available if we compiled with XPU.
+inline bool is_available() {
+  return c10::xpu::device_count() > 0;
+}
+
+TORCH_XPU_API DeviceProp* getCurrentDeviceProperties();
+
+TORCH_XPU_API DeviceProp* getDeviceProperties(DeviceIndex device);
+
+TORCH_XPU_API int32_t getGlobalIdxFromDevice(DeviceIndex device);
+
+} // namespace at::xpu
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUDevice.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUDevice.h
new file mode 100644
index 0000000000000000000000000000000000000000..d4ab7187513c15ad0bbc8cff610ec3831f3a51fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUDevice.h
@@ -0,0 +1,13 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <c10/xpu/XPUFunctions.h>
+
+namespace at::xpu {
+
+inline Device getDeviceFromPtr(void* ptr) {
+  auto device = c10::xpu::get_device_idx_from_pointer(ptr);
+  return {c10::DeviceType::XPU, device};
+}
+
+} // namespace at::xpu
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUEvent.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUEvent.h
new file mode 100644
index 0000000000000000000000000000000000000000..ededd6ebf4f15b0c132743b0125cfd0c7c95e421
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUEvent.h
@@ -0,0 +1,191 @@
+#pragma once
+#include <ATen/xpu/XPUContext.h>
+
+#include <optional>
+
+namespace at::xpu {
+
+/*
+ * XPUEvent are movable not copyable wrappers around SYCL event. XPUEvent are
+ * constructed lazily when first recorded. It has a device, and this device is
+ * acquired from the first recording stream. Later streams that record the event
+ * must match the same device.
+ *
+ * Currently, XPUEvent does NOT support to export an inter-process event from
+ * another process via inter-process comunication(IPC). So it means that
+ * inter-process communication for event handles between different processes is
+ * not available. This could impact some applications that rely on cross-process
+ * synchronization and communication.
+ */
+struct TORCH_XPU_API XPUEvent {
+  // Constructors
+  XPUEvent(bool enable_timing = false) noexcept
+      : enable_timing_{enable_timing} {}
+
+  ~XPUEvent() {
+    if (isCreated()) {
+      const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+      if (C10_UNLIKELY(interp)) {
+        (*interp)->trace_gpu_event_deletion(
+            at::kXPU, reinterpret_cast<uintptr_t>(event_.get()));
+      }
+    }
+  }
+
+  XPUEvent(const XPUEvent&) = delete;
+  XPUEvent& operator=(const XPUEvent&) = delete;
+
+  XPUEvent(XPUEvent&& other) = default;
+  XPUEvent& operator=(XPUEvent&& other) = default;
+
+  operator sycl::event&() const {
+    return event();
+  }
+
+  std::optional<at::Device> device() const {
+    if (isCreated()) {
+      return at::Device(at::kXPU, device_index_);
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  inline bool isCreated() const {
+    return (event_.get() != nullptr);
+  }
+
+  DeviceIndex device_index() const {
+    return device_index_;
+  }
+
+  sycl::event& event() const {
+    return *event_;
+  }
+
+  bool query() const {
+    using namespace sycl::info;
+    if (!isCreated()) {
+      return true;
+    }
+
+    return event().get_info<event::command_execution_status>() ==
+        event_command_status::complete;
+  }
+
+  void record() {
+    record(getCurrentXPUStream());
+  }
+
+  void recordOnce(const XPUStream& stream) {
+    if (!isCreated()) {
+      record(stream);
+    }
+  }
+
+  void record(const XPUStream& stream) {
+    if (!isCreated()) {
+      device_index_ = stream.device_index();
+      assignEvent(stream.queue());
+      const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+      if (C10_UNLIKELY(interp)) {
+        (*interp)->trace_gpu_event_creation(
+            at::kXPU, reinterpret_cast<uintptr_t>(event_.get()));
+      }
+    } else {
+      TORCH_CHECK(
+          device_index_ == stream.device_index(),
+          "Event device ",
+          device_index_,
+          " does not match recording stream's device ",
+          stream.device_index(),
+          ".");
+      reassignEvent(stream.queue());
+    }
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_record(
+          at::kXPU,
+          reinterpret_cast<uintptr_t>(event_.get()),
+          reinterpret_cast<uintptr_t>(&stream.queue()));
+    }
+  }
+
+  void block(const XPUStream& stream) {
+    if (isCreated()) {
+      std::vector<sycl::event> event_list{event()};
+      // Make this stream wait until event_ is completed.
+      stream.queue().ext_oneapi_submit_barrier(event_list);
+      const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+      if (C10_UNLIKELY(interp)) {
+        (*interp)->trace_gpu_event_wait(
+            at::kXPU,
+            reinterpret_cast<uintptr_t>(event_.get()),
+            reinterpret_cast<uintptr_t>(&stream.queue()));
+      }
+    }
+  }
+
+  double elapsed_time(const XPUEvent& other) const {
+    TORCH_CHECK(
+        isCreated() && other.isCreated(),
+        "Both events must be recorded before calculating elapsed time.");
+    TORCH_CHECK(
+        query() && other.query(),
+        "Both events must be completed before calculating elapsed time.");
+    TORCH_CHECK(
+        enable_timing_ && other.enable_timing_,
+        "Both events must be created with argument 'enable_timing=True'.");
+
+#if SYCL_COMPILER_VERSION < 20250000
+    TORCH_CHECK_NOT_IMPLEMENTED(
+        false,
+        "elapsed_time of XPUEvent requires PyTorch to be built with SYCL compiler version 2025.0.0 or newer.");
+#endif
+
+    using namespace sycl::info::event_profiling;
+    // Block until both of the recorded events are completed.
+    uint64_t end_time_ns = other.event().get_profiling_info<command_end>();
+    uint64_t start_time_ns = event().get_profiling_info<command_end>();
+    // Return the eplased time in milliseconds.
+    return 1e-6 *
+        (static_cast<double>(end_time_ns) - static_cast<double>(start_time_ns));
+  }
+
+  void synchronize() const {
+    if (isCreated()) {
+      const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+      if (C10_UNLIKELY(interp)) {
+        (*interp)->trace_gpu_event_synchronization(
+            at::kXPU, reinterpret_cast<uintptr_t>(event_.get()));
+      }
+      event().wait_and_throw();
+    }
+  }
+
+ private:
+  void assignEvent(sycl::queue& queue) {
+#if SYCL_COMPILER_VERSION >= 20250000
+    if (enable_timing_) {
+      event_ = std::make_unique<sycl::event>(
+          sycl::ext::oneapi::experimental::submit_profiling_tag(queue));
+    } else {
+      event_ = std::make_unique<sycl::event>(queue.ext_oneapi_submit_barrier());
+    }
+#else
+    event_ = std::make_unique<sycl::event>(queue.ext_oneapi_submit_barrier());
+#endif
+  }
+
+  void reassignEvent(sycl::queue& queue) {
+    event_.reset();
+    assignEvent(queue);
+  }
+
+  bool enable_timing_ = false;
+  DeviceIndex device_index_ = -1;
+  // Only need to track the last event, as events in an in-order queue are
+  // executed sequentially.
+  std::unique_ptr<sycl::event> event_;
+};
+
+} // namespace at::xpu
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUGeneratorImpl.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUGeneratorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..a1f264382a366d70410adc7c98280694b63d6ba5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/XPUGeneratorImpl.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#include <ATen/core/Generator.h>
+
+namespace at {
+
+struct TORCH_XPU_API XPUGeneratorImpl : public GeneratorImpl {
+  // Constructors
+  XPUGeneratorImpl(DeviceIndex device_index = -1);
+  ~XPUGeneratorImpl() override = default;
+
+  // XPUGeneratorImpl methods
+  std::shared_ptr<XPUGeneratorImpl> clone() const;
+  void set_current_seed(uint64_t seed) override;
+  void set_offset(uint64_t offset) override;
+  uint64_t get_offset() const override;
+  uint64_t current_seed() const override;
+  uint64_t seed() override;
+  void set_state(const c10::TensorImpl& new_state) override;
+  c10::intrusive_ptr<c10::TensorImpl> get_state() const override;
+  void set_philox_offset_per_thread(uint64_t offset);
+  uint64_t philox_offset_per_thread() const;
+  std::pair<uint64_t, uint64_t> philox_engine_inputs(uint64_t increment);
+  static c10::DeviceType device_type();
+
+ private:
+  XPUGeneratorImpl* clone_impl() const override;
+  uint64_t seed_ = default_rng_seed_val;
+  uint64_t philox_offset_per_thread_ = 0;
+};
+
+namespace xpu::detail {
+
+TORCH_XPU_API const Generator& getDefaultXPUGenerator(DeviceIndex device = -1);
+
+TORCH_XPU_API Generator createXPUGenerator(DeviceIndex device = -1);
+
+} // namespace xpu::detail
+} // namespace at
diff --git a/lib/python3.11/site-packages/torch/include/ATen/xpu/detail/XPUHooks.h b/lib/python3.11/site-packages/torch/include/ATen/xpu/detail/XPUHooks.h
new file mode 100644
index 0000000000000000000000000000000000000000..4cc25e1fe8405fe7df061345411db4ac4c7db53d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/ATen/xpu/detail/XPUHooks.h
@@ -0,0 +1,28 @@
+#pragma once
+
+#include <ATen/detail/XPUHooksInterface.h>
+
+namespace at::xpu::detail {
+
+// The real implementation of XPUHooksInterface
+struct XPUHooks : public at::XPUHooksInterface {
+  XPUHooks(at::XPUHooksArgs) {}
+  void init() const override;
+  bool hasXPU() const override;
+  std::string showConfig() const override;
+  int32_t getGlobalIdxFromDevice(const at::Device& device) const override;
+  const Generator& getDefaultGenerator(
+      DeviceIndex device_index = -1) const override;
+  Generator getNewGenerator(DeviceIndex device_index = -1) const override;
+  Device getDeviceFromPtr(void* data) const override;
+  c10::DeviceIndex getNumGPUs() const override;
+  DeviceIndex current_device() const override;
+  void deviceSynchronize(DeviceIndex device_index) const override;
+  Allocator* getPinnedMemoryAllocator() const override;
+  bool isPinnedPtr(const void* data) const override;
+  bool hasPrimaryContext(DeviceIndex device_index) const override;
+  DeviceIndex deviceCount() const override;
+  DeviceIndex getCurrentDevice() const override;
+};
+
+} // namespace at::xpu::detail
diff --git a/lib/python3.11/site-packages/torch/include/THC/THCAtomics.cuh b/lib/python3.11/site-packages/torch/include/THC/THCAtomics.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..e4b1dc5f8da32e759c4df8c080b8e7630ca7c4c8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/THC/THCAtomics.cuh
@@ -0,0 +1,3 @@
+#pragma once
+// TODO: Remove once torchvision has been updated to use the ATen header
+#include <ATen/cuda/Atomic.cuh>
diff --git a/lib/python3.11/site-packages/torch/include/THC/THCDeviceUtils.cuh b/lib/python3.11/site-packages/torch/include/THC/THCDeviceUtils.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..03294d0c846ed085e973ba7ae7a54d08dee1963b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/THC/THCDeviceUtils.cuh
@@ -0,0 +1,3 @@
+#pragma once
+// TODO: Remove this header
+#include <ATen/cuda/DeviceUtils.cuh>
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Allocator.h b/lib/python3.11/site-packages/torch/include/c10/core/Allocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..c881d104934b441ba855bef2cf2cc804397c9e5b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Allocator.h
@@ -0,0 +1,332 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <memory>
+#include <utility>
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/ThreadLocalDebugInfo.h>
+#include <c10/util/UniqueVoidPtr.h>
+
+namespace c10 {
+
+// A DataPtr is a unique pointer (with an attached deleter and some
+// context for the deleter) to some memory, which also records what
+// device is for its data.
+//
+// nullptr DataPtrs can still have a nontrivial device; this allows
+// us to treat zero-size allocations uniformly with non-zero allocations.
+//
+class C10_API DataPtr {
+ private:
+  c10::detail::UniqueVoidPtr ptr_;
+  Device device_;
+
+ public:
+  // Choice of CPU here is arbitrary; if there's an "undefined" device
+  // we could use that too
+  DataPtr() : ptr_(), device_(DeviceType::CPU) {}
+  DataPtr(void* data, Device device) : ptr_(data), device_(device) {}
+  DataPtr(void* data, void* ctx, DeleterFnPtr ctx_deleter, Device device)
+      : ptr_(data, ctx, ctx_deleter), device_(device) {}
+  void* operator->() const {
+    return ptr_.get();
+  }
+  void clear() {
+    ptr_.clear();
+  }
+  void* get() const {
+    return ptr_.get();
+  }
+  void* mutable_get() {
+    return ptr_.get();
+  }
+  void* get_context() const {
+    return ptr_.get_context();
+  }
+  void* release_context() {
+    return ptr_.release_context();
+  }
+  std::unique_ptr<void, DeleterFnPtr>&& move_context() {
+    return ptr_.move_context();
+  }
+  operator bool() const {
+    return static_cast<bool>(ptr_);
+  }
+  template <typename T>
+  T* cast_context(DeleterFnPtr expected_deleter) const {
+    return ptr_.cast_context<T>(expected_deleter);
+  }
+  DeleterFnPtr get_deleter() const {
+    return ptr_.get_deleter();
+  }
+  /**
+   * Compare the deleter in a DataPtr to expected_deleter.
+   * If it matches, replace the deleter with new_deleter
+   * and return true; otherwise, does nothing and returns
+   * false.
+   *
+   * In general, it is not safe to unconditionally set the
+   * deleter on a DataPtr, because you don't know what
+   * the deleter is, and thus will have a hard time properly
+   * disposing of the deleter without storing the original
+   * deleter (this is difficult to do, because DeleterFnPtr
+   * is not a closure, and because the context on DataPtr is
+   * only a single word, you generally don't have enough
+   * space to store both the original deleter and its context).
+   * However, in some cases, you know /exactly/ what the deleter
+   * is, and you have a new deleter that manually wraps
+   * the old one.  In this case, you can safely swap the deleter
+   * after asserting that the deleters line up.
+   *
+   * What are the requirements on new_deleter?  It must still
+   * properly dispose of the void* pointer passed in as its argument,
+   * where void* is whatever the context of the original deleter
+   * is.  So in general, you expect the new deleter to look something
+   * like this:
+   *
+   *      [](void* ptr) {
+   *        some_new_stuff(ptr);
+   *        get_orig_allocator()->raw_deleter(ptr);
+   *      }
+   *
+   * Note that it won't work to close over the original
+   * allocator; you don't have enough space to do that!  Also,
+   * it's unsafe to assume that the passed in pointer in
+   * question is the memory pointer in question; it might not
+   * be; be sure to read the source code of the Allocator
+   * in question to confirm this.
+   */
+  [[nodiscard]] bool compare_exchange_deleter(
+      DeleterFnPtr expected_deleter,
+      DeleterFnPtr new_deleter) {
+    return ptr_.compare_exchange_deleter(expected_deleter, new_deleter);
+  }
+  Device device() const {
+    return device_;
+  }
+  // Unsafely mutates the device on a DataPtr.  Under normal use,
+  // you should never actually need to call this function.
+  // We need this for the implementation of the hack detailed
+  // in Note [Masquerading as CUDA]
+  void unsafe_set_device(Device device) {
+    device_ = device;
+  }
+};
+
+// NB: Device is NOT tested for here; a CUDA nullptr is as much a nullptr as a
+// CPU nullptr
+
+inline bool operator==(const DataPtr& dp, std::nullptr_t) noexcept {
+  return !dp;
+}
+inline bool operator==(std::nullptr_t, const DataPtr& dp) noexcept {
+  return !dp;
+}
+inline bool operator!=(const DataPtr& dp, std::nullptr_t) noexcept {
+  return dp;
+}
+inline bool operator!=(std::nullptr_t, const DataPtr& dp) noexcept {
+  return dp;
+}
+
+// Note [raw_allocate/raw_deallocate and Thrust]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Thrust's support for custom allocators requires us to write something
+// like this:
+//
+//  class ThrustAllocator {
+//    char* allocate(size_t);
+//    void deallocate(char*, size_t);
+//  };
+//
+// This is not good for our unique_ptr based allocator interface, as
+// there is no way to get to the context when we free.
+//
+// However, in some cases the context is exactly the same as
+// the data pointer.  In this case, we can support the "raw"
+// allocate and deallocate interface.  This is what
+// raw_deleter signifies.  By default, it returns a nullptr, which means that
+// the raw interface is not implemented.  Be sure to implement it whenever
+// possible, or the raw interface will incorrectly reported as unsupported,
+// when it is actually possible.
+
+// NOLINTNEXTLINE(cppcoreguidelines-special-member-functions)
+struct C10_API Allocator {
+  virtual ~Allocator() = default;
+
+  virtual DataPtr allocate(size_t n) = 0;
+
+  // Clones an allocation that came from this allocator.
+  //
+  // To perform the copy, this function calls `copy_data`, which
+  // must be implemented by derived classes.
+  //
+  // Note that this explicitly ignores any context that may have been
+  // attached to the input data.
+  //
+  // Requires: input data was allocated by the same allocator.
+  DataPtr clone(const void* data, std::size_t n);
+
+  // Checks if DataPtr has a simple context, not wrapped with any out of the
+  // ordinary contexts.
+  virtual bool is_simple_data_ptr(const DataPtr& data_ptr) const;
+
+  // If this returns a non nullptr, it means that allocate()
+  // is guaranteed to return a unique_ptr with this deleter attached;
+  // it means the rawAllocate and rawDeallocate APIs are safe to use.
+  // This function MUST always return the same BoundDeleter.
+  virtual DeleterFnPtr raw_deleter() const {
+    return nullptr;
+  }
+  void* raw_allocate(size_t n) {
+    auto dptr = allocate(n);
+    AT_ASSERT(dptr.get() == dptr.get_context());
+    return dptr.release_context();
+  }
+  void raw_deallocate(void* ptr) {
+    auto d = raw_deleter();
+    AT_ASSERT(d);
+    d(ptr);
+  }
+
+  // Copies data from one allocation to another.
+  // Pure virtual, so derived classes must define behavior.
+  // Derived class implementation can simply call `default_copy_data`
+  // to use `std::memcpy`.
+  //
+  // Requires: src and dest were allocated by this allocator
+  // Requires: src and dest both have length >= count
+  virtual void copy_data(void* dest, const void* src, std::size_t count)
+      const = 0;
+
+ protected:
+  // Uses `std::memcpy` to copy data.
+  // Child classes can use this as `copy_data` when an alternative copy
+  // API is not needed.
+  void default_copy_data(void* dest, const void* src, std::size_t count) const;
+};
+
+// This context is used to generate DataPtr which have arbitrary
+// std::function deleters associated with them.  In some user facing
+// functions, we give a (user-friendly) interface for constructing
+// tensors from external data which take an arbitrary std::function
+// deleter.  Grep for InefficientStdFunctionContext to find these
+// occurrences.
+//
+// This context is inefficient because we have to do a dynamic
+// allocation InefficientStdFunctionContext, on top of the dynamic
+// allocation which is implied by std::function itself.
+struct C10_API InefficientStdFunctionContext {
+  void* ptr_{nullptr};
+  std::function<void(void*)> deleter_;
+  InefficientStdFunctionContext(void* ptr, std::function<void(void*)> deleter)
+      : ptr_(ptr), deleter_(std::move(deleter)) {}
+  InefficientStdFunctionContext(const InefficientStdFunctionContext&) = delete;
+  InefficientStdFunctionContext(InefficientStdFunctionContext&& rhs) noexcept
+      : ptr_(std::exchange(rhs.ptr_, nullptr)),
+        deleter_(std::move(rhs.deleter_)) {}
+  InefficientStdFunctionContext& operator=(
+      const InefficientStdFunctionContext&) = delete;
+  // NOLINTNEXTLINE(*-noexcept-move-*)
+  InefficientStdFunctionContext& operator=(
+      InefficientStdFunctionContext&& rhs) {
+    this->~InefficientStdFunctionContext();
+    ptr_ = std::exchange(rhs.ptr_, nullptr);
+    deleter_ = std::move(rhs.deleter_);
+    return *this;
+  }
+  ~InefficientStdFunctionContext() {
+    if (deleter_) {
+      deleter_(ptr_);
+    }
+  }
+  static DataPtr makeDataPtr(
+      void* ptr,
+      std::function<void(void*)> deleter,
+      Device device);
+};
+
+/** Set the allocator for DeviceType `t`. The passed in allocator pointer is
+ *  expected to have static lifetime; this function does NOT take ownership
+ *  of the raw pointer. (The reason for this is to prevent existing pointers
+ *  to an allocator of a particular device from being invalidated when
+ *  SetAllocator is called.)
+ *
+ *  Also note that this is not thread-safe, and we assume this function will
+ *  only be called during initialization.
+ *
+ *  The 'priority' flag is introduced when we want to overwrite the default
+ *  allocator, since the allocators are set statically. The default priority
+ *  is 0, which means the lowest. Only higher or equal priority can overwrite
+ *  existing ones.
+ */
+C10_API void SetAllocator(DeviceType t, Allocator* alloc, uint8_t priority = 0);
+C10_API Allocator* GetAllocator(const DeviceType& t);
+
+template <DeviceType t>
+struct AllocatorRegisterer {
+  explicit AllocatorRegisterer(Allocator* alloc) {
+    SetAllocator(t, alloc);
+  }
+};
+
+#define REGISTER_ALLOCATOR(t, f)                       \
+  namespace {                                          \
+  static c10::AllocatorRegisterer<t> g_allocator_d(f); \
+  }
+
+// An interface for reporting thread local memory usage
+// per device
+struct C10_API MemoryReportingInfoBase : public c10::DebugInfoBase {
+  /**
+   * alloc_size corresponds to the size of the ptr.
+   *
+   * total_allocated corresponds to total allocated memory.
+   *
+   * total_reserved corresponds to total size of memory pool, both used and
+   * unused, if applicable.
+   */
+  virtual void reportMemoryUsage(
+      void* ptr,
+      int64_t alloc_size,
+      size_t total_allocated,
+      size_t total_reserved,
+      Device device) = 0;
+
+  virtual void reportOutOfMemory(
+      int64_t alloc_size,
+      size_t total_allocated,
+      size_t total_reserved,
+      Device device);
+
+  virtual bool memoryProfilingEnabled() const = 0;
+};
+
+C10_API bool memoryProfilingEnabled();
+C10_API void reportMemoryUsageToProfiler(
+    void* ptr,
+    int64_t alloc_size,
+    size_t total_allocated,
+    size_t total_reserved,
+    Device device);
+
+C10_API void reportOutOfMemoryToProfiler(
+    int64_t alloc_size,
+    size_t total_allocated,
+    size_t total_reserved,
+    Device device);
+
+// used to hold traceback information in allocators
+// NOLINTNEXTLINE(cppcoreguidelines-special-member-functions)
+struct GatheredContext {
+  virtual ~GatheredContext() = default;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/AutogradState.h b/lib/python3.11/site-packages/torch/include/c10/core/AutogradState.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad168b8c05987b35209fa3450de21a9c29281090
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/AutogradState.h
@@ -0,0 +1,73 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+// Structure used to pack all the thread local boolean
+// flags used by autograd
+struct C10_API AutogradState {
+  static AutogradState& get_tls_state();
+  static void set_tls_state(AutogradState state);
+
+  AutogradState(
+      bool grad_mode,
+      bool inference_mode,
+      bool fw_grad_mode,
+      bool multithreading_enabled)
+      : grad_mode_(grad_mode),
+        inference_mode_(inference_mode),
+        fw_grad_mode_(fw_grad_mode),
+        multithreading_enabled_(multithreading_enabled),
+        view_replay_enabled_(false) {}
+
+  void set_grad_mode(bool enabled) {
+    grad_mode_ = enabled;
+  }
+
+  void set_fw_grad_mode(bool enabled) {
+    fw_grad_mode_ = enabled;
+  }
+
+  void set_inference_mode(bool enabled) {
+    inference_mode_ = enabled;
+  }
+
+  void set_multithreading_enabled(bool multithreading_enabled) {
+    multithreading_enabled_ = multithreading_enabled;
+  }
+
+  void set_view_replay_enabled(bool view_replay_enabled) {
+    view_replay_enabled_ = view_replay_enabled;
+  }
+
+  bool get_grad_mode() const {
+    return grad_mode_;
+  }
+
+  bool get_fw_grad_mode() const {
+    return fw_grad_mode_;
+  }
+
+  bool get_inference_mode() const {
+    return inference_mode_;
+  }
+
+  bool get_multithreading_enabled() const {
+    return multithreading_enabled_;
+  }
+
+  bool get_view_replay_enabled() const {
+    return view_replay_enabled_;
+  }
+
+ private:
+  bool grad_mode_ : 1;
+  bool inference_mode_ : 1;
+  bool fw_grad_mode_ : 1;
+  bool multithreading_enabled_ : 1;
+  // NOLINTNEXTLINE(cppcoreguidelines-use-default-member-init)
+  bool view_replay_enabled_ : 1;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Backend.h b/lib/python3.11/site-packages/torch/include/c10/core/Backend.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ecaa7be7377414337fe3d019e37face208e20eb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Backend.h
@@ -0,0 +1,387 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/util/Exception.h>
+
+#include <stdexcept>
+
+namespace c10 {
+
+/**
+ * This legacy enum class defines the set of backends supported by old school,
+ * code generated Type-based ATen.  A "backend" in this sense roughly
+ * corresponds to the cartesian product of (device type, layout), but restricted
+ * only to combinations which we actually have kernels for.  Backend does NOT
+ * include dtype.
+ *
+ * The reason we are sunsetting this enum class is because it doesn't allow for
+ * open registration; e.g., if you want to add SparseXLA, you'd have to
+ * edit this enum; you wouldn't be able to do it out of tree.  DispatchKey is
+ * the replacement for Backend which supports open registration.
+ *
+ * NB: The concept of 'Backend' here disagrees with the notion of backend
+ * exposed to users in torch.backends.  Backend here is something like "CPU"
+ * or "SparseCUDA"; backend in torch.backends is something like "MKL" or
+ * "CUDNN".
+ */
+enum class Backend {
+  CPU,
+  CUDA,
+  HIP,
+  VE,
+  FPGA,
+  IPU,
+  XPU,
+  SparseCPU,
+  SparseCUDA,
+  SparseCsrCPU,
+  SparseCsrCUDA,
+  SparseHIP,
+  SparseVE,
+  SparseXPU,
+  SparsePrivateUse1,
+  SparseCsrHIP,
+  SparseCsrVE,
+  SparseCsrXPU,
+  SparseCsrPrivateUse1,
+  MAIA,
+  XLA,
+  Vulkan,
+  Metal,
+  Meta,
+  QuantizedCPU,
+  QuantizedCUDA,
+  QuantizedXPU,
+  QuantizedPrivateUse1,
+  Undefined,
+  MkldnnCPU,
+  MPS,
+  HPU,
+  Lazy,
+  MTIA,
+  PrivateUse1,
+  NumOptions
+};
+
+inline Backend dispatchKeyToBackend(DispatchKey t) {
+  if (t == DispatchKey::CPU || t == DispatchKey::AutogradCPU) {
+    return Backend::CPU;
+  } else if (t == DispatchKey::CUDA || t == DispatchKey::AutogradCUDA) {
+    return Backend::CUDA;
+  } else if (t == DispatchKey::HIP) {
+    return Backend::HIP;
+  } else if (t == DispatchKey::VE) {
+    return Backend::VE;
+  } else if (t == DispatchKey::FPGA) {
+    return Backend::FPGA;
+  } else if (t == DispatchKey::MAIA) {
+    return Backend::MAIA;
+  } else if (t == DispatchKey::XLA || t == DispatchKey::AutogradXLA) {
+    return Backend::XLA;
+  } else if (t == DispatchKey::Lazy || t == DispatchKey::AutogradLazy) {
+    return Backend::Lazy;
+  } else if (t == DispatchKey::MPS || t == DispatchKey::AutogradMPS) {
+    return Backend::MPS;
+  } else if (t == DispatchKey::Vulkan) {
+    return Backend::Vulkan;
+  } else if (t == DispatchKey::Metal) {
+    return Backend::Metal;
+  } else if (t == DispatchKey::Meta) {
+    return Backend::Meta;
+  } else if (t == DispatchKey::SparseCPU) {
+    return Backend::SparseCPU;
+  } else if (t == DispatchKey::SparseCUDA) {
+    return Backend::SparseCUDA;
+  } else if (t == DispatchKey::SparseHIP) {
+    return Backend::SparseHIP;
+  } else if (t == DispatchKey::SparseVE) {
+    return Backend::SparseVE;
+  } else if (t == DispatchKey::SparsePrivateUse1) {
+    return Backend::SparsePrivateUse1;
+  } else if (t == DispatchKey::SparseCsrCPU) {
+    return Backend::SparseCsrCPU;
+  } else if (t == DispatchKey::SparseCsrCUDA) {
+    return Backend::SparseCsrCUDA;
+  } else if (t == DispatchKey::SparseCsrHIP) {
+    return Backend::SparseCsrHIP;
+  } else if (t == DispatchKey::SparseCsrVE) {
+    return Backend::SparseCsrVE;
+  } else if (t == DispatchKey::SparseCsrPrivateUse1) {
+    return Backend::SparseCsrPrivateUse1;
+  } else if (t == DispatchKey::MkldnnCPU) {
+    return Backend::MkldnnCPU;
+  } else if (t == DispatchKey::QuantizedCPU) {
+    return Backend::QuantizedCPU;
+  } else if (t == DispatchKey::QuantizedCUDA) {
+    return Backend::QuantizedCUDA;
+  } else if (t == DispatchKey::IPU || t == DispatchKey::AutogradIPU) {
+    return Backend::IPU;
+  } else if (t == DispatchKey::XPU || t == DispatchKey::AutogradXPU) {
+    return Backend::XPU;
+  } else if (t == DispatchKey::SparseXPU) {
+    return Backend::SparseXPU;
+  } else if (t == DispatchKey::SparseCsrXPU) {
+    return Backend::SparseCsrXPU;
+  } else if (t == DispatchKey::QuantizedXPU) {
+    return Backend::QuantizedXPU;
+  } else if (t == DispatchKey::QuantizedPrivateUse1) {
+    return Backend::QuantizedPrivateUse1;
+  } else if (t == DispatchKey::HPU || t == DispatchKey::AutogradHPU) {
+    return Backend::HPU;
+  } else if (t == DispatchKey::MTIA || t == DispatchKey::AutogradMTIA) {
+    return Backend::MTIA;
+  } else if (
+      t == DispatchKey::PrivateUse1 || t == DispatchKey::AutogradPrivateUse1) {
+    return Backend::PrivateUse1;
+  } else if (t == DispatchKey::Undefined) {
+    return Backend::Undefined;
+  } else {
+    TORCH_CHECK(false, "Unrecognized tensor type ID: ", t);
+  }
+}
+
+inline DispatchKey backendToDispatchKey(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+      return DispatchKey::CPU;
+    case Backend::CUDA:
+      return DispatchKey::CUDA;
+    case Backend::HIP:
+      return DispatchKey::HIP;
+    case Backend::VE:
+      return DispatchKey::VE;
+    case Backend::FPGA:
+      return DispatchKey::FPGA;
+    case Backend::MAIA:
+      return DispatchKey::MAIA;
+    case Backend::XLA:
+      return DispatchKey::XLA;
+    case Backend::Lazy:
+      return DispatchKey::Lazy;
+    case Backend::IPU:
+      return DispatchKey::IPU;
+    case Backend::XPU:
+      return DispatchKey::XPU;
+    case Backend::SparseXPU:
+      return DispatchKey::SparseXPU;
+    case Backend::SparseCsrXPU:
+      return DispatchKey::SparseCsrXPU;
+    case Backend::SparseCPU:
+      return DispatchKey::SparseCPU;
+    case Backend::SparseCUDA:
+      return DispatchKey::SparseCUDA;
+    case Backend::SparseHIP:
+      return DispatchKey::SparseHIP;
+    case Backend::SparseVE:
+      return DispatchKey::SparseVE;
+    case Backend::SparsePrivateUse1:
+      return DispatchKey::SparsePrivateUse1;
+    case Backend::SparseCsrCPU:
+      return DispatchKey::SparseCsrCPU;
+    case Backend::SparseCsrCUDA:
+      return DispatchKey::SparseCsrCUDA;
+    case Backend::SparseCsrHIP:
+      return DispatchKey::SparseCsrHIP;
+    case Backend::SparseCsrVE:
+      return DispatchKey::SparseCsrVE;
+    case Backend::SparseCsrPrivateUse1:
+      return DispatchKey::SparseCsrPrivateUse1;
+    case Backend::MkldnnCPU:
+      return DispatchKey::MkldnnCPU;
+    case Backend::Vulkan:
+      return DispatchKey::Vulkan;
+    case Backend::Metal:
+      return DispatchKey::Metal;
+    case Backend::Meta:
+      return DispatchKey::Meta;
+    case Backend::QuantizedCPU:
+      return DispatchKey::QuantizedCPU;
+    case Backend::QuantizedCUDA:
+      return DispatchKey::QuantizedCUDA;
+    case Backend::QuantizedPrivateUse1:
+      return DispatchKey::QuantizedPrivateUse1;
+    case Backend::Undefined:
+      return DispatchKey::Undefined;
+    case Backend::MPS:
+      return DispatchKey::MPS;
+    case Backend::HPU:
+      return DispatchKey::HPU;
+    case Backend::MTIA:
+      return DispatchKey::MTIA;
+    case Backend::PrivateUse1:
+      return DispatchKey::PrivateUse1;
+    default:
+      throw std::runtime_error("Unknown backend");
+  }
+}
+
+inline DeviceType backendToDeviceType(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+    case Backend::MkldnnCPU:
+    case Backend::SparseCPU:
+    case Backend::SparseCsrCPU:
+    case Backend::QuantizedCPU:
+      return DeviceType::CPU;
+    case Backend::CUDA:
+    case Backend::SparseCUDA:
+    case Backend::QuantizedCUDA:
+    case Backend::SparseCsrCUDA:
+      return DeviceType::CUDA;
+    case Backend::HIP:
+      return DeviceType::HIP;
+    case Backend::VE:
+      return DeviceType::VE;
+    case Backend::FPGA:
+      return DeviceType::FPGA;
+    case Backend::MAIA:
+      return DeviceType::MAIA;
+    case Backend::XLA:
+      return DeviceType::XLA;
+    case Backend::Lazy:
+      return DeviceType::Lazy;
+    case Backend::SparseHIP:
+      return DeviceType::HIP;
+    case Backend::SparseVE:
+      return DeviceType::VE;
+    case Backend::SparseCsrHIP:
+      return DeviceType::HIP;
+    case Backend::SparseCsrVE:
+      return DeviceType::VE;
+    case Backend::IPU:
+      return DeviceType::IPU;
+    case Backend::XPU:
+    case Backend::SparseXPU:
+    case Backend::SparseCsrXPU:
+    case Backend::QuantizedXPU:
+      return DeviceType::XPU;
+    case Backend::Vulkan:
+      return DeviceType::Vulkan;
+    case Backend::Metal:
+      return DeviceType::Metal;
+    case Backend::Meta:
+      return DeviceType::Meta;
+    case Backend::MPS:
+      return DeviceType::MPS;
+    case Backend::HPU:
+      return DeviceType::HPU;
+    case Backend::MTIA:
+      return DeviceType::MTIA;
+    case Backend::PrivateUse1:
+    case Backend::SparsePrivateUse1:
+    case Backend::SparseCsrPrivateUse1:
+    case Backend::QuantizedPrivateUse1:
+      return DeviceType::PrivateUse1;
+    case Backend::Undefined:
+      TORCH_CHECK(false, "Undefined backend is not a valid device type");
+    default:
+      TORCH_CHECK(false, "Unknown backend");
+  }
+}
+
+inline const char* toString(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+      return "CPU";
+    case Backend::CUDA:
+      return "CUDA";
+    case Backend::HIP:
+      return "HIP";
+    case Backend::VE:
+      return "VE";
+    case Backend::FPGA:
+      return "FPGA";
+    case Backend::XPU:
+      return "XPU";
+    case Backend::IPU:
+      return "IPU";
+    case Backend::MAIA:
+      return "MAIA";
+    case Backend::XLA:
+      return "XLA";
+    case Backend::Lazy:
+      return "Lazy";
+    case Backend::MPS:
+      return "MPS";
+    case Backend::SparseCPU:
+      return "SparseCPU";
+    case Backend::SparseCUDA:
+      return "SparseCUDA";
+    case Backend::SparseHIP:
+      return "SparseHIP";
+    case Backend::SparseVE:
+      return "SparseVE";
+    case Backend::SparseXPU:
+      return "SparseXPU";
+    case Backend::SparsePrivateUse1:
+      return "SparsePrivateUse1";
+    case Backend::SparseCsrCPU:
+      return "SparseCsrCPU";
+    case Backend::SparseCsrCUDA:
+      return "SparseCsrCUDA";
+    case Backend::SparseCsrHIP:
+      return "SparseCsrHIP";
+    case Backend::SparseCsrVE:
+      return "SparseCsrVE";
+    case Backend::SparseCsrXPU:
+      return "SparseCsrXPU";
+    case Backend::SparseCsrPrivateUse1:
+      return "SparseCsrPrivateUse1";
+    case Backend::MkldnnCPU:
+      return "MkldnnCPU";
+    case Backend::Vulkan:
+      return "Vulkan";
+    case Backend::Metal:
+      return "Metal";
+    case Backend::Meta:
+      return "Meta";
+    case Backend::QuantizedCPU:
+      return "QuantizedCPU";
+    case Backend::QuantizedCUDA:
+      return "QuantizedCUDA";
+    case Backend::QuantizedXPU:
+      return "QuantizedXPU";
+    case Backend::QuantizedPrivateUse1:
+      return "QuantizedPrivateUse1";
+    case Backend::HPU:
+      return "HPU";
+    case Backend::MTIA:
+      return "MTIA";
+    case Backend::PrivateUse1:
+      return "PrivateUseOne";
+    default:
+      return "UNKNOWN_BACKEND";
+  }
+}
+
+inline bool isSparse(Backend b) {
+  switch (b) {
+    case Backend::SparseXPU:
+    case Backend::SparseCPU:
+    case Backend::SparseCUDA:
+    case Backend::SparseHIP:
+    case Backend::SparseVE:
+    case Backend::SparsePrivateUse1:
+      return true;
+    default:
+      return false;
+  }
+}
+
+inline bool isSparseCsr(Backend b) {
+  switch (b) {
+    case Backend::SparseCsrXPU:
+    case Backend::SparseCsrCPU:
+    case Backend::SparseCsrCUDA:
+    case Backend::SparseCsrHIP:
+    case Backend::SparseCsrVE:
+    case Backend::SparseCsrPrivateUse1:
+      return true;
+    default:
+      return false;
+  }
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/CPUAllocator.h b/lib/python3.11/site-packages/torch/include/c10/core/CPUAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..98debb9db50ddffbca6ff4fc567e895d482d27c9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/CPUAllocator.h
@@ -0,0 +1,59 @@
+#pragma once
+
+#include <cstdint>
+#include <cstring>
+#include <mutex>
+#include <unordered_map>
+
+#include <c10/core/Allocator.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Flags.h>
+
+// TODO: rename to c10
+C10_DECLARE_bool(caffe2_report_cpu_memory_usage);
+
+namespace c10 {
+
+using MemoryDeleter = void (*)(void*);
+
+// A helper function that is basically doing nothing.
+C10_API void NoDelete(void*);
+
+// A simple struct that is used to report C10's memory allocation,
+// deallocation status and out-of-memory events to the profiler
+class C10_API ProfiledCPUMemoryReporter {
+ public:
+  ProfiledCPUMemoryReporter() = default;
+  void New(void* ptr, size_t nbytes);
+  void OutOfMemory(size_t nbytes);
+  void Delete(void* ptr);
+
+ private:
+  std::mutex mutex_;
+  std::unordered_map<void*, size_t> size_table_;
+  size_t allocated_ = 0;
+  size_t log_cnt_ = 0;
+};
+
+C10_API ProfiledCPUMemoryReporter& profiledCPUMemoryReporter();
+
+// Get the CPU Allocator.
+C10_API at::Allocator* GetCPUAllocator();
+// Sets the CPU allocator to the given allocator: the caller gives away the
+// ownership of the pointer.
+C10_API void SetCPUAllocator(at::Allocator* alloc, uint8_t priority = 0);
+
+// Get the Default CPU Allocator
+C10_API at::Allocator* GetDefaultCPUAllocator();
+
+// Get the Default Mobile CPU Allocator
+C10_API at::Allocator* GetDefaultMobileCPUAllocator();
+
+// The CPUCachingAllocator is experimental and might disappear in the future.
+// The only place that uses it is in StaticRuntime.
+// Set the CPU Caching Allocator
+C10_API void SetCPUCachingAllocator(Allocator* alloc, uint8_t priority = 0);
+// Get the CPU Caching Allocator
+C10_API Allocator* GetCPUCachingAllocator();
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/CachingDeviceAllocator.h b/lib/python3.11/site-packages/torch/include/c10/core/CachingDeviceAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..8724ecf88ae0f9c323d9263e2d1fefeca18a79d5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/CachingDeviceAllocator.h
@@ -0,0 +1,131 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/util/irange.h>
+
+#include <array>
+
+namespace c10::CachingDeviceAllocator {
+
+struct Stat {
+  void increase(size_t amount) {
+    current += static_cast<int64_t>(amount);
+    peak = std::max(current, peak);
+    allocated += static_cast<int64_t>(amount);
+  }
+
+  void decrease(size_t amount) {
+    current -= static_cast<int64_t>(amount);
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        current >= 0,
+        "Negative tracked stat in device allocator (likely logic error).");
+    freed += static_cast<int64_t>(amount);
+  }
+
+  void reset_accumulated() {
+    allocated = 0;
+    freed = 0;
+  }
+
+  void reset_peak() {
+    peak = current;
+  }
+
+  int64_t current = 0;
+  int64_t peak = 0;
+  int64_t allocated = 0;
+  int64_t freed = 0;
+};
+
+enum struct StatType : uint64_t {
+  AGGREGATE = 0,
+  SMALL_POOL = 1,
+  LARGE_POOL = 2,
+  NUM_TYPES = 3 // remember to update this whenever a new stat type is added
+};
+
+using StatArray = std::array<Stat, static_cast<size_t>(StatType::NUM_TYPES)>;
+using StatTypes = std::array<bool, static_cast<size_t>(StatType::NUM_TYPES)>;
+
+template <typename Func>
+void for_each_selected_stat_type(const StatTypes& stat_types, Func f) {
+  for (const auto stat_type : c10::irange(stat_types.size())) {
+    if (stat_types[stat_type]) {
+      f(stat_type);
+    }
+  }
+}
+
+// Struct containing memory allocator summary statistics for a device.
+struct DeviceStats {
+  // COUNT: allocations requested by client code
+  StatArray allocation;
+  // COUNT: number of allocated segments from device memory allocation.
+  StatArray segment;
+  // COUNT: number of active memory blocks (allocated or used by stream)
+  StatArray active;
+  // COUNT: number of inactive, split memory blocks (unallocated but can't be
+  // released via device memory deallocation)
+  StatArray inactive_split;
+
+  // SUM: bytes allocated by this memory alocator
+  StatArray allocated_bytes;
+  // SUM: bytes reserved by this memory allocator (both free and used)
+  StatArray reserved_bytes;
+  // SUM: bytes within active memory blocks
+  StatArray active_bytes;
+  // SUM: bytes within inactive, split memory blocks
+  StatArray inactive_split_bytes;
+  // SUM: bytes requested by client code
+  StatArray requested_bytes;
+
+  // COUNT: total number of failed calls to device malloc necessitating cache
+  // flushes.
+  int64_t num_alloc_retries = 0;
+
+  // COUNT: total number of OOMs (i.e. failed calls to device memory allocation
+  // after cache flush)
+  int64_t num_ooms = 0;
+
+  // COUNT: total number of oversize blocks allocated from pool
+  Stat oversize_allocations;
+
+  // COUNT: total number of oversize blocks requiring malloc
+  Stat oversize_segments;
+
+  // COUNT: total number of synchronize_and_free_events() calls
+  int64_t num_sync_all_streams = 0;
+
+  // COUNT: total number of device memory allocation calls. This includes both
+  // mapped and malloced memory.
+  int64_t num_device_alloc = 0;
+
+  // COUNT: total number of device memory deallocation calls. This includes both
+  // un-mapped and free memory.
+  int64_t num_device_free = 0;
+
+  // SIZE: maximum block size that is allowed to be split.
+  int64_t max_split_size = 0;
+};
+
+// Size pretty-printer
+inline std::string format_size(uint64_t size) {
+  std::ostringstream os;
+  os.precision(2);
+  os << std::fixed;
+  if (size <= 1024) {
+    os << size << " bytes";
+  } else if (size <= 1048576) {
+    os << (static_cast<double>(size) / 1024.0);
+    os << " KiB";
+  } else if (size <= 1073741824ULL) {
+    os << static_cast<double>(size) / 1048576.0;
+    os << " MiB";
+  } else {
+    os << static_cast<double>(size) / 1073741824.0;
+    os << " GiB";
+  }
+  return os.str();
+}
+
+} // namespace c10::CachingDeviceAllocator
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/CompileTimeFunctionPointer.h b/lib/python3.11/site-packages/torch/include/c10/core/CompileTimeFunctionPointer.h
new file mode 100644
index 0000000000000000000000000000000000000000..a5fbd1f3e1f3849b4585a623c37e2a5cf2a0a924
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/CompileTimeFunctionPointer.h
@@ -0,0 +1,57 @@
+#pragma once
+
+#include <c10/util/TypeTraits.h>
+#include <type_traits>
+
+namespace c10 {
+
+/**
+ * Represent a function pointer as a C++ type.
+ * This allows using the function pointer as a type
+ * in a template and calling it from inside the template
+ * allows the compiler to inline the call because it
+ * knows the function pointer at compile time.
+ *
+ * Example 1:
+ *  int add(int a, int b) {return a + b;}
+ *  using Add = TORCH_FN_TYPE(add);
+ *  template<class Func> struct Executor {
+ *    int execute(int a, int b) {
+ *      return Func::func_ptr()(a, b);
+ *    }
+ *  };
+ *  Executor<Add> executor;
+ *  EXPECT_EQ(3, executor.execute(1, 2));
+ *
+ * Example 2:
+ *  int add(int a, int b) {return a + b;}
+ *  template<class Func> int execute(Func, int a, int b) {
+ *    return Func::func_ptr()(a, b);
+ *  }
+ *  EXPECT_EQ(3, execute(TORCH_FN(add), 1, 2));
+ */
+template <class FuncType_, FuncType_* func_ptr_>
+struct CompileTimeFunctionPointer final {
+  static_assert(
+      guts::is_function_type<FuncType_>::value,
+      "TORCH_FN can only wrap function types.");
+  using FuncType = FuncType_;
+
+  static constexpr FuncType* func_ptr() {
+    return func_ptr_;
+  }
+};
+
+template <class T>
+struct is_compile_time_function_pointer : std::false_type {};
+template <class FuncType, FuncType* func_ptr>
+struct is_compile_time_function_pointer<
+    CompileTimeFunctionPointer<FuncType, func_ptr>> : std::true_type {};
+
+} // namespace c10
+
+#define TORCH_FN_TYPE(func)                                           \
+  ::c10::CompileTimeFunctionPointer<                                  \
+      std::remove_pointer_t<std::remove_reference_t<decltype(func)>>, \
+      func>
+#define TORCH_FN(func) TORCH_FN_TYPE(func)()
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/ConstantSymNodeImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/ConstantSymNodeImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..c371a860645cf33620c723706c338a1cf7f1d7c6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/ConstantSymNodeImpl.h
@@ -0,0 +1,110 @@
+#pragma once
+
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <cstdint>
+#include <optional>
+#include <string>
+#include <variant>
+
+namespace c10 {
+
+// Unlike other SymNodeImpl, this cannot be "dispatched" conventionally,
+// as it typically needs to defer to another SymNodeImpl
+//
+// Can either represent a bool, int (don't support float yet) this is useful
+// for representing otherwise unrepresentable large negative integer constant.
+template <typename T>
+class C10_API ConstantSymNodeImpl : public SymNodeImpl {
+  static_assert(
+      ::std::is_same_v<T, int64_t> || ::std::is_same_v<T, bool>,
+      "ConstantSymNodeImpl can only accept int64_t or bool types");
+
+ public:
+  ConstantSymNodeImpl(T val) : value_(val) {}
+
+  bool is_int() override {
+    return is_int_();
+  }
+  bool is_bool() override {
+    return is_bool_();
+  }
+  bool is_float() override {
+    return false;
+  }
+  int64_t guard_int(
+      const char* file [[maybe_unused]],
+      int64_t line [[maybe_unused]]) override {
+    TORCH_CHECK(is_int(), "not an int");
+    return int_();
+  }
+  bool guard_bool(
+      const char* file [[maybe_unused]],
+      int64_t line [[maybe_unused]]) override {
+    TORCH_CHECK(is_bool(), "not a bool");
+    return bool_();
+  }
+  double guard_float(
+      const char* file [[maybe_unused]],
+      int64_t line [[maybe_unused]]) override {
+    TORCH_CHECK(false, "not a float");
+  }
+  int64_t int_() override {
+    TORCH_CHECK(is_int(), "not an int");
+    return ::std::get<int64_t>(value_);
+  }
+  bool bool_() override {
+    TORCH_CHECK(is_bool(), "not a bool");
+    return ::std::get<bool>(value_);
+  }
+  bool has_hint() override {
+    return true;
+  }
+  c10::SymNode eq(const c10::SymNode& other) override;
+  c10::SymNode ne(const c10::SymNode& other) override;
+  c10::SymNode ge(const c10::SymNode& other) override;
+  c10::SymNode le(const c10::SymNode& other) override;
+  c10::SymNode lt(const c10::SymNode& other) override;
+  c10::SymNode gt(const c10::SymNode& other) override;
+  c10::SymNode mul(const c10::SymNode& other) override;
+  ::std::string str() override {
+    if constexpr (is_int_()) {
+      return ::std::to_string(::std::get<int64_t>(value_));
+    } else {
+      return ::std::get<bool>(value_) ? "true" : "false";
+    }
+  }
+  std::optional<int64_t> constant_int() override {
+    if constexpr (is_int_()) {
+      return ::std::get<int64_t>(value_);
+    } else {
+      return std::nullopt;
+    }
+  }
+  std::optional<bool> constant_bool() override {
+    if constexpr (is_bool_()) {
+      return ::std::get<bool>(value_);
+    } else {
+      return std::nullopt;
+    }
+  }
+  bool is_constant() override {
+    return true;
+  }
+  bool is_symbolic() override {
+    return false;
+  }
+
+ private:
+  ::std::variant<int64_t, bool> value_;
+
+  static constexpr bool is_int_() {
+    return ::std::is_same_v<T, int64_t>;
+  }
+  static constexpr bool is_bool_() {
+    return ::std::is_same_v<T, bool>;
+  }
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Contiguity.h b/lib/python3.11/site-packages/torch/include/c10/core/Contiguity.h
new file mode 100644
index 0000000000000000000000000000000000000000..36f41b6251c0cc64f680499c08799b1201456f3e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Contiguity.h
@@ -0,0 +1,129 @@
+#pragma once
+#include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/SmallVector.h>
+#include <c10/util/irange.h>
+
+#include <algorithm>
+#include <cstdint>
+
+namespace c10 {
+
+template <typename T>
+bool _compute_contiguous(ArrayRef<T> sizes, ArrayRef<T> strides, T numel) {
+  bool is_contiguous = true;
+  if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_eq(numel, 0))) {
+    return is_contiguous;
+  }
+  T z = 1;
+  // NB: make sure we do signed arithmetic
+  for (int64_t d = int64_t(sizes.size()) - 1; d >= 0; d--) {
+    const auto& size_d = sizes[d];
+    if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+      if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_eq(strides[d], z))) {
+        z *= size_d;
+      } else {
+        is_contiguous = false;
+        break;
+      }
+    }
+  }
+  return is_contiguous;
+}
+
+template <typename T>
+bool _compute_channels_last_contiguous_2d(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  // Please don't combine these code, constant array is used here to let
+  // compiler fully unroll the loop to get better performance
+  switch (sizes.size()) {
+    case 4: {
+      T expected = 1;
+      for (auto& d : {1, 3, 2, 0}) {
+        const auto& size_d = sizes[d];
+        if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+          if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(strides[d], expected))) {
+            return false;
+          }
+          expected *= size_d;
+        }
+      }
+      return true;
+    }
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 3:
+      // TODO dim == 3 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+bool _compute_channels_last_contiguous_3d(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  // Please don't combine these code, constant array is used here to let
+  // compiler fully unroll the loop to get better performance
+  switch (sizes.size()) {
+    case 5: {
+      T expected = 1;
+      for (auto& d : {1, 4, 3, 2, 0}) {
+        const auto& size_d = sizes[d];
+        if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+          if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(strides[d], expected))) {
+            return false;
+          }
+          expected *= size_d;
+        }
+      }
+      return true;
+    }
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 4:
+      // TODO dim == 4 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+bool _compute_non_overlapping_and_dense(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  auto dim = sizes.size();
+  if (dim == 1) {
+    return sizes[0] < 2 || strides[0] == 1;
+  }
+  SmallVector<int64_t, 5> perm;
+  perm.resize(dim);
+  for (const auto i : c10::irange(dim)) {
+    perm[i] = i;
+  }
+  // Sort by strides, leaving 0 and 1 sized dims at the end of the array
+  std::sort(perm.begin(), perm.end(), [&](int64_t a, int64_t b) {
+    if (sizes[a] < 2) {
+      return false;
+    } else if (sizes[b] < 2) {
+      return true;
+    }
+    return strides[a] < strides[b];
+  });
+  T require_stride = 1;
+  for (const auto i : c10::irange(dim)) {
+    const auto& size_perm_i = sizes[perm[i]];
+    if (size_perm_i < 2) {
+      return true;
+    }
+    if (strides[perm[i]] != require_stride) {
+      return false;
+    }
+    require_stride *= size_perm_i;
+  }
+  return true;
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/CopyBytes.h b/lib/python3.11/site-packages/torch/include/c10/core/CopyBytes.h
new file mode 100644
index 0000000000000000000000000000000000000000..78eb0dc9f090a28ba10c3120ae9979b9af9f387a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/CopyBytes.h
@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <cstddef>
+
+namespace c10 {
+
+using CopyBytesFunction = void (*)(
+    size_t nbytes,
+    const void* src,
+    Device src_device,
+    void* dst,
+    Device dst_device);
+
+struct C10_API _CopyBytesFunctionRegisterer {
+  _CopyBytesFunctionRegisterer(
+      DeviceType from,
+      DeviceType to,
+      CopyBytesFunction func_sync,
+      CopyBytesFunction func_async = nullptr);
+};
+
+#define REGISTER_COPY_BYTES_FUNCTION(from, to, ...)           \
+  namespace {                                                 \
+  static _CopyBytesFunctionRegisterer C10_ANONYMOUS_VARIABLE( \
+      g_copy_function)(from, to, __VA_ARGS__);                \
+  }
+
+/*
+ * WARNING: Implementations for this function are currently registered from
+ * ATen and caffe2, not yet from c10. Don't use this if not either ATen
+ * or caffe2 is present as well.
+ * We can't move them yet, because the CUDA implementations aren't unified yet
+ * between ATen and caffe2.
+ * We're planning to move the implementations into c10/backend/xxx
+ * to make c10 self contained again.
+ */
+C10_API void CopyBytes(
+    size_t nbytes,
+    const void* src,
+    Device src_device,
+    void* dst,
+    Device dst_device,
+    bool async);
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DefaultDtype.h b/lib/python3.11/site-packages/torch/include/c10/core/DefaultDtype.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f23051dc682395ba92b2fcc4043162abaa8ec47
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DefaultDtype.h
@@ -0,0 +1,15 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/macros/Export.h>
+
+namespace caffe2 {
+class TypeMeta;
+} // namespace caffe2
+
+namespace c10 {
+C10_API void set_default_dtype(caffe2::TypeMeta dtype);
+C10_API const caffe2::TypeMeta get_default_dtype();
+C10_API ScalarType get_default_dtype_as_scalartype();
+C10_API const caffe2::TypeMeta get_default_complex_dtype();
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DefaultTensorOptions.h b/lib/python3.11/site-packages/torch/include/c10/core/DefaultTensorOptions.h
new file mode 100644
index 0000000000000000000000000000000000000000..284af1388ef648df356cf13f2737b784fc269a73
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DefaultTensorOptions.h
@@ -0,0 +1,45 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Layout.h>
+#include <c10/core/ScalarType.h>
+#include <c10/util/typeid.h>
+
+namespace c10 {
+
+struct TensorOptions;
+
+/// Like TensorOptions, but all fields are guaranteed to be filled.
+struct DefaultTensorOptions {
+  DefaultTensorOptions() = default;
+
+  caffe2::TypeMeta dtype() const noexcept {
+    return dtype_;
+  }
+  Device device() const noexcept {
+    return device_;
+  }
+  Layout layout() const noexcept {
+    return layout_;
+  }
+  bool requires_grad() const noexcept {
+    return requires_grad_;
+  }
+
+  // Defined in TensorOptions.h
+  inline DefaultTensorOptions& merge(const TensorOptions& options);
+
+ private:
+  caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make<float>(); // 64-bit
+  Device device_ = at::kCPU; // 32-bit
+  Layout layout_ = at::kStrided; // 8-bit
+  bool requires_grad_ = false; // 8-bit
+};
+
+inline const DefaultTensorOptions& getDefaultTensorOptions() {
+  static const auto options = DefaultTensorOptions();
+  return options;
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Device.h b/lib/python3.11/site-packages/torch/include/c10/core/Device.h
new file mode 100644
index 0000000000000000000000000000000000000000..cbe9129852adecc986f6b541be2c1f53aac08b40
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Device.h
@@ -0,0 +1,216 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <iosfwd>
+#include <string>
+
+namespace c10 {
+
+/// An index representing a specific device; e.g., the 1 in GPU 1.
+/// A DeviceIndex is not independently meaningful without knowing
+/// the DeviceType it is associated; try to use Device rather than
+/// DeviceIndex directly.
+using DeviceIndex = int8_t;
+
+/// Represents a compute device on which a tensor is located. A device is
+/// uniquely identified by a type, which specifies the type of machine it is
+/// (e.g. CPU or CUDA GPU), and a device index or ordinal, which identifies the
+/// specific compute device when there is more than one of a certain type. The
+/// device index is optional, and in its defaulted state represents (abstractly)
+/// "the current device". Further, there are two constraints on the value of the
+/// device index, if one is explicitly stored:
+/// 1. A negative index represents the current device, a non-negative index
+/// represents a specific, concrete device,
+/// 2. When the device type is CPU, the device index must be zero.
+struct C10_API Device final {
+  using Type = DeviceType;
+
+  /// Constructs a new `Device` from a `DeviceType` and an optional device
+  /// index.
+  /* implicit */ Device(DeviceType type, DeviceIndex index = -1)
+      : type_(type), index_(index) {
+    validate();
+  }
+
+  /// Constructs a `Device` from a string description, for convenience.
+  /// The string supplied must follow the following schema:
+  /// `(cpu|cuda)[:<device-index>]`
+  /// where `cpu` or `cuda` specifies the device type, and
+  /// `:<device-index>` optionally specifies a device index.
+  /* implicit */ Device(const std::string& device_string);
+
+  /// Returns true if the type and index of this `Device` matches that of
+  /// `other`.
+  bool operator==(const Device& other) const noexcept {
+    return this->type_ == other.type_ && this->index_ == other.index_;
+  }
+
+  /// Returns true if the type or index of this `Device` differs from that of
+  /// `other`.
+  bool operator!=(const Device& other) const noexcept {
+    return !(*this == other);
+  }
+
+  /// Sets the device index.
+  void set_index(DeviceIndex index) {
+    index_ = index;
+  }
+
+  /// Returns the type of device this is.
+  DeviceType type() const noexcept {
+    return type_;
+  }
+
+  /// Returns the optional index.
+  DeviceIndex index() const noexcept {
+    return index_;
+  }
+
+  /// Returns true if the device has a non-default index.
+  bool has_index() const noexcept {
+    return index_ != -1;
+  }
+
+  /// Return true if the device is of CUDA type.
+  bool is_cuda() const noexcept {
+    return type_ == DeviceType::CUDA;
+  }
+
+  /// Return true if the device is of PrivateUse1 type.
+  bool is_privateuseone() const noexcept {
+    return type_ == DeviceType::PrivateUse1;
+  }
+
+  /// Return true if the device is of MPS type.
+  bool is_mps() const noexcept {
+    return type_ == DeviceType::MPS;
+  }
+
+  /// Return true if the device is of HIP type.
+  bool is_hip() const noexcept {
+    return type_ == DeviceType::HIP;
+  }
+
+  /// Return true if the device is of VE type.
+  bool is_ve() const noexcept {
+    return type_ == DeviceType::VE;
+  }
+
+  /// Return true if the device is of XPU type.
+  bool is_xpu() const noexcept {
+    return type_ == DeviceType::XPU;
+  }
+
+  /// Return true if the device is of IPU type.
+  bool is_ipu() const noexcept {
+    return type_ == DeviceType::IPU;
+  }
+
+  /// Return true if the device is of XLA type.
+  bool is_xla() const noexcept {
+    return type_ == DeviceType::XLA;
+  }
+
+  /// Return true if the device is of MTIA type.
+  bool is_mtia() const noexcept {
+    return type_ == DeviceType::MTIA;
+  }
+
+  /// Return true if the device is of HPU type.
+  bool is_hpu() const noexcept {
+    return type_ == DeviceType::HPU;
+  }
+
+  /// Return true if the device is of Lazy type.
+  bool is_lazy() const noexcept {
+    return type_ == DeviceType::Lazy;
+  }
+
+  /// Return true if the device is of Vulkan type.
+  bool is_vulkan() const noexcept {
+    return type_ == DeviceType::Vulkan;
+  }
+
+  /// Return true if the device is of Metal type.
+  bool is_metal() const noexcept {
+    return type_ == DeviceType::Metal;
+  }
+
+  /// Return true if the device is of MAIA type.
+  bool is_maia() const noexcept {
+    return type_ == DeviceType::MAIA;
+  }
+
+  /// Return true if the device is of META type.
+  bool is_meta() const noexcept {
+    return type_ == DeviceType::Meta;
+  }
+
+  /// Return true if the device is of CPU type.
+  bool is_cpu() const noexcept {
+    return type_ == DeviceType::CPU;
+  }
+
+  /// Return true if the device supports arbitrary strides.
+  bool supports_as_strided() const noexcept {
+    return type_ != DeviceType::IPU && type_ != DeviceType::XLA &&
+        type_ != DeviceType::Lazy && type_ != DeviceType::MTIA;
+  }
+
+  /// Same string as returned from operator<<.
+  std::string str() const;
+
+ private:
+  DeviceType type_;
+  DeviceIndex index_ = -1;
+  void validate() {
+    // Removing these checks in release builds noticeably improves
+    // performance in micro-benchmarks.
+    // This is safe to do, because backends that use the DeviceIndex
+    // have a later check when we actually try to switch to that device.
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        index_ >= -1,
+        "Device index must be -1 or non-negative, got ",
+        static_cast<int>(index_));
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        !is_cpu() || index_ <= 0,
+        "CPU device index must be -1 or zero, got ",
+        static_cast<int>(index_));
+  }
+};
+
+C10_API std::ostream& operator<<(std::ostream& stream, const Device& device);
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::Device> {
+  size_t operator()(c10::Device d) const noexcept {
+    // Are you here because this static assert failed?  Make sure you ensure
+    // that the bitmasking code below is updated accordingly!
+    static_assert(sizeof(c10::DeviceType) == 1, "DeviceType is not 8-bit");
+    static_assert(sizeof(c10::DeviceIndex) == 1, "DeviceIndex is not 8-bit");
+    // Note [Hazard when concatenating signed integers]
+    // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    // We must first convert to a same-sized unsigned type, before promoting to
+    // the result type, to prevent sign extension when any of the values is -1.
+    // If sign extension occurs, you'll clobber all of the values in the MSB
+    // half of the resulting integer.
+    //
+    // Technically, by C/C++ integer promotion rules, we only need one of the
+    // uint32_t casts to the result type, but we put in both for explicitness's
+    // sake.
+    uint32_t bits = static_cast<uint32_t>(static_cast<uint8_t>(d.type()))
+            << 16 |
+        static_cast<uint32_t>(static_cast<uint8_t>(d.index()));
+    return std::hash<uint32_t>{}(bits);
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DeviceArray.h b/lib/python3.11/site-packages/torch/include/c10/core/DeviceArray.h
new file mode 100644
index 0000000000000000000000000000000000000000..d9d4c72d48cd6bbfca3740c029eee31f23ca29e4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DeviceArray.h
@@ -0,0 +1,28 @@
+#include <c10/core/Allocator.h>
+#include <c10/util/Exception.h>
+#include <cstddef>
+#include <cstdint>
+#include <type_traits>
+
+namespace c10 {
+
+template <typename T>
+class DeviceArray {
+ public:
+  DeviceArray(c10::Allocator& allocator, size_t size)
+      : data_ptr_(allocator.allocate(size * sizeof(T))) {
+    static_assert(std::is_trivial_v<T>, "T must be a trivial type");
+    TORCH_INTERNAL_ASSERT(
+        0 == (reinterpret_cast<intptr_t>(data_ptr_.get()) % alignof(T)),
+        "c10::DeviceArray: Allocated memory is not aligned for this data type");
+  }
+
+  T* get() {
+    return static_cast<T*>(data_ptr_.get());
+  }
+
+ private:
+  c10::DataPtr data_ptr_;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DeviceGuard.h b/lib/python3.11/site-packages/torch/include/c10/core/DeviceGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..7fa366049480472859ebc849db7316dac6f06d80
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DeviceGuard.h
@@ -0,0 +1,202 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/InlineDeviceGuard.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+#include <c10/util/Optional.h>
+
+namespace c10 {
+
+/// RAII guard that sets a certain default device in its constructor, and
+/// changes it back to the device that was originally active upon destruction.
+///
+/// The device is always reset to the one that was active at the time of
+/// construction of the guard. Even if you `set_device` after construction, the
+/// destructor will still reset the device to the one that was active at
+/// construction time.
+///
+/// This device guard does NOT have an uninitialized state; it is guaranteed
+/// to reset a device on exit.  If you are in a situation where you *might*
+/// want to setup a guard (i.e., are looking for the moral equivalent
+/// of std::optional<DeviceGuard>), see OptionalDeviceGuard.
+class DeviceGuard {
+ public:
+  /// No default constructor; see Note [Omitted default constructor from RAII]
+  explicit DeviceGuard() = delete;
+
+  /// Set the current device to the passed Device.
+  explicit DeviceGuard(Device device) : guard_(device) {}
+
+  /// This constructor is for testing only.
+  explicit DeviceGuard(
+      Device device,
+      const impl::DeviceGuardImplInterface* impl)
+      : guard_(device, impl) {}
+
+  ~DeviceGuard() = default;
+
+  /// Copy is disallowed
+  DeviceGuard(const DeviceGuard&) = delete;
+  DeviceGuard& operator=(const DeviceGuard&) = delete;
+
+  /// Move is disallowed, as DeviceGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  DeviceGuard(DeviceGuard&& other) = delete;
+  DeviceGuard& operator=(DeviceGuard&& other) = delete;
+
+  /// Sets the device to the given one.  The specified device must be consistent
+  /// with the device type originally specified during guard construction.
+  ///
+  /// TODO: The consistency check here is inconsistent with StreamGuard's
+  /// behavior with set_stream, where a stream on a different device than
+  /// the original one isn't an error; we just reset the stream and then
+  /// switch devices.
+  void reset_device(at::Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// This method is for testing only.
+  void reset_device(
+      at::Device device,
+      const impl::DeviceGuardImplInterface* impl) {
+    guard_.reset_device(device, impl);
+  }
+
+  /// Sets the device index to the given one.  The device type is inferred
+  /// from the original device type the guard was constructed with.
+  void set_index(DeviceIndex index) {
+    guard_.set_index(index);
+  }
+
+  /// Returns the device that was set at the time the guard was constructed.
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+ private:
+  impl::InlineDeviceGuard<impl::VirtualGuardImpl> guard_;
+};
+
+/**
+ * A OptionalDeviceGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ * Morally, a OptionalDeviceGuard is equivalent to std::optional<DeviceGuard>,
+ * but with extra constructors and methods as appropriate.
+ *
+ * Besides its obvious use (optionally applying a DeviceGuard),
+ * OptionalDeviceGuard is often also used for the following idiom:
+ *
+ *    OptionalDeviceGuard g;
+ *    for (const auto& t : tensors) {
+ *      g.set_device(t.device());
+ *      do_something_with(t);
+ *    }
+ *
+ * This usage is marginally more efficient than constructing a DeviceGuard every
+ * iteration of the for loop, as it avoids an unnecessary device reset.
+ *
+ * Unlike DeviceGuard, a OptionalDeviceGuard may be uninitialized.  This occurs
+ * when you use the nullary constructor, or pass a nullopt to the constructor.
+ * Uninitialized OptionalDeviceGuards do *nothing*; they do not know what the
+ * original device was and they do not reset on destruction.  This is why
+ * original_device() and current_device() return std::optional<Device> rather
+ * than Device (as they do in DeviceGuard), and also is why we didn't just
+ * provide OptionalDeviceGuard by default and hide DeviceGuard from users.
+ *
+ * The semantics of an OptionalDeviceGuard are exactly explained by thinking
+ * of it as an std::optional<DeviceGuard>.  In particular, an initialized
+ * OptionalDeviceGuard doesn't restore device to its value at construction; it
+ * restores device to its value *at initialization*.  So if you have the
+ * program:
+ *
+ *     setDevice(1);
+ *     OptionalDeviceGuard g;
+ *     setDevice(2);
+ *     g.reset_device(Device(DeviceType::CUDA, 3));  // initializes!
+ *
+ * On destruction, g will reset device to 2, rather than 1.
+ *
+ * An uninitialized OptionalDeviceGuard is distinct from a (initialized)
+ * DeviceGuard whose original_device_ and current_device_ match, since the
+ * DeviceGuard will still reset the device to original_device_.
+ */
+class OptionalDeviceGuard {
+ public:
+  /// Create an uninitialized guard.  Set the guard later using reset_device.
+  explicit OptionalDeviceGuard() = default;
+
+  /// Initialize the guard, setting the current device to the passed Device.
+  explicit OptionalDeviceGuard(Device device) : guard_(device) {}
+
+  /// Initialize the guard if a Device is passed; otherwise leave the
+  /// guard uninitialized.
+  explicit OptionalDeviceGuard(std::optional<Device> device) : guard_(device) {}
+
+  /// Constructor for testing only.
+  explicit OptionalDeviceGuard(
+      Device device,
+      const impl::DeviceGuardImplInterface* impl)
+      : guard_(device, impl) {}
+
+  ~OptionalDeviceGuard() = default;
+  /// Copy is disallowed
+  OptionalDeviceGuard(const OptionalDeviceGuard&) = delete;
+  OptionalDeviceGuard& operator=(const OptionalDeviceGuard&) = delete;
+
+  /// Move is disallowed
+  /// See Note [Explicit initialization of optional fields]
+  /// and // Note [Move construction for RAII guards is tricky]
+  /// for rationale.
+  OptionalDeviceGuard(OptionalDeviceGuard&& other) = delete;
+  OptionalDeviceGuard& operator=(OptionalDeviceGuard&& other) = delete;
+
+  /// Sets the device to the given one.  The specified device must be consistent
+  /// with the device type originally specified during guard construction.
+  void reset_device(at::Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// For testing only
+  void reset_device(
+      at::Device device,
+      const impl::DeviceGuardImplInterface* impl) {
+    guard_.reset_device(device, impl);
+  }
+
+  /// Returns the device that was set at the time the guard was constructed.
+  std::optional<Device> original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via reset_device.
+  std::optional<Device> current_device() const {
+    return guard_.current_device();
+  }
+
+ private:
+  impl::InlineOptionalDeviceGuard<impl::VirtualGuardImpl> guard_{};
+};
+
+// Note [Whither the DeviceGuard boilerplate]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Design note: in principle, we could avoid these wrappers using:
+//
+// using DeviceGuard = impl::InlineDeviceGuard<impl::VirtualGuardImpl>;
+// using OptionalDeviceGuard =
+// impl::InlineOptionalDeviceGuard<impl::VirtualGuardImpl>;
+//
+// But the error messages are worse, and our users can't just look at the
+// header file to find out what's going on.  Furthermore, for specializations
+// like CUDAStreamGuard, it can be profitable to replace some interfaces with
+// refined types (e.g., return CUDAStream instead of Stream).  So, we eat
+// the boilerplate and write out the API explicitly.
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DeviceType.h b/lib/python3.11/site-packages/torch/include/c10/core/DeviceType.h
new file mode 100644
index 0000000000000000000000000000000000000000..911c863363f962e5b5f37f862bc0b81fdee80740
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DeviceType.h
@@ -0,0 +1,123 @@
+#pragma once
+
+// This is directly synchronized with caffe2/proto/caffe2.proto, but
+// doesn't require me to figure out how to get Protobuf headers into
+// ATen/core (which would require a lot more build system hacking.)
+// If you modify me, keep me synchronized with that file.
+
+#include <c10/macros/Export.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <ostream>
+#include <string>
+
+namespace c10 {
+
+// These contains all device types that also have a BackendComponent
+// and therefore participate in per-backend functionality dispatch keys.
+// This is most backends except PrivateUse2 and PrivateUse3
+#define C10_FORALL_BACKEND_DEVICE_TYPES(_, extra) \
+  _(CPU, extra)                                   \
+  _(CUDA, extra)                                  \
+  _(HIP, extra)                                   \
+  _(XLA, extra)                                   \
+  _(MPS, extra)                                   \
+  _(IPU, extra)                                   \
+  _(XPU, extra)                                   \
+  _(HPU, extra)                                   \
+  _(VE, extra)                                    \
+  _(Lazy, extra)                                  \
+  _(Meta, extra)                                  \
+  _(MTIA, extra)                                  \
+  _(PrivateUse1, extra)
+
+enum class DeviceType : int8_t {
+  CPU = 0,
+  CUDA = 1, // CUDA.
+  MKLDNN = 2, // Reserved for explicit MKLDNN
+  OPENGL = 3, // OpenGL
+  OPENCL = 4, // OpenCL
+  IDEEP = 5, // IDEEP.
+  HIP = 6, // AMD HIP
+  FPGA = 7, // FPGA
+  MAIA = 8, // ONNX Runtime / Microsoft
+  XLA = 9, // XLA / TPU
+  Vulkan = 10, // Vulkan
+  Metal = 11, // Metal
+  XPU = 12, // XPU
+  MPS = 13, // MPS
+  Meta = 14, // Meta (tensors with no data)
+  HPU = 15, // HPU / HABANA
+  VE = 16, // SX-Aurora / NEC
+  Lazy = 17, // Lazy Tensors
+  IPU = 18, // Graphcore IPU
+  MTIA = 19, // Meta training and inference devices
+  PrivateUse1 = 20, // PrivateUse1 device
+  // NB: If you add more devices:
+  //  - Change the implementations of DeviceTypeName and isValidDeviceType
+  //    in DeviceType.cpp
+  //  - Change the number below
+  COMPILE_TIME_MAX_DEVICE_TYPES = 21,
+};
+
+constexpr DeviceType kCPU = DeviceType::CPU;
+constexpr DeviceType kCUDA = DeviceType::CUDA;
+constexpr DeviceType kHIP = DeviceType::HIP;
+constexpr DeviceType kFPGA = DeviceType::FPGA;
+constexpr DeviceType kMAIA = DeviceType::MAIA;
+constexpr DeviceType kXLA = DeviceType::XLA;
+constexpr DeviceType kMPS = DeviceType::MPS;
+constexpr DeviceType kMeta = DeviceType::Meta;
+constexpr DeviceType kVulkan = DeviceType::Vulkan;
+constexpr DeviceType kMetal = DeviceType::Metal;
+constexpr DeviceType kXPU = DeviceType::XPU;
+constexpr DeviceType kHPU = DeviceType::HPU;
+constexpr DeviceType kVE = DeviceType::VE;
+constexpr DeviceType kLazy = DeviceType::Lazy;
+constexpr DeviceType kIPU = DeviceType::IPU;
+constexpr DeviceType kMTIA = DeviceType::MTIA;
+constexpr DeviceType kPrivateUse1 = DeviceType::PrivateUse1;
+
+// define explicit int constant
+constexpr int COMPILE_TIME_MAX_DEVICE_TYPES =
+    static_cast<int>(DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES);
+
+static_assert(
+    COMPILE_TIME_MAX_DEVICE_TYPES <= 21,
+    "Hey!  You seem to be adding a lot of new DeviceTypes.  The intent was "
+    "for this constant to reflect the actual number of DeviceTypes we support "
+    "in PyTorch; it's important that this number is not too large as we "
+    "use this to allocate stack arrays in some places in our code.  If you "
+    "are indeed just adding the 20th device type, feel free to change "
+    "the check to 32; but if you are adding some sort of extensible device "
+    "types registration, please be aware that you are affecting code that "
+    "this number is small.  Try auditing uses of this constant.");
+
+C10_API std::string DeviceTypeName(DeviceType d, bool lower_case = false);
+
+C10_API bool isValidDeviceType(DeviceType d);
+
+C10_API std::ostream& operator<<(std::ostream& stream, DeviceType type);
+
+C10_API void register_privateuse1_backend(const std::string& backend_name);
+C10_API std::string get_privateuse1_backend(bool lower_case = true);
+
+C10_API bool is_privateuse1_backend_registered();
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::DeviceType> {
+  std::size_t operator()(c10::DeviceType k) const {
+    return std::hash<int>()(static_cast<int>(k));
+  }
+};
+} // namespace std
+
+namespace torch {
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using c10::DeviceType;
+} // namespace torch
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DispatchKey.h b/lib/python3.11/site-packages/torch/include/c10/core/DispatchKey.h
new file mode 100644
index 0000000000000000000000000000000000000000..fc5bdabd18fdd424fdb6f1c093da1184742fe97c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DispatchKey.h
@@ -0,0 +1,747 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <ostream>
+#include <string>
+
+namespace c10 {
+
+// Semantically, each value of BackendComponent identifies a "backend" for our
+// dispatch. Some functionalities that we may dispatch to are allowed to
+// register different handlers for each backend. The BackendComponent is then
+// used to figure out which backend implementation to dispatch to.
+
+// In implementation terms, the backend component identifies a specific "bit" in
+// a DispatchKeySet. The bits in the DispatchKeySet are split between the bottom
+// ~12 "BackendComponent" bits, while the remaining upper bits are assigned to
+// functionalities. When we encounter a functionality bit that is known to be
+// customizable per-backend, then we also look at the lower BackendComponent
+// bits and take the highest bit to determine which backend's implementation to
+// use.
+
+// WARNING!  If you add a new backend component to the end of this list,
+// make sure you register it before Meta.
+// Meta must be at the end so that meta key in tls triggers meta kernels.
+// (But you shouldn't: private use keys should have higher precedence than all
+// built-in keys)
+
+// If you add a new (non-privateuse) backend here,
+// make sure to add an Autograd<Backend> fallthrough kernel
+// in aten/src/ATen/core/VariableFallbackKernel.cpp
+
+#define C10_FORALL_BACKEND_COMPONENTS(_, extra) \
+  _(CPU, extra)                                 \
+  _(CUDA, extra)                                \
+  _(HIP, extra)                                 \
+  _(XLA, extra)                                 \
+  _(MPS, extra)                                 \
+  _(IPU, extra)                                 \
+  _(XPU, extra)                                 \
+  _(HPU, extra)                                 \
+  _(VE, extra)                                  \
+  _(Lazy, extra)                                \
+  _(MTIA, extra)                                \
+  _(PrivateUse1, extra)                         \
+  _(PrivateUse2, extra)                         \
+  _(PrivateUse3, extra)                         \
+  _(Meta, extra)
+
+// WARNING!  If we add a new per-backend functionality key that has higher
+// priority than Autograd, then make sure you update EndOfRuntimeBackendKeys
+
+#define C10_FORALL_FUNCTIONALITY_KEYS(_) \
+  _(Dense, )                             \
+  _(Quantized, Quantized)                \
+  _(Sparse, Sparse)                      \
+  _(SparseCsr, SparseCsr)                \
+  _(NestedTensor, NestedTensor)          \
+  _(AutogradFunctionality, Autograd)
+
+enum class BackendComponent : uint8_t {
+
+  // A "backend" is colloquially used to refer to handlers for dispatch
+  // which actually implement the numerics of an operation in question.
+  //
+  // Due to the nature of the enum, these backends are specified in
+  // an ordered way, but for most backends this order is not semantically
+  // meaningful (e.g., it's valid to reorder these backends without changing
+  // semantics).  The only situation when backend ordering is meaningful
+  // is when the backend participates in multiple dispatch with another
+  // backend; e.g., CPU and CUDA (cuda must have higher priority).
+
+  // These keys don't correspond to individual kernels.
+  // Instead, they represent the backends that are allowed to override specific
+  // pieces of functionality:
+  // - dense kernels (e.g. DispatchKey::CPU)
+  // - sparse kernels (e.g. DispatchKey::SparseCPU)
+  // - quantized kernels (e.g. DispatchKey::QuantizedCPU)
+  // - autograd kernels (e.g. DispatchKey::AutogradCPU)
+  // We reserve space in the runtime operator table for this full cross product
+  // of
+  // [backends in this enum] x [keys below that are explicitly marked as having
+  // per-backend functionality]
+  //
+  // A meta tensor is a tensor without any data associated with it.  (They
+  // have also colloquially been referred to as tensors on the "null" device).
+  // A meta tensor can be used to dry run operators without actually doing any
+  // computation, e.g., add on two meta tensors would give you another meta
+  // tensor with the output shape and dtype, but wouldn't actually add anything.
+
+  InvalidBit = 0,
+#define DEFINE_BACKEND_COMPONENT(n, _) n##Bit,
+  C10_FORALL_BACKEND_COMPONENTS(DEFINE_BACKEND_COMPONENT, unused)
+#undef DEFINE_BACKEND_COMPONENT
+
+  // Define an alias to represent end of backend dispatch keys.
+  // If you add new backend keys after PrivateUse3, please also update it here.
+  EndOfBackendKeys = MetaBit,
+};
+
+// Semantically, a dispatch key identifies a possible "level" in our
+// dispatch, for which a handler may be registered. Each handler corresponds
+// to a type of functionality.
+//
+// In implementation terms, the dispatch key identifies a specific "bit" in a
+// DispatchKeySet.  Higher bit indexes get handled by dispatching first (because
+// we "count leading zeros" when we extract the highest priority dispatch
+// key.)
+//
+// Note [DispatchKey Classification]
+// This enum actually contains several types of keys, which are explained
+// in more detail further down:
+// (1) non-customizable backends (e.g. FPGA)
+// (2) non-customizable functionalities (e.g. Functionalize)
+// (3) functionalized that are customizable per backend (e.g. Dense, Sparse,
+// AutogradFunctionality) (4) per-backend instances of customizable
+// functionalities (e.g. CPU, SparseCPU, AutogradCPU) (5) alias keys (e.g.
+// CompositeImplicitAutograd)
+//
+// Of the categories above, it's important to note:
+// (a) which keys are assigned individual bits in a DispatchKeySet
+// (b) which keys are assigned individual slots in the runtime operator table
+// ("Runtime keys")
+//
+// (1), (2) and (3) all get their own dedicated bits in the DispatchKeySet.
+// (1), (2) and (4) all get their own dedicated slots in the runtime operator
+// table.
+
+// See Note [DispatchKeySet Internal Representation] for more details.
+//
+// NOTE: Keep the list in sync with `DispatchKey` in torchgen/model.py
+enum class DispatchKey : uint16_t {
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~ UNDEFINED ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // This is not a "real" functionality, but it exists to give us a "nullopt"
+  // element we can return for cases when a DispatchKeySet contains no elements.
+  // You can think a more semantically accurate definition of DispatchKey is:
+  //
+  //    using DispatchKey = std::optional<RealDispatchKey>
+  //
+  // and Undefined == nullopt.  We didn't actually represent
+  // it this way because std::optional<RealDispatchKey> would take two
+  // words, when DispatchKey fits in eight bits.
+
+  Undefined = 0,
+
+  // Define an alias for Undefined to represent CatchAll (long term
+  // this will get eliminated, but for now it's convenient)
+  CatchAll = Undefined,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~ Functionality Keys ~~~~~~~~~~~~~~~~~~~~~~ //
+  // Every value in the enum (up to EndOfFunctionalityKeys)
+  // corresponds to an individual "functionality" that can be dispatched to.
+  // This is represented in the DispatchKeySet by assigning each of these enum
+  // values
+  // to each of the remaining (64 - len(BackendComponent)) bits.
+  //
+  // Most of these functionalities have a single handler assigned to them,
+  // making them "runtime keys".
+  // That map to a single slot in the runtime operator table.
+  //
+  // A few functionalities are allowed to be customizable per backend.
+  // See [Note: Per-Backend Functionality Dispatch Keys] for details.
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Dense,
+
+  // Below are non-extensible backends.
+  // These are backends that currently don't have their own overrides for
+  // Autograd/Sparse/Quantized kernels,
+  // and we therefore don't waste space in the runtime operator table allocating
+  // space for them.
+  // If any of these backends ever need to customize, e.g., Autograd, then we'll
+  // need to add a DispatchKey::*Bit for them.
+
+  // TODO: put this in BackendComponents
+  FPGA, // Xilinx support lives out of tree at
+  // https://gitlab.com/pytorch-complex/vitis_kernels
+
+  // TODO: put this in BackendComponents
+  // MAIA backend lives out of tree
+  // - test/cpp_extensions/maia_extension.cpp
+  // - test/test_torch.py
+  // - aten/src/ATen/test/extension_backend_test.cpp
+  MAIA,
+
+  Vulkan, // TODO: put this in BackendComponents
+  Metal, // TODO: put this in BackendComponents
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Quantized,
+
+  // This backend is to support custom RNGs; it lets you go
+  // to a different kernel if you pass in a generator that is not a
+  // traditional CPUGeneratorImpl/CUDAGeneratorImpl.  To make use of this
+  // key:
+  //  1) set it as a second parameter of at::Generator constructor call in
+  //     the user-defined PRNG class.
+  //  2) use it as a dispatch key while registering custom kernels
+  //     (templatized kernels specialized for user-defined PRNG class)
+  // intended for out of tree use; tested by aten/src/ATen/test/rng_test.cpp
+  CustomRNGKeyId,
+
+  // TODO: Make Mkldnn a functionality key, so we can give it Meta
+  // support
+  // Here are backends which specify more specialized operators
+  // based on the layout of the tensor.  Note that the sparse backends
+  // are one case where ordering matters: sparse multi-dispatches with
+  // the corresponding dense tensors, and must be handled before them.
+  MkldnnCPU, // registered at build/aten/src/ATen/RegisterMkldnnCPU.cpp
+  // NB: not to be confused with MKLDNN, which is Caffe2 only
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Sparse,
+
+  SparseCsr,
+
+  NestedTensor,
+
+  // In some situations, it is not immediately obvious what the correct
+  // backend for function is, because the function in question doesn't
+  // have any "tensor" arguments.  In this case, a BackendSelect function
+  // can be registered to implement the custom determination of the
+  // correct backend.
+  BackendSelect,
+
+  Python,
+
+  // Out-of-core key for Fake Tensor in torchdistx.
+  // See https://pytorch.org/torchdistx/latest/fake_tensor.html
+  // TODO: delete this in favor of Python-implemented fake tensor
+  Fake,
+  // See Note [Out-of-tree vmap+grad prototype]. The purpose of this key
+  // is to insert code after the "autograd subsystem" runs, so this key should
+  // be directly after ADInplaceOrView and all of the autograd keys.
+  FuncTorchDynamicLayerBackMode,
+
+  // Alias and mutation removal.
+  // If some backends want to opt into only alias removal or only mutation
+  // removal,
+  // we can consider adding separate keys dedicated to those individual passes.
+  // See Note [Functionalization Pass In Core] for details.
+  Functionalize,
+
+  // The named dispatch key is set for any tensors with named dimensions.
+  // Although we have a dispatch key for named tensors, for historical reasons,
+  // this dispatch key doesn't do any of the substantive functionality for named
+  // tensor (though, hypothetically, it could!)  At the moment, it's just
+  // responsible for letting us give good error messages when operations
+  // don't support named tensors.
+  //
+  // NB: If you ever consider moving named tensor functionality into
+  // this dispatch key, note that it might be necessary add another dispatch
+  // key that triggers before composite operators, in case a composite operator
+  // has named dimension propagation that doesn't match that of its
+  // constituent parts.
+  // TODO: delete this once torchdim lands in functorch
+  Named,
+
+  // The Conjugate dispatch key is set for any tensors that need to perform
+  // conjugation
+  // This is implemented at a dispatch level right before any backends run
+  Conjugate,
+
+  // The Negative dispatch key is set for any tensors that need to perform
+  // negation
+  // This is implemented at a dispatch level right before any backends run
+  Negative,
+
+  ZeroTensor, // registered at build/aten/src/ATen/RegisterZeroTensor.cpp
+
+  // Note [ADInplaceOrView key]
+  // ADInplaceOrView key is used by inplace or view ops to register a kernel
+  // that does additional setup for future autograd computation.
+  //
+  // 1. For inplace ops this kernel does version bump
+  // 2. For view ops this kernel does `as_view` setup where we properly setup
+  //    DifferentiableViewMeta on the view tensors.
+  //
+  // For other ops it's fallthrough kernel since there's no extra
+  // work to do.
+  //
+  // Note [Dream: skip VariableType kernel when requires_grad=false]
+  //
+  // In an ideal world where we can skip VariableType kernel for inputs
+  // with requires_grad=false, instead of a fallthrough kernel, we'll
+  // register a kernel shown below to all functional ops as well:
+  // torch::Tensor my_functional_op(...) {
+  //   {
+  //     // Note for every op in VariableType, you need to go through
+  //     // `AutoDispatchBelowADInplaceOrView` guard exactly once to add the
+  //     // key to TLS excluded set. If you don't go through it at all,
+  //     // inplace/view ops called through `at::` inside your backend
+  //     // kernel will dispatch to ADInplaceOrView kernels and do a lot
+  //     // of extra work.
+  //     at::AutoDispatchBelowADInplaceOrView guard;
+  //     at::redispatch::my_functional_op(...);
+  //   }
+  // }
+  // But this work is currently blocked since it adds an extra dispatch
+  // for all ops and it's non-trivial overhead at model level(a few percents).
+  // Thus our current approach takes advantage of the fact every kernel go
+  // through VariableType kernel first and pulls the
+  // `at::AutoDispatchBelowADInplaceOrView` guard of functional ops
+  // up to the `VariableType` kernel. Thus we only add the extra dispatch
+  // to view/inplace ops to minimize its perf impact to real models.
+  ADInplaceOrView,
+  // Note [Alias Dispatch Key : Autograd]
+  // All backends are oblivious to autograd; autograd is handled as a
+  // layer which happens on top of all backends. It inspects the autograd
+  // metadata of all inputs, determines what autograd metadata should be
+  // constructed by the output, and otherwise defers to the backend to
+  // actually do the numeric computation.  Autograd contains
+  // the bulk of this logic.
+
+  // Autograd is now an alias dispatch key which by default maps to all
+  // backend-specific autograd keys.
+  // Backend-specific allow backends to override the default kernel registered
+  // to Autograd key as needed.
+  // For example, XLA wants to define autograd for einsum directly.
+  // Registering a custom autograd implementation at the XLA key won't work
+  // because we process Autograd before XLA.  This key has higher priority and
+  // gets processed first.  You generally should NOT redispatch after handling
+  // autograd here (since that would result in execution of the Autograd
+  // operator, which you're trying to skip).  In AutogradXLA implementations,
+  // you are responsible for handling autograd yourself, or deferring to other
+  // operators which support autograd.
+
+  // Currently we only have backend-specific autograd keys for CPU/CUDA/XLA and
+  // reserved user-defined backends. All other in-tree backends share the
+  // AutogradOther key. We can add specific autograd key for those backends
+  // upon request.
+  AutogradOther,
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  AutogradFunctionality,
+
+  // NestedTensor is an example of something that isn't a "real backend"
+  // (because it mostly consists of redispatching kernels)
+  // but it would like to override autograd functionality in C++.
+  // We can handle cases like this by adding an extra functionality key
+  // exclusively for handling autograd for NestedTensor.
+  // lives out of tree at
+  // https://github.com/pytorch/nestedtensor
+  AutogradNestedTensor,
+
+  Tracer,
+
+  // TODO: make Autocast a functionality key
+  // Autocasting precedes VariableTypeId, to ensure casts are autograd-exposed
+  // and inputs are saved for backward in the post-autocast type.
+  AutocastCPU,
+  AutocastXPU,
+  AutocastIPU,
+  AutocastHPU,
+  AutocastXLA,
+  // AutocastXLA is only being used for TPUs. XLA GPUs continue to use
+  // AutocastCUDA.
+  AutocastMPS,
+  AutocastCUDA,
+  AutocastPrivateUse1,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~ WRAPPERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // There are a number of alternative modes which may want to handle before
+  // autograd; for example, error checking, tracing, profiling or vmap.  They
+  // go here.
+
+  FuncTorchBatched, // See Note [Out-of-tree vmap+grad prototype]
+
+  // Dispatch key for BatchedTensorImpl wrapping a nested tensor.
+  BatchedNestedTensor,
+
+  FuncTorchVmapMode, // See Note [Out-of-tree vmap+grad prototype]
+
+  // This is the dispatch key for BatchedTensorImpl, which is used to implement
+  // batching rules for vmap.
+  Batched,
+
+  // When we are inside a vmap, all tensors dispatch on this key.
+  // See Note: [DispatchKey::VmapMode usage] for more details.
+  VmapMode,
+
+  FuncTorchGradWrapper, // See Note [Out-of-tree vmap+grad prototype]
+
+  // Out-of-core key for Deferred Module Initialization in torchdistx.
+  // See https://pytorch.org/torchdistx/latest/deferred_init.html
+  DeferredInit,
+
+  // Used by Python key logic to know the set of tls on entry to the dispatcher
+  // This kernel assumes it is the top-most non-functorch-related DispatchKey.
+  // If you add a key above, make sure to update the fallback implementation for
+  // this.
+  PythonTLSSnapshot,
+
+  // This key should be at the very top of the dispatcher
+  FuncTorchDynamicLayerFrontMode, // See Note [Out-of-tree vmap+grad prototype]
+
+  // TESTING: This is intended to be a generic testing tensor type id.
+  // Don't use it for anything real; its only acceptable use is within a single
+  // process test.  Use it by creating a TensorImpl with this DispatchKey, and
+  // then registering operators to operate on this type id.  See
+  // aten/src/ATen/core/dispatch/backend_fallback_test.cpp for a usage example.
+  TESTING_ONLY_GenericWrapper,
+
+  // TESTING: This is intended to be a generic testing tensor type id.
+  // Don't use it for anything real; its only acceptable use is within a ingle
+  // process test.  Use it by toggling the mode on and off via
+  // TESTING_ONLY_tls_generic_mode_set_enabled and then registering operators
+  // to operate on this type id.  See
+  // aten/src/ATen/core/dispatch/backend_fallback_test.cpp
+  // for a usage example
+  TESTING_ONLY_GenericMode,
+
+  // This key is used for pre-dispatch tracing in make_fx.
+  // It has lower priority than the PythonDispatcher key
+  // because we use the PythonDispatcher to intercept the key from python,
+  // and avoid having to implement it in C++.
+  PreDispatch,
+
+  // This is a bypass that allows you to skip running the C++ dispatcher
+  // entirely
+  PythonDispatcher,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FIN ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  EndOfFunctionalityKeys, // End of functionality keys.
+
+// ~~~~~~~~~~~~~~ "Dense" Per-Backend Dispatch keys ~~~~~~~~~~~~~~~~~~~~ //
+// Here are backends which you think of as traditionally specifying
+// how to implement operations on some device.
+
+#define DEFINE_PER_BACKEND_KEYS_FOR_BACKEND(n, prefix) prefix##n,
+
+#define DEFINE_PER_BACKEND_KEYS(fullname, prefix)      \
+  StartOf##fullname##Backends,                         \
+      C10_FORALL_BACKEND_COMPONENTS(                   \
+          DEFINE_PER_BACKEND_KEYS_FOR_BACKEND, prefix) \
+          EndOf##fullname##Backends = prefix##Meta,
+
+  C10_FORALL_FUNCTIONALITY_KEYS(DEFINE_PER_BACKEND_KEYS)
+
+#undef DEFINE_PER_BACKEND_KEYS
+#undef DEFINE_PER_BACKEND_KEYS_FOR_BACKEND
+
+      EndOfRuntimeBackendKeys = EndOfAutogradFunctionalityBackends,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~ Alias Dispatch Keys ~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // Note [Alias Dispatch Keys]
+  // Alias dispatch keys are synthetic dispatch keys which map to multiple
+  // runtime dispatch keys. Alisa keys have precedence, but they are always
+  // lower precedence than runtime keys. You can register a kernel to an
+  // alias key, the kernel might be populated to the mapped runtime keys
+  // during dispatch table computation.
+  // If a runtime dispatch key has multiple kernels from alias keys, which
+  // kernel wins is done based on the precedence of alias keys (but runtime
+  // keys always have precedence over alias keys).
+  // Alias keys won't be directly called during runtime.
+
+  // See Note [Alias Dispatch Key : Autograd]
+  Autograd,
+  CompositeImplicitAutograd, // registered at
+  // build/aten/src/ATen/RegisterCompositeImplicitAutograd.cpp
+
+  // Note: The alias keyset for FuncTorchBatchedDecomposition is disjoint from
+  // all
+  // other alias keysets
+  // and so precedence order doesn't matter
+  FuncTorchBatchedDecomposition, // registered at
+  // build/aten/src/ATen/RegisterFuncTorchBatchedDecomposition.cpp
+  // Note: The alias keyset for CompositeImplicitAutogradNestedTensor is
+  // disjoint from all other alias keysets
+  CompositeImplicitAutogradNestedTensor, // registered at
+  // build/aten/src/ATen/RegisterCompositeImplicitAutogradNestedTensor.cpp
+  CompositeExplicitAutograd, // registered at
+  // build/aten/src/ATen/RegisterCompositeExplicitAutograd.cpp
+  // See Note [CompositeExplicitAutogradNonFunctional Key]
+  CompositeExplicitAutogradNonFunctional, // registered at
+  // build/aten/src/ATen/RegisterCompositeExplicitAutograd.cpp
+
+  // Define an alias key to represent end of alias dispatch keys.
+  // If you add new alias keys after Autograd, please also update it here.
+  StartOfAliasKeys = Autograd,
+  EndOfAliasKeys = CompositeExplicitAutogradNonFunctional, //
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~ BC ALIASES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // The aliases exist for backwards compatibility reasons, they shouldn't
+  // be used
+  CPUTensorId = CPU,
+  CUDATensorId = CUDA,
+  DefaultBackend = CompositeExplicitAutograd,
+  PrivateUse1_PreAutograd = AutogradPrivateUse1,
+  PrivateUse2_PreAutograd = AutogradPrivateUse2,
+  PrivateUse3_PreAutograd = AutogradPrivateUse3,
+  Autocast = AutocastCUDA,
+};
+
+// Note [Private use DispatchKey]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Private use tensor IDs are preallocated tensor type IDs for use in user
+// applications.  Similar to private use fields in HTTP, they can be used
+// by end users for experimental or private applications, without needing
+// to "standardize" the tensor ID (which would be done by submitting a PR
+// to PyTorch to add your type ID).
+//
+// Private use tensor IDs are appropriate to use if you want to experiment
+// with adding a new tensor type (without having to patch PyTorch first) or
+// have a private, non-distributed application that needs to make use of a
+// new tensor type.  Private use tensor IDs are NOT appropriate to use for
+// libraries intended to be distributed to further users: please contact
+// the PyTorch developers to get a type ID registered in this case.
+//
+// We provide two classes of private user tensor id: regular DispatchKeys
+// and Autograd DispatchKeys.  DispatchKeys serve the role of ordinary "backend"
+// DispatchKeys; if you were adding support for a new type of accelerator, you
+// would use a backend DispatchKey, and ideally automatically reuse
+// AutogradOther definitions already defined in PyTorch.  AutogradPrivateUse
+// DispatchKeys serve as "wrapper" DispatchKeys: they are only necessary for
+// tensors that compose multiple internal tensors, and for cases when the
+// built-in autograd formulas for operators are not appropriate.
+
+static_assert(
+    (static_cast<uint8_t>(BackendComponent::EndOfBackendKeys) +
+     static_cast<uint8_t>(DispatchKey::EndOfFunctionalityKeys)) <= 64,
+    "The BackendComponent and DispatchKey enums (below EndOfFunctionalityKeys)"
+    " both map to backend and functionality bits"
+    " into a 64-bit bitmask; you must have less than 64 total entries between them");
+
+// Check if a DispatchKey is an alias mapping to other runtime keys.
+constexpr bool isAliasDispatchKey(DispatchKey k) {
+  return k >= DispatchKey::StartOfAliasKeys && k <= DispatchKey::EndOfAliasKeys;
+}
+
+// [Note: Per-Backend Functionality Dispatch Keys]
+// Check if a DispatchKey is a per-backend functionality key
+// Any functionalities that can be customized per-backend should be added here.
+// These keys correspond to functionalities that can be customized individually
+// per backend. While they only take up one bit in the `DispatchKeySet` bitset,
+// they map to (# backends) slots in the operator table.
+// Each of these keys also has a separate set of "runtime keys" in the dispatch
+// key enum, per backend, which *do* map to the individual operator table slots.
+// For example, the "Sparse" key maps to an individual bit in the
+// DispatchKeySet, while `SparseCPU`, `SparseCUDA`, etc all map to individual
+// slots in the runtime operator table.
+
+constexpr bool isPerBackendFunctionalityKey(DispatchKey k) {
+  if (k == DispatchKey::Dense || k == DispatchKey::Quantized ||
+      k == DispatchKey::Sparse || k == DispatchKey::SparseCsr ||
+      k == DispatchKey::AutogradFunctionality ||
+      k == DispatchKey::NestedTensor) {
+    return true;
+  } else {
+    return false;
+  }
+}
+
+// Note that this includes Undefined in the total count.
+// BUT EndOfFunctionalityKeys is its own (placeholder) key.
+// e.g. Undefined=0, Dense=1, Sparse=2, EndOfFunctionalityKeys=3.
+// In the above example, there are 3 total functionality keys.
+constexpr uint8_t num_functionality_keys =
+    static_cast<uint8_t>(DispatchKey::EndOfFunctionalityKeys);
+
+constexpr uint8_t num_backends =
+    static_cast<uint8_t>(BackendComponent::EndOfBackendKeys);
+
+// Note [No More Than 16 Backends]
+// Search for this note to find places in the code where the "no more than 16
+// backends" invariant is baked in.
+static_assert(
+    static_cast<uint8_t>(BackendComponent::EndOfBackendKeys) <= 16,
+    "BackendComponent currently only supports <= 16 backends. If we really need to extend this, \
+there are a few places where this invariant is baked in");
+
+constexpr uint8_t numPerBackendFunctionalityKeys() {
+  uint8_t count = 0;
+  for (uint8_t k = 0; k <= num_functionality_keys; ++k) {
+    if (isPerBackendFunctionalityKey(static_cast<DispatchKey>(k)))
+      ++count;
+  }
+  return count;
+}
+
+#if defined(C10_MOBILE_TRIM_DISPATCH_KEYS)
+// See [Note: Trimmed Mobile Dispatch Keys]
+constexpr uint16_t num_runtime_entries = 8;
+#else
+constexpr uint16_t num_runtime_entries = num_functionality_keys +
+    (numPerBackendFunctionalityKeys() * (num_backends - 1));
+#endif
+
+// See Note [No More Than 16 Backends]
+constexpr uint16_t full_backend_mask =
+    (static_cast<uint16_t>(1) << num_backends) - 1;
+
+C10_API const char* toString(DispatchKey);
+C10_API const char* toString(BackendComponent);
+C10_API std::ostream& operator<<(std::ostream&, DispatchKey);
+C10_API std::ostream& operator<<(std::ostream&, BackendComponent);
+
+C10_API DispatchKey getAutogradKeyFromBackend(BackendComponent k);
+
+// Parses a string into a dispatch key.
+// If the string cannot be correctly parsed, throws an exception.
+C10_API c10::DispatchKey parseDispatchKey(const std::string& k);
+
+// These are some convenience identifiers for dispatch keys which are
+// shorter to type than their long counterparts.  Note that some of these
+// dispatch keys directly correspond to DeviceType; and most APIs that
+// accept DispatchKey also accept DeviceType; e.g.,
+// torch::dispatch(torch::kCPU, ...) is also valid.
+constexpr DispatchKey kAutograd = DispatchKey::Autograd;
+
+// See Note [The Ordering of Per-Backend Dispatch Keys Matters!]
+// This function relies on the invariant that the dispatch keys between
+// StartOfDenseBackends and EndOfRuntimeBackendKeys are ordered by backend
+// in the same order as `BackendComponent`.
+constexpr BackendComponent toBackendComponent(DispatchKey k) {
+  if (k >= DispatchKey::StartOfDenseBackends &&
+      k <= DispatchKey::EndOfDenseBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfDenseBackends));
+  } else if (
+      k >= DispatchKey::StartOfQuantizedBackends &&
+      k <= DispatchKey::EndOfQuantizedBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfQuantizedBackends));
+  } else if (
+      k >= DispatchKey::StartOfSparseBackends &&
+      k <= DispatchKey::EndOfSparseBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfSparseBackends));
+  } else if (
+      k >= DispatchKey::StartOfSparseCsrBackends &&
+      k <= DispatchKey::EndOfSparseCsrBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfSparseCsrBackends));
+  } else if (
+      k >= DispatchKey::StartOfNestedTensorBackends &&
+      k <= DispatchKey::EndOfNestedTensorBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfNestedTensorBackends));
+  } else if (
+      k >= DispatchKey::StartOfAutogradFunctionalityBackends &&
+      k <= DispatchKey::EndOfAutogradFunctionalityBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(
+            DispatchKey::StartOfAutogradFunctionalityBackends));
+  } else {
+    return BackendComponent::InvalidBit;
+  }
+}
+
+constexpr DispatchKey toFunctionalityKey(DispatchKey k) {
+  if (k <= DispatchKey::EndOfFunctionalityKeys) {
+    return k;
+  } else if (k <= DispatchKey::EndOfDenseBackends) {
+    return DispatchKey::Dense;
+  } else if (k <= DispatchKey::EndOfQuantizedBackends) {
+    return DispatchKey::Quantized;
+  } else if (k <= DispatchKey::EndOfSparseBackends) {
+    return DispatchKey::Sparse;
+  } else if (k <= DispatchKey::EndOfSparseCsrBackends) {
+    return DispatchKey::SparseCsr;
+  } else if (k <= DispatchKey::EndOfNestedTensorBackends) {
+    return DispatchKey::NestedTensor;
+  } else if (k <= DispatchKey::EndOfAutogradFunctionalityBackends) {
+    return DispatchKey::AutogradFunctionality;
+  } else {
+    return DispatchKey::Undefined;
+  }
+}
+
+BackendComponent toBackendComponent(DeviceType device_type);
+
+// Given (DispatchKey::Dense, BackendComponent::CUDABit), returns
+// DispatchKey::CUDA.
+// See Note [The Ordering of Per-Backend Dispatch Keys Matters!]
+// This function relies on the invariant that the dispatch keys between
+// StartOfDenseBackends and EndOfRuntimeBackendKeys are ordered by backend
+// in the same order as `BackendComponent`.
+constexpr DispatchKey toRuntimePerBackendFunctionalityKey(
+    DispatchKey functionality_k,
+    BackendComponent backend_k) {
+  if (functionality_k == DispatchKey::Dense) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfDenseBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::Sparse) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfSparseBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::SparseCsr) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfSparseCsrBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::Quantized) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfQuantizedBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::NestedTensor) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfNestedTensorBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::AutogradFunctionality) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(
+            DispatchKey::StartOfAutogradFunctionalityBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  return DispatchKey::Undefined;
+}
+
+} // namespace c10
+
+namespace torch {
+// Expose the constant, but not the TYPE (DispatchKey is an implementation
+// detail!)
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using c10::kAutograd;
+} // namespace torch
+
+// NB: You really shouldn't use this instance; this enum is guaranteed
+// to be pretty small so a regular array should be acceptable.
+namespace std {
+template <>
+struct hash<c10::DispatchKey> {
+  typedef size_t result_type;
+  typedef c10::DispatchKey argument_type;
+
+  size_t operator()(c10::DispatchKey x) const {
+    return static_cast<size_t>(x);
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DispatchKeySet.h b/lib/python3.11/site-packages/torch/include/c10/core/DispatchKeySet.h
new file mode 100644
index 0000000000000000000000000000000000000000..289a88312c916fe79381f3ea357160d72d0f710f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DispatchKeySet.h
@@ -0,0 +1,949 @@
+#pragma once
+#include <c10/core/DispatchKey.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Metaprogramming.h>
+#include <c10/util/TypeList.h>
+#include <c10/util/llvmMathExtras.h>
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <initializer_list>
+#include <iterator>
+#include <ostream>
+#include <string>
+#include <type_traits>
+
+namespace c10 {
+
+struct FunctionalityOffsetAndMask {
+  // empty constructor shouldn't be used; only needed to initialize
+  // the array before populating it.
+  FunctionalityOffsetAndMask() = default;
+  FunctionalityOffsetAndMask(uint16_t offset, uint16_t mask)
+      : offset(offset), mask(mask) {}
+  // This needs to big enough to cover the size of the operator table.
+  uint16_t offset{};
+  // See Note [No More Than 16 Backends]
+  // This mask needs to be big enough to mask all of the backend bits.
+  // We probably don't ever want to have more than 16 backend bits, so uint16_t
+  // should be enough.
+  uint16_t mask{};
+};
+static_assert(
+    c10::num_runtime_entries < 65536,
+    "The dispatcher currently only supports up to 2^16 runtime entries");
+
+C10_API std::array<FunctionalityOffsetAndMask, num_functionality_keys>
+initializeFunctionalityOffsetsAndMasks();
+
+C10_ALWAYS_INLINE static const std::
+    array<FunctionalityOffsetAndMask, num_functionality_keys>&
+    offsetsAndMasks() {
+  static auto offsets_and_masks_ = initializeFunctionalityOffsetsAndMasks();
+  return offsets_and_masks_;
+}
+
+// A representation of a set of DispatchKeys. A DispatchKeySet contains both
+// "functionality" bits and "backend bits", and every tensor holds its own
+// DispatchKeySet. The Dispatcher implements multiple dispatch by grabbing the
+// keyset on every input tensor, or’ing them together, and dispatching to a
+// specific piece of functionality. The functionality bits are *ordered*. When
+// multiple functionality bits are set, we use the highest priority
+// functionality. Similarly, multiple backend bits can theoretically be set if
+// you call an operator with multiple tensors from difference devices (e.g. CPU
+// and CUDA), although support for mixed device dispatch is limited (the only
+// kernels that gracefully handle mixed device inputs for now are cuda kernels
+// that take in a scalar cpu tensor).
+
+// A representation of a set of DispatchKeys.  A tensor may have multiple
+// tensor type ids, e.g., a Variable tensor can also be a CPU tensor; the
+// DispatchKeySet specifies what type ids apply.  The internal representation is
+// as a 64-bit bit set (this means only 64 tensor type ids are supported).
+//
+// As mentioned above, DispatchKeys are ordered; thus, we can ask questions like
+// "what is the highest priority DispatchKey in the set"?  (The set itself is
+// not ordered; two sets with the same ids will always have the ids ordered in
+// the same way.)
+//
+// Note [DispatchKeySet Internal Representation]
+// Internally, dispatch keys are packed into 64-bit DispatchKeySet objects
+// that get passed around at runtime.
+// However, there isn't necessarily a 1-to-1 mapping between bits in the keyset
+// and individual dispatch keys.
+//
+// First: why do we have this distinction, and why not map every dispatch key
+// directly to a bit? This is mostly because we have several types of
+// functionalities that different backends would like to customize. For example,
+// we have:
+// - "Dense":     CPU, CUDA, XLA, ... (~12 keys)
+// - "Sparse":    SparseCPU, SparseCUDA, ...
+// - "SparseCsr": SparseCsrCPU, SparseCsrCUDA, ...
+// - "Quantized": QuantizedCPU, QuantizedCUDA, QuantizedXLA, ...
+// - "Autograd":  AutogradCPU, AutogradCUDA, Autograd XLA, ...
+// The problem is that total number of keys grows quadratically with [#
+// backends] x [# functionalities], making it very difficult to map each key
+// directly to a bit in a bitset without dramatically increasing the size of the
+// bitset over time.
+//
+// The two enums (BackendComponent and DispatchKey) can be divided roughly into
+// 5 categories.
+//
+// (1) "Building block" keys
+//    (a) backends: Everything in the BackendComponent enum (e.g. CPUBit,
+//    CUDABit) (b) functionalities: (per-backend) functionality-bit DispatchKeys
+//    (e.g. AutogradFunctionality, SparseCsr, Sparse, Dense)
+// (2) "Runtime" keys
+//    (a) "non-customizable backends" (e.g. FPGA)
+//    (b) "non-customizable functionalities" (e.g. Functionalize)
+//    (c) "per-backend instances of customizable functionalities" (e.g. CPU,
+//    SparseCPU, AutogradCPU)
+// (3) "Alias" DispatchKeys (see Note [Alias Dispatch Keys])
+//
+// (1) Building block keys always correspond to individual bits in a
+// DispatchKeySet. They can also be combined in a DispatchKeySet to form actual
+// runtime keys. e.g.
+//     auto dense_cpu_ks = DispatchKeySet({DispatchKey::CPUBit,
+//     DispatchKey::Dense});
+//     // The keyset has the runtime dense-cpu key.
+//     dense_cpu_ks.has(DispatchKey::CPU);
+//     // And it contains the building block keys too.
+//     dense_cpu_ks.has(DispatchKey::CPUBit);
+//     dense_cpu_ks.has(DispatchKey::Dense);
+//
+// Not every backend and not every functionality counts as a "building block
+// key". This is mostly to give us more levers to pull in the design space.
+// Backend keys and functionality keys that count as "building blocks" will
+// contribute to a full cross product of functionality that can be overriden.
+//
+// For example, right now we have at least 12 "backend" building
+// blocks (CPU, CUDA, XLA, ...) and at least 5 "functionality"
+// building blocks (Dense, Sparse, SparseCsr, Quantized,
+// AutogradFunctionality, ...). These keys together allow every
+// dispatcher operator to be customized in up to 12*4 different
+// ways. Each of those requires a slot in the operator table of every
+// dispatcher operator.  Not every piece of functionality necessarily
+// needs to be customizable per-backend, and not every backend
+// necessarily needs to be able to customize every type of
+// functionality.
+//
+//
+// (2) Every runtime key corresponds directly to a slot in an operator's runtime
+// dispatch table, and you can directly register kernels to a runtime dispatch
+// key.
+//
+// For per-backend functionalities like "Dense" or "AutogradFunctionality",
+// you can think of the corresponding runtime dispatch keys as "instances" of
+// that functionality, per backend. E.g. "CPU", "CUDA", "XLA", etc. are all
+// runtime instances of the "Dense" building block key.
+
+// (2a) and (2b) are represented identically in the DispatchKeySet logic:
+// - backend-agnostic functionalities (e.g. FuncTorchBatched) are NOT
+// customizable per backend.
+//   In order to do so, we'd need to promote it to a per-backend functionality
+//   "building block" key.
+// - non-customizable backends (e.g. FPGA) can NOT customize existing
+// functionality like Sparse, Autograd, etc.
+//   In order to do so, we'd need to promote it to a backend "building block"
+//   key.
+//
+// In both cases, these keys directly correspond to runtime slots in the
+// operator table.
+//
+//
+// (3) "Alias" keys
+// See Note [Alias Dispatch Keys]
+//
+// Final note: for anyone making future changes to the Dispatcher +
+// DispatchKeySet internals, there's a closed PR with a basic
+// python-implementation of the Dispatcher that might be useful in quickly
+// testing out and validating changes. See it at
+// https://github.com/pytorch/pytorch/pull/68743
+
+// An undefined tensor is one with an empty tensor type set.
+class DispatchKeySet final {
+ public:
+  enum Full { FULL };
+  enum FullAfter { FULL_AFTER };
+  enum Raw { RAW };
+
+  // NB: default constructor representation as zero is MANDATORY as
+  // use of DispatchKeySet in TLS requires this.
+  constexpr DispatchKeySet() = default;
+
+  constexpr DispatchKeySet(Full)
+      : repr_((1ULL << (num_backends + num_functionality_keys - 1)) - 1) {}
+
+  constexpr DispatchKeySet(FullAfter, DispatchKey t)
+      // LSB after t are OK, but not t itself.
+      // "functionalities" have a notion of ordering (e.g. Autograd > Sparse >
+      // Quantized > Dense). But backends don't really have an ordering.
+      // Therefore, we're enforcing that FullAfter can only be used on
+      // "functionality" keys.
+      : repr_(
+            (1ULL
+             << (num_backends + static_cast<uint8_t>(toFunctionalityKey(t)) -
+                 1)) -
+            1) {
+    *this = add(DispatchKey::PythonDispatcher);
+  }
+
+  // Public version of DispatchKeySet(uint64_t) API; external users
+  // must be explicit when they do this!
+  constexpr DispatchKeySet(Raw, uint64_t x) : repr_(x) {}
+
+  constexpr explicit DispatchKeySet(BackendComponent k) {
+    if (k == BackendComponent::InvalidBit) {
+      repr_ = 0;
+    } else {
+      repr_ = 1ULL << (static_cast<uint8_t>(k) - 1);
+    }
+  }
+
+  constexpr explicit DispatchKeySet(DispatchKey k) {
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    if (k == DispatchKey::Undefined) {
+      // Case 1: handle Undefined specifically
+      repr_ = 0;
+    } else if (k <= DispatchKey::EndOfFunctionalityKeys) {
+      // Case 2: handle "functionality-only" keys
+      // These keys have a functionality bit set, but no backend bits
+      // These can technically be either:
+      // - valid runtime keys (e.g. DispatchKey::AutogradOther,
+      // DispatchKey::FuncTorchBatched, etc)
+      // - "building block" keys that aren't actual runtime keys (e.g.
+      // DispatchKey::Dense or Sparse)
+      uint64_t functionality_val = 1ULL
+          << (num_backends + static_cast<uint8_t>(k) - 1);
+      repr_ = functionality_val;
+    } else if (k <= DispatchKey::EndOfRuntimeBackendKeys) {
+      // Case 3: "runtime" keys that have a functionality bit AND a backend bit.
+      // First compute which bit to flip for the functionality.
+      auto functionality_k = toFunctionalityKey(k);
+      // The - 1 is because Undefined is technically a "functionality" that
+      // doesn't show up in the bitset. So e.g. Dense is technically the second
+      // functionality, but the lowest functionality bit.
+      uint64_t functionality_val = 1ULL
+          << (num_backends + static_cast<uint8_t>(functionality_k) - 1);
+
+      // then compute which bit to flip for the backend
+      // Case 4a: handle the runtime instances of "per-backend functionality"
+      // keys For example, given DispatchKey::CPU, we should set:
+      // - the Dense functionality bit
+      // - the CPUBit backend bit
+      // first compute which bit to flip for the backend
+      auto backend_k = toBackendComponent(k);
+      uint64_t backend_val = backend_k == BackendComponent::InvalidBit
+          ? 0
+          : 1ULL << (static_cast<uint8_t>(backend_k) - 1);
+      repr_ = functionality_val + backend_val;
+    } else {
+      // At this point, we should have covered every case except for alias keys.
+      // Technically it would be possible to add alias dispatch keys to a
+      // DispatchKeySet, but the semantics are a little confusing and this
+      // currently isn't needed anywhere.
+      repr_ = 0;
+    }
+  }
+
+  constexpr uint64_t keys_to_repr(std::initializer_list<DispatchKey> ks) {
+    uint64_t repr = 0;
+    for (auto k : ks) {
+      repr |= DispatchKeySet(k).repr_;
+    }
+    return repr;
+  }
+
+  constexpr uint64_t backend_bits_to_repr(
+      std::initializer_list<BackendComponent> ks) {
+    uint64_t repr = 0;
+    for (auto k : ks) {
+      repr |= DispatchKeySet(k).repr_;
+    }
+    return repr;
+  }
+
+  explicit constexpr DispatchKeySet(std::initializer_list<DispatchKey> ks)
+      : repr_(keys_to_repr(ks)) {}
+
+  explicit constexpr DispatchKeySet(std::initializer_list<BackendComponent> ks)
+      // Note: for some reason, putting this logic directly in the constructor
+      // appears to fail to compile on CUDA 10.1.
+      // See an example internal failure at
+      // https://www.internalfb.com/intern/skycastle/run/76561193669136035/artifact/actionlog.76561193742069401.stderr
+      : repr_(backend_bits_to_repr(ks)) {}
+
+  // Test if a DispatchKey is in the set
+  inline bool has(DispatchKey t) const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(t != DispatchKey::Undefined);
+    return has_all(DispatchKeySet(t));
+  }
+  constexpr bool has_backend(BackendComponent t) const {
+    return has_all(DispatchKeySet(t));
+  }
+
+  // Test if a DispatchKey is in the set
+  // Given a DispatchKeySet of functionality keys and (potentially) backend
+  // keys, tests if all of them are in the current set.
+  constexpr bool has_all(DispatchKeySet ks) const {
+    return static_cast<bool>((repr_ & ks.repr_) == ks.repr_);
+  }
+
+  // Given a DispatchKeySet of functionality keys and (potentially) backend
+  // keys, tests if any of them are in the current set. This could technically
+  // be pretty easily implemented using has(). It is strictly a perf
+  // optimization though. There are many places in the code base where we want
+  // to test for multiple functionality keys together. HOWEVER, runtime
+  // per-backend functionality keys aren't allowed to be used with this
+  // function, because you can end up with weird results. e.g.
+  // DispatchKeySet(DispatchKey::AutogradCPU).has_any(DispatchKeySet(DispatchKey::CPU))
+  // would return true.
+  inline bool has_any(DispatchKeySet ks) const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        // Either there are no backend bits in the input keyset
+        ((ks.repr_ & full_backend_mask) == 0) ||
+        // or there are no per-backend-functionality bits
+        // See [Note: Per-Backend Functionality Dispatch Keys]
+        ((ks &
+          DispatchKeySet({
+                             DispatchKey::Dense,
+                             DispatchKey::Quantized,
+                             DispatchKey::Sparse,
+                             DispatchKey::SparseCsr,
+                             DispatchKey::AutogradFunctionality,
+                         })
+              .repr_) == 0));
+    return static_cast<bool>((repr_ & ks.repr_) != 0);
+  }
+  // Test if DispatchKeySet is a superset of ks.
+  bool isSupersetOf(DispatchKeySet ks) const {
+    return (repr_ & ks.repr_) == ks.repr_;
+  }
+  // Perform set union
+  constexpr DispatchKeySet operator|(DispatchKeySet other) const {
+    return DispatchKeySet(repr_ | other.repr_);
+  }
+  // Perform set intersection
+  constexpr DispatchKeySet operator&(DispatchKeySet other) const {
+    return DispatchKeySet(repr_ & other.repr_);
+  }
+  // Compute the set difference self - other,
+  // but ONLY for the functionality keys.
+  // Any backend bits set on self will remain unchanged.
+  // See Note [Removing keys from DispatchKeySet Only Affects Functionality
+  // Keys]
+  constexpr DispatchKeySet operator-(DispatchKeySet other) const {
+    return DispatchKeySet(repr_ & (full_backend_mask | ~other.repr_));
+  }
+
+  // Compute self ^ other
+  constexpr DispatchKeySet operator^(DispatchKeySet other) const {
+    return DispatchKeySet(repr_ ^ other.repr_);
+  }
+  bool operator==(DispatchKeySet other) const {
+    return repr_ == other.repr_;
+  }
+  bool operator!=(DispatchKeySet other) const {
+    return repr_ != other.repr_;
+  }
+  // Add a DispatchKey to the DispatchKey set.  Does NOT mutate,
+  // returns the extended DispatchKeySet!
+  [[nodiscard]] constexpr DispatchKeySet add(DispatchKey t) const {
+    return *this | DispatchKeySet(t);
+  }
+  [[nodiscard]] constexpr DispatchKeySet add(DispatchKeySet ks) const {
+    return *this | ks;
+  }
+
+  // Remove a DispatchKey from the DispatchKey set.
+  // This is generally not an operation you should be doing
+  // (it's used to implement the printing overload, operator<<)
+  //
+  // Note [Removing keys from DispatchKeySet Only Affects Functionality Keys]
+  // Only functionality bits are allowed to be removed from a keyset.
+  // For now, we're only allowing removal of "functionality bits" from the
+  // keyset, which is specifically needed by the fallthrough key calculation
+  // logic. Why is removing backend bits problematic? Consider this example:
+  //
+  // DispatchKeySet([DispatchKey.CPU, DispatchKey.AutogradCUDA,
+  // DispatchKey.CUDA]).remove(DispatchKey.AutogradCUDA)
+  // DispatchKeySet([DispatchKey.CPU,
+  // DispatchKey.AutogradCUDA]).remove(DispatchKey.AutogradCUDA)
+  //
+  // What do we want to happen?
+  // Technically, we'd like it to be true that after removal,
+  // the first keyset still has the CUDA dispatch key while the second doesn't.
+  // Unfortunately there's no way to represent that, because the two keysets are
+  // represented the same way internally: functionality bits: Autograd, Dense
+  // backend bits: CPU, CUDA
+  //
+  // Instead, remove(DispatchKey.AutogradCPU) will only remove the "Autograd"
+  // bit from the bitset.
+  [[nodiscard]] constexpr DispatchKeySet remove(DispatchKey t) const {
+    return DispatchKeySet(
+        repr_ & ~(DispatchKeySet(t).repr_ & ~full_backend_mask));
+  }
+  // You're allowed to remove a backend bit from a DispatchKeySet,
+  // but you have to be explicit about it (remove_backend() instead of
+  // remove()).
+  constexpr DispatchKeySet remove_backend(BackendComponent b) const {
+    return DispatchKeySet(repr_ & ~(DispatchKeySet(b).repr_));
+  }
+  // Is the set empty?  (AKA undefined tensor)
+  bool empty() const {
+    return repr_ == 0;
+  }
+  uint64_t raw_repr() {
+    return repr_;
+  }
+
+  DispatchKey highestFunctionalityKey() const {
+    auto functionality_idx = indexOfHighestBit();
+    // This means that none of the functionality bits were set.
+    if (functionality_idx < num_backends)
+      return DispatchKey::Undefined;
+    // The first num_backend bits in the keyset don't correspond to real
+    // dispatch keys.
+    return static_cast<DispatchKey>(functionality_idx - num_backends);
+  }
+
+  // This is similar like toBackendComponent(DispatchKey), but less restrictive.
+  // toBackendComponent() errors out if the key that it was passed has no
+  // backend bits, which is useful for error checking. We need a version of that
+  // here that can also handle "fake" backends like FPGA, because they need to
+  // map to the AutogradOther key. For those backends, we return
+  // BackendComponent::InvalidBit.
+  BackendComponent highestBackendKey() const {
+    // mask to mask out functionality bits
+    auto backend_idx =
+        DispatchKeySet(repr_ & full_backend_mask).indexOfHighestBit();
+    // all zeros across the backend bits means that no backend bits are set.
+    if (backend_idx == 0)
+      return BackendComponent::InvalidBit;
+    return static_cast<BackendComponent>(backend_idx);
+  }
+
+  // returns the DispatchKey of highest priority in the set.
+  DispatchKey highestPriorityTypeId() const {
+    auto functionality_k = highestFunctionalityKey();
+    if (isPerBackendFunctionalityKey(functionality_k)) {
+      return toRuntimePerBackendFunctionalityKey(
+          functionality_k, highestBackendKey());
+    }
+    return functionality_k;
+  }
+
+  // Returns the index of the most-significant bit in the keyset.
+  // This is used to as part of the calculation into the operator table to get:
+  // - the highest "functionality" bit in the keyset.
+  // - the highest "backend" bit in the keyset.
+  uint8_t indexOfHighestBit() const {
+    return 64 - llvm::countLeadingZeros(repr_);
+  }
+
+#if defined(C10_MOBILE_TRIM_DISPATCH_KEYS)
+  // [Note: Trimmed Mobile Dispatch Keys]
+  /**
+   * The method below maps the dispatch key in the enum DispatchKey to an
+   * integer index in the dispatchTable_ array in OperatorEntry. The array
+   * is trimmed for mobile to reduce peak memory usage since it's
+   * unnecessary to reserve additional space for dispatch keys that will
+   * never be used on mobile.
+   */
+  int getDispatchTableIndexForDispatchKeySet() const {
+    auto dk = highestPriorityTypeId();
+    switch (dk) {
+      case DispatchKey::Undefined:
+        return 0;
+      case DispatchKey::CPU:
+        return 1;
+      case DispatchKey::QuantizedCPU:
+        return 2;
+      case DispatchKey::SparseCPU:
+        return 3;
+      case DispatchKey::BackendSelect:
+        return 4;
+      case DispatchKey::ADInplaceOrView:
+        return 5;
+      case DispatchKey::AutogradOther:
+        return 6;
+      case DispatchKey::AutogradCPU:
+        return 7;
+      default:
+        return -1;
+    }
+  }
+#else
+  // returns the index in the operator table of highest priority key in the the
+  // keyset Note that we could in theory implement this using
+  // highestPriorityTypeId(), but this code is very hotpath and we can do it
+  // faster without it.
+  int getDispatchTableIndexForDispatchKeySet() const {
+    auto functionality_idx =
+        DispatchKeySet(repr_ >> num_backends).indexOfHighestBit();
+    auto offset_and_mask = offsetsAndMasks()[functionality_idx];
+    // Mask the functionality bits out first, then right-shift by 1.
+    // right-shifting by 1 because everything is zero-indexed.
+    // E.g. 000001 (CPU) should give us an offset of 0, 000010 (CUDA) should
+    // give us an offset of 1, etc.
+    auto backend_idx =
+        DispatchKeySet((repr_ & offset_and_mask.mask) >> 1).indexOfHighestBit();
+    return offset_and_mask.offset + backend_idx;
+  }
+#endif
+
+  // returns the "index" of the highest priority backend in the keyset.
+  // This is pretty similar to getBackendKey(), but:
+  // - It's hotpath code (part of the runtime bitset calculation)
+  // - I's returns an integer index, not an enum value
+  // - Everything is shifted to the right by 1.
+  //   BackendComponent::InvalidBit is technically the lowest enum value,
+  //   but it isn't included in the runtime table. So CPUBit = 1, CUDABit = 2,
+  //   etc.
+  uint64_t getBackendIndex() const {
+    return DispatchKeySet((repr_ & full_backend_mask) >> 1).indexOfHighestBit();
+  }
+
+ private:
+  constexpr DispatchKeySet(uint64_t repr) : repr_(repr) {}
+  uint64_t repr_ = 0;
+
+ public:
+  // STL iterator for DispatchKeySet. Iterates through all runtime DispatchKeys
+  // in the set. The iterator is only invalidated by the destruction of the
+  // underlying DispatchKeySet as the iterator stores a pointer to the raw
+  // representation of the DispatchKeySet. Note: When we encounter a per-backend
+  // functionality (e.g. Dense or Sparse), we will iterate through EVERY backend
+  // in the keyset, for that functionality. For example, if the next
+  // functionality key to iterate over is Autograd, and the backend bits in the
+  // keyset correspond to [BackendComponent::CPUBit, BackendComponent::CUDABit],
+  // then the next two keys we return will be DispatchKey::AutogradCPU,
+  // DispatchKey::AutogradCUDA (CPU first because it has lower precedence than
+  // CUDA in DispatchKey.h).
+  class iterator {
+   public:
+    using self_type = iterator;
+    using iterator_category = std::input_iterator_tag;
+    using value_type = DispatchKey;
+    using difference_type = ptrdiff_t;
+    using reference = value_type&;
+    using pointer = value_type*;
+    // final mask value should mask out the entire keyset
+    static const uint8_t end_iter_mask_val =
+        num_backends + num_functionality_keys;
+    // final key value should be the last DispatchKey
+    static const uint8_t end_iter_key_val = num_functionality_keys;
+
+    // current_dispatchkey_idx_ will iterate through all functionality bits.
+    // current_backendcomponent_idx_ will iterate through all backend bits.
+    explicit iterator(
+        const uint64_t* data_ptr,
+        uint8_t next_functionality = num_backends,
+        uint8_t next_backend = 0)
+        : data_ptr_(data_ptr),
+          next_functionality_(next_functionality),
+          next_backend_(next_backend),
+          // These are in an invalid state at construction time, and set by the
+          // first increment call
+          current_dispatchkey_idx_(end_iter_key_val),
+          current_backendcomponent_idx_(end_iter_key_val) {
+      // Go to the first key in the set
+      TORCH_INTERNAL_ASSERT(
+          next_functionality_ >= num_backends,
+          "num_backends=",
+          static_cast<uint32_t>(num_backends),
+          "next_functionality_=",
+          static_cast<uint32_t>(next_functionality_));
+      ++(*this);
+    }
+
+    C10_API self_type& operator++();
+
+    self_type operator++(int) {
+      self_type previous_iterator = *this;
+      ++(*this);
+      return previous_iterator;
+    }
+
+    bool operator==(const self_type& rhs) const {
+      return next_functionality_ == rhs.next_functionality_ &&
+          current_dispatchkey_idx_ == rhs.current_dispatchkey_idx_ &&
+          next_backend_ == rhs.next_backend_ &&
+          current_backendcomponent_idx_ == rhs.current_backendcomponent_idx_;
+    }
+    bool operator!=(const self_type& rhs) const {
+      return next_functionality_ != rhs.next_functionality_ ||
+          current_dispatchkey_idx_ != rhs.current_dispatchkey_idx_ ||
+          next_backend_ != rhs.next_backend_ ||
+          current_backendcomponent_idx_ != rhs.current_backendcomponent_idx_;
+    }
+    DispatchKey operator*() const {
+      auto functionality_key =
+          static_cast<DispatchKey>(current_dispatchkey_idx_);
+      if (isPerBackendFunctionalityKey(functionality_key)) {
+        auto next_key = toRuntimePerBackendFunctionalityKey(
+            functionality_key,
+            static_cast<BackendComponent>(current_backendcomponent_idx_));
+        // We expect all of the Dense, Sparse, Quantized, and Autograd keys to
+        // be ordered the same way with respect to their backends
+        TORCH_INTERNAL_ASSERT(
+            toBackendComponent(next_key) ==
+                static_cast<BackendComponent>(current_backendcomponent_idx_),
+            "Tried to map functionality key ",
+            toString(functionality_key),
+            " and backend bit ",
+            toString(
+                static_cast<BackendComponent>(current_backendcomponent_idx_)),
+            " to a runtime key, but ended up with ",
+            toString(next_key),
+            ". This can happen if the order of the backend dispatch keys in DispatchKey.h isn't consistent.",
+            " Please double check that enum for inconsistencies.");
+        return next_key;
+      } else {
+        return functionality_key;
+      }
+    }
+
+   private:
+    const uint64_t* data_ptr_;
+    uint8_t next_functionality_;
+    uint8_t next_backend_;
+    uint8_t current_dispatchkey_idx_;
+    uint8_t current_backendcomponent_idx_;
+  };
+
+ public:
+  // Returns iterator to the first key in the set. If no keys are in the
+  // set, then will return the end iterator.
+  iterator begin() const {
+    return iterator(&repr_);
+  }
+
+  // We do not need to iterate beyond EndOfFunctionalityKeys so we will treat
+  // this as the end iterator.
+  iterator end() const {
+    return iterator(&repr_, iterator::end_iter_mask_val);
+  }
+};
+
+C10_API std::string toString(DispatchKeySet);
+C10_API std::ostream& operator<<(std::ostream&, DispatchKeySet);
+
+C10_API inline int getDispatchTableIndexForDispatchKey(DispatchKey k) {
+  return DispatchKeySet(k).getDispatchTableIndexForDispatchKeySet();
+}
+
+// Alias key DispatchKey::Autograd maps to
+// (autograd_dispatch_keyset x full_backend_mask)
+// NB: keys in this set also get associated with CompositeImplicitAutograd
+//
+// Note [autograd_dispatch_keyset Does Not Include Backend Bits]
+// We don't want to include any backend bits (BackendComponent::CPUBit, etc)
+// directly in autograd_dispatch_keyset.
+// Why? keysets like autograd_dispatch_keyset are commonly used to remove
+// autograd keys from a DispatchKeySet throughout the code base. However, you
+// are only allowed to remove functionality bits from a keyset, not backend
+// bits. See Note [Removing keys from DispatchKeySet Only Affects Functionality
+// Keys] for details. To be consistent and avoid confusion, we're explicitly
+// setting up autograd_dispatch_keyset to not have any backend bits.
+constexpr DispatchKeySet autograd_dispatch_keyset = DispatchKeySet({
+    DispatchKey::AutogradFunctionality,
+    DispatchKey::AutogradOther,
+    DispatchKey::AutogradNestedTensor,
+});
+
+constexpr DispatchKeySet autocast_dispatch_keyset = DispatchKeySet({
+    DispatchKey::AutocastCPU,
+    DispatchKey::AutocastMPS,
+    DispatchKey::AutocastCUDA,
+    DispatchKey::AutocastXPU,
+    DispatchKey::AutocastIPU,
+    DispatchKey::AutocastHPU,
+    DispatchKey::AutocastXLA,
+    DispatchKey::AutocastPrivateUse1,
+});
+
+// See Note [TLS Initialization]
+constexpr DispatchKeySet default_included_set = DispatchKeySet({
+    DispatchKey::BackendSelect,
+    DispatchKey::ADInplaceOrView,
+});
+
+constexpr DispatchKeySet default_excluded_set = DispatchKeySet({
+    DispatchKey::AutocastCPU,
+    DispatchKey::AutocastMPS,
+    DispatchKey::AutocastCUDA,
+    DispatchKey::AutocastXPU,
+    DispatchKey::AutocastIPU,
+    DispatchKey::AutocastHPU,
+    DispatchKey::AutocastXLA,
+    DispatchKey::AutocastPrivateUse1,
+});
+
+constexpr DispatchKeySet autograd_dispatch_keyset_with_ADInplaceOrView =
+    autograd_dispatch_keyset | DispatchKeySet(DispatchKey::ADInplaceOrView);
+
+constexpr DispatchKeySet python_ks = DispatchKeySet({
+    DispatchKey::Python,
+    DispatchKey::PythonTLSSnapshot,
+});
+
+constexpr DispatchKeySet sparse_ks = DispatchKeySet(DispatchKey::Sparse);
+
+constexpr DispatchKeySet sparse_csr_ks = DispatchKeySet(DispatchKey::SparseCsr);
+
+constexpr DispatchKeySet mkldnn_ks = DispatchKeySet(DispatchKey::MkldnnCPU);
+
+// backend dispatch keys that map to DispatchKey::AutogradOther
+// NB: keys in this set also get associated with CompositeImplicitAutograd
+constexpr DispatchKeySet autogradother_backends =
+    DispatchKeySet(
+        // HIP and VE aren't in this list: they now have their own backend bits
+        // which means that they can now have their own Autograd keys.
+        // Technically, HIP will now redispatch to its own custom AutogradHIP
+        // slot in the runtime table.
+        {DispatchKey::FPGA,
+         DispatchKey::MAIA,
+         DispatchKey::Vulkan,
+         DispatchKey::Metal,
+         DispatchKey::CustomRNGKeyId,
+         DispatchKey::MkldnnCPU,
+         // Sparse and Quantized backends also live here.
+         DispatchKey::Sparse,
+         DispatchKey::SparseCsr,
+         DispatchKey::Quantized})
+    // Including the backend bits because this keyset is used during op
+    // registration, which requires looping over all runtime autogradother
+    // backend keys.
+    | DispatchKeySet(DispatchKeySet::RAW, full_backend_mask);
+
+// The set of dispatch keys that come after autograd
+// n.b. this relies on the fact that AutogradOther is currently the lowest
+// Autograd key
+constexpr DispatchKeySet after_autograd_keyset =
+    DispatchKeySet(DispatchKeySet::FULL_AFTER, c10::DispatchKey::AutogradOther);
+
+// The set of dispatch keys that come after ADInplaceOrView
+constexpr DispatchKeySet after_ADInplaceOrView_keyset = DispatchKeySet(
+    DispatchKeySet::FULL_AFTER,
+    c10::DispatchKey::ADInplaceOrView);
+
+// The set of dispatch keys that come after Functionalize
+constexpr DispatchKeySet after_func_keyset =
+    DispatchKeySet(DispatchKeySet::FULL_AFTER, c10::DispatchKey::Functionalize)
+        .remove(
+            // NOTE: we also need to remove ADInplaceOrView from the keyset when
+            // redispatching after the func kernels. This is because we're not
+            // calling the same op; we originally called an inplace op, and now
+            // we aren't. The original key calculation figured out which keys
+            // were Fallthrough based on the inplace op. That means that it did
+            // not include the ADInPlaceOrView kernel as a fallthrough key.
+            // However, we WANT the ADInPlaceOrView kernel to be ignored now
+            // that we're calling an out-of-place op. Re-invoking
+            // Dispatcher::call would re-run the Fallthrough key calculation and
+            // get us that, But at::redispatch is more performant. We can get
+            // away with it by explicitly removing the key here.
+            c10::DispatchKey::ADInplaceOrView);
+
+constexpr DispatchKeySet backend_bitset_mask =
+    DispatchKeySet(DispatchKeySet::RAW, (1ULL << num_backends) - 1);
+
+constexpr auto inplace_or_view_ks =
+    DispatchKeySet(DispatchKey::ADInplaceOrView);
+constexpr auto autograd_cpu_ks = DispatchKeySet(DispatchKey::AutogradCPU);
+constexpr auto autograd_ipu_ks = DispatchKeySet(DispatchKey::AutogradIPU);
+constexpr auto autograd_xpu_ks = DispatchKeySet(DispatchKey::AutogradXPU);
+constexpr auto autograd_cuda_ks = DispatchKeySet(DispatchKey::AutogradCUDA);
+constexpr auto autograd_xla_ks = DispatchKeySet(DispatchKey::AutogradXLA);
+constexpr auto autograd_lazy_ks = DispatchKeySet(DispatchKey::AutogradLazy);
+constexpr auto autograd_meta_ks = DispatchKeySet(DispatchKey::AutogradMeta);
+constexpr auto autograd_mps_ks = DispatchKeySet(DispatchKey::AutogradMPS);
+constexpr auto autograd_hpu_ks = DispatchKeySet(DispatchKey::AutogradHPU);
+constexpr auto autograd_privateuse1_ks =
+    DispatchKeySet(DispatchKey::AutogradPrivateUse1);
+constexpr auto autograd_privateuse2_ks =
+    DispatchKeySet(DispatchKey::AutogradPrivateUse2);
+constexpr auto autograd_privateuse3_ks =
+    DispatchKeySet(DispatchKey::AutogradPrivateUse3);
+constexpr auto autograd_other_ks = DispatchKeySet(DispatchKey::AutogradOther);
+constexpr auto autograd_nested =
+    DispatchKeySet(DispatchKey::AutogradNestedTensor);
+// keyset corresponding to functorch keys that have their own dedicated
+// TensorImpl subclass.
+constexpr auto functorch_transforms_ks = DispatchKeySet(
+    {DispatchKey::FuncTorchBatched,
+     DispatchKey::FuncTorchVmapMode,
+     DispatchKey::Batched,
+     DispatchKey::VmapMode,
+     DispatchKey::FuncTorchGradWrapper});
+
+constexpr auto functorch_batched_ks =
+    DispatchKeySet({DispatchKey::FuncTorchBatched});
+
+// This keyset has:
+// (1) the functionality bits corresponding to backends (dense, sparse,
+// quantized) (2) all of the backend bits set
+constexpr DispatchKeySet backend_functionality_keys =
+    DispatchKeySet({
+        DispatchKey::Dense,
+        DispatchKey::Quantized,
+        DispatchKey::Sparse,
+        DispatchKey::SparseCsr,
+    }) |
+    DispatchKeySet(DispatchKeySet::RAW, full_backend_mask);
+
+struct OpTableOffsetAndMask {
+  uint16_t offset;
+  uint16_t backend_mask;
+};
+
+static_assert(
+    num_backends <= 16,
+    "Right now we expect the number of backends not to exceed 16. In the (unlikely) event"
+    " that this changes, the size of OpTableOffsetAndMask::backend_mask needs to be increased too.");
+
+// true if t is a backend dispatch key
+C10_API bool isBackendDispatchKey(DispatchKey t);
+
+// Resolve alias dispatch key to DispatchKeySet if applicable
+C10_API DispatchKeySet getRuntimeDispatchKeySet(DispatchKey t);
+
+// Resolve alias dispatch key to DispatchKeySet if applicable,
+// and check if k is a part of that set
+C10_API bool runtimeDispatchKeySetHas(DispatchKey t, DispatchKey k);
+
+// Returns a DispatchKeySet of all backend keys mapped to Autograd dispatch key
+// t, DispatchKeySet is empty if t is not alias of DispatchKey::Autograd.
+C10_API DispatchKeySet getBackendKeySetFromAutograd(DispatchKey t);
+
+// Returns a DispatchKeySet of autograd related keys mapped to backend.
+// for a given backend key, use the associated autograd key.
+// for non-backend keys, use AutogradOther as a default.
+// Note: it's convenient and fast to return a default here rather than (say)
+// returning an std::optional<DispatchKey>, or throwing. But it makes callers
+// responsible for either a) enforcing the invariant that only backend keys
+// be passed as arguments, or b) interpreting our return value carefully.
+inline DispatchKeySet getAutogradRelatedKeySetFromBackend(BackendComponent t) {
+  switch (t) {
+    case BackendComponent::CPUBit:
+      return inplace_or_view_ks | autograd_cpu_ks;
+    case BackendComponent::IPUBit:
+      return inplace_or_view_ks | autograd_ipu_ks;
+    case BackendComponent::XPUBit:
+      return inplace_or_view_ks | autograd_xpu_ks;
+    case BackendComponent::CUDABit:
+      return inplace_or_view_ks | autograd_cuda_ks;
+    case BackendComponent::XLABit:
+      return inplace_or_view_ks | autograd_xla_ks;
+    case BackendComponent::LazyBit:
+      return inplace_or_view_ks | autograd_lazy_ks;
+    case BackendComponent::MetaBit:
+      return inplace_or_view_ks | autograd_meta_ks;
+    case BackendComponent::MPSBit:
+      return inplace_or_view_ks | autograd_mps_ks;
+    case BackendComponent::HPUBit:
+      return inplace_or_view_ks | autograd_hpu_ks;
+    case BackendComponent::PrivateUse1Bit:
+      return inplace_or_view_ks | autograd_privateuse1_ks;
+    case BackendComponent::PrivateUse2Bit:
+      return inplace_or_view_ks | autograd_privateuse2_ks;
+    case BackendComponent::PrivateUse3Bit:
+      return inplace_or_view_ks | autograd_privateuse3_ks;
+    default:
+      return inplace_or_view_ks | autograd_other_ks;
+  }
+}
+
+// Returns a DispatchKeySet of autocast related keys mapped to backend.
+inline DispatchKeySet getAutocastRelatedKeySetFromBackend(BackendComponent t) {
+  constexpr auto autocast_cpu_ks = DispatchKeySet(DispatchKey::AutocastCPU);
+  constexpr auto autocast_xpu_ks = DispatchKeySet(DispatchKey::AutocastXPU);
+  constexpr auto autocast_ipu_ks = DispatchKeySet(DispatchKey::AutocastIPU);
+  constexpr auto autocast_hpu_ks = DispatchKeySet(DispatchKey::AutocastHPU);
+  constexpr auto autocast_cuda_ks = DispatchKeySet(DispatchKey::AutocastCUDA);
+  constexpr auto autocast_xla_ks = DispatchKeySet(DispatchKey::AutocastXLA);
+  constexpr auto autocast_privateuse1_ks =
+      DispatchKeySet(DispatchKey::AutocastPrivateUse1);
+  constexpr auto autocast_mps_ks = DispatchKeySet(DispatchKey::AutocastMPS);
+  switch (t) {
+    case BackendComponent::CPUBit:
+      return autocast_cpu_ks;
+    case BackendComponent::XPUBit:
+      return autocast_xpu_ks;
+    case BackendComponent::IPUBit:
+      return autocast_ipu_ks;
+    case BackendComponent::HPUBit:
+      return autocast_hpu_ks;
+    case BackendComponent::CUDABit:
+      return autocast_cuda_ks;
+    case BackendComponent::XLABit:
+      return autocast_xla_ks;
+    case BackendComponent::PrivateUse1Bit:
+      return autocast_privateuse1_ks;
+    case BackendComponent::MPSBit:
+      return autocast_mps_ks;
+    default:
+      return DispatchKeySet();
+  }
+}
+
+// returns the "backend" DispatchKey of highest priority in the set.
+// This is basically like highestBackendKey(), except that we have some
+// "functionality" bits that correspond to backends (Sparse, Quantized)
+inline DispatchKey highestPriorityBackendTypeId(DispatchKeySet ks) {
+  return (ks & backend_functionality_keys).highestPriorityTypeId();
+}
+
+// This API exists because we have a use case for checking
+// getRuntimeDispatchKeySet(alias).has(DispatchKey::Undefined)
+// in OperatorEntry.cpp but we disallow it in has() API.
+C10_API bool isIncludedInAlias(DispatchKey k, DispatchKey alias);
+
+// Historically, every tensor only had a single DispatchKey, and it was always
+// something like CPU, and there wasn't any of this business where TLS
+// could cause the DispatchKey of a tensor to change.  But we still have some
+// legacy code that is still using DispatchKey for things like instanceof
+// checks; if at all possible, refactor the code to stop using DispatchKey in
+// those cases.
+inline DispatchKey legacyExtractDispatchKey(DispatchKeySet s) {
+  // NB: If you add any extra keys that can be stored in TensorImpl on
+  // top of existing "backend" keys like CPU/CUDA, you need to add it
+  // here.  At the moment, autograd keys and ADInplaceOrView key need this
+  // treatment;
+  return (s - autograd_dispatch_keyset_with_ADInplaceOrView -
+          autocast_dispatch_keyset -
+          DispatchKeySet(
+              {DispatchKey::Functionalize,
+               DispatchKey::PythonTLSSnapshot,
+               DispatchKey::FuncTorchGradWrapper,
+               DispatchKey::FuncTorchVmapMode,
+               DispatchKey::FuncTorchBatched,
+               DispatchKey::Python}))
+      .highestPriorityTypeId();
+}
+
+template <class T>
+using is_not_DispatchKeySet = std::negation<std::is_same<DispatchKeySet, T>>;
+
+// Given a function type, constructs a function_traits type that drops the first
+// parameter type if the first parameter is of type DispatchKeySet. NB:
+// DispatchKeySet is currently explicitly hidden from JIT (mainly to avoid
+// pushing unnecessary arguments on the stack - see Note [ Plumbing Keys Through
+// the Dispatcher] for details). If at any point in the future we need to expose
+// this type to JIT, revisit the usage of this type alias.
+template <class FuncType>
+using remove_DispatchKeySet_arg_from_func = guts::make_function_traits_t<
+    typename guts::infer_function_traits_t<FuncType>::return_type,
+    typename std::conditional_t<
+        std::is_same_v<
+            DispatchKeySet,
+            typename guts::typelist::head_with_default_t<
+                void,
+                typename guts::infer_function_traits_t<
+                    FuncType>::parameter_types>>,
+        guts::typelist::drop_if_nonempty_t<
+            typename guts::infer_function_traits_t<FuncType>::parameter_types,
+            1>,
+        typename guts::infer_function_traits_t<FuncType>::parameter_types>>;
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/DynamicCast.h b/lib/python3.11/site-packages/torch/include/c10/core/DynamicCast.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a845776a263b68ce731d15350a8b9a040b98f6b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/DynamicCast.h
@@ -0,0 +1,125 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Load.h>
+#include <c10/util/TypeCast.h>
+
+namespace c10 {
+
+// Dynamic type casting utils:
+// - fetch_and_cast
+// - cast_and_store
+//
+// fetch_and_cast fetch a value with dynamic type specified by a ScalarType
+// from a void pointer and cast it to a static type.
+//
+// cast_and_store casts a static typed value into dynamic type specified
+// by a ScalarType, and store it into a void pointer.
+//
+// NOTE:
+//
+// Dynamic casting allows us to support type promotion without blowing up
+// the combination space: For example, without dynamic cast, in order to
+// implement `add_` with type promotion, we would need something like
+//
+// AT_DISPATCH_ALL_TYPES(output.dtype(),
+//    AT_DISPATCH_ALL_TYPES(input1.dtype(),
+//       AT_DISPATCH_ALL_TYPES(input2.dtype(),
+//           [](arg0_t a, arg1_t b) -> out_t { return a + b; }
+//       )
+//    )
+// )
+//
+// If we support N dtypes, the above code would generate the a+b kernel for
+// all the N * N * N different supported types, the compilation time and
+// binary size would become horrible.
+//
+// Dynamic casting might sounds like a bad idea in terms of performance.
+// Especially if you ever do it in a loop, you are going to do a billion tests.
+// But in practice it is not as bad as it might look:
+//
+// - on CPU, this is a branch that always has the same outcome, therefore
+//   hopefully the branch predictor could do the job pretty well
+// - on GPU, these branches will not diverge, so we could still have the same
+//   warp executing the same line of code
+// - Most kernels, like `add`, are bandwidth bound, adding a few clock cycles to
+//   check an integer does not hurt the performance much because the ALUs would
+//   wait for load instructions anyway.
+//
+// For the discussion and benchmark, refer to:
+// - https://github.com/pytorch/pytorch/pull/28343
+// - https://github.com/pytorch/pytorch/pull/28344
+// - https://github.com/pytorch/pytorch/pull/28345
+//
+
+#ifdef C10_HOST_DEVICE
+#define ERROR_UNSUPPORTED_CAST CUDA_KERNEL_ASSERT(false);
+#else
+#define ERROR_UNSUPPORTED_CAST TORCH_CHECK(false, "Unexpected scalar type");
+#endif
+
+// Fetch a value with dynamic type src_type from ptr, and cast it to static type
+// dest_t.
+#define FETCH_AND_CAST_CASE(type, scalartype) \
+  case ScalarType::scalartype:                \
+    return c10::convert<dest_t>(c10::load<type>(ptr));
+
+template <typename dest_t>
+C10_HOST_DEVICE inline dest_t fetch_and_cast(
+    const ScalarType src_type,
+    const void* ptr) {
+  switch (src_type) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(FETCH_AND_CAST_CASE)
+    FETCH_AND_CAST_CASE(uint16_t, UInt16)
+    FETCH_AND_CAST_CASE(uint32_t, UInt32)
+    FETCH_AND_CAST_CASE(uint64_t, UInt64)
+    default:
+      ERROR_UNSUPPORTED_CAST
+  }
+  return dest_t(0); // just to avoid compiler warning
+}
+
+// Cast a value with static type src_t into dynamic dest_type, and store it to
+// ptr.
+#define CAST_AND_STORE_CASE(type, scalartype) \
+  case ScalarType::scalartype:                \
+    *(type*)ptr = c10::convert<type>(value);  \
+    return;
+template <typename src_t>
+C10_HOST_DEVICE inline void cast_and_store(
+    const ScalarType dest_type,
+    void* ptr,
+    src_t value) {
+  switch (dest_type) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(CAST_AND_STORE_CASE)
+    CAST_AND_STORE_CASE(uint16_t, UInt16)
+    CAST_AND_STORE_CASE(uint32_t, UInt32)
+    CAST_AND_STORE_CASE(uint64_t, UInt64)
+    default:;
+  }
+  ERROR_UNSUPPORTED_CAST
+}
+
+#define DEFINE_UNCASTABLE(T, scalartype_)                     \
+  template <>                                                 \
+  C10_HOST_DEVICE inline T fetch_and_cast<T>(                 \
+      const ScalarType src_type, const void* ptr) {           \
+    CUDA_KERNEL_ASSERT(ScalarType::scalartype_ == src_type);  \
+    return c10::load<T>(ptr);                                 \
+  }                                                           \
+  template <>                                                 \
+  C10_HOST_DEVICE inline void cast_and_store<T>(              \
+      const ScalarType dest_type, void* ptr, T value) {       \
+    CUDA_KERNEL_ASSERT(ScalarType::scalartype_ == dest_type); \
+    *(T*)ptr = value;                                         \
+  }
+
+AT_FORALL_QINT_TYPES(DEFINE_UNCASTABLE)
+
+#undef FETCH_AND_CAST_CASE
+#undef CAST_AND_STORE_CASE
+#undef DEFINE_UNCASTABLE
+#undef ERROR_UNSUPPORTED_CAST
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Event.h b/lib/python3.11/site-packages/torch/include/c10/core/Event.h
new file mode 100644
index 0000000000000000000000000000000000000000..b94db9f4f26d0bad670b1b1b0c2d508a42943615
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Event.h
@@ -0,0 +1,137 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Stream.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/InlineEvent.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+
+namespace c10 {
+
+/**
+ * A backend-generic movable, not copyable, not thread-safe event.
+ *
+ * The design of this event follows that of CUDA and HIP events. These events
+ * are recorded and waited on by streams and can be rerecorded to,
+ * each rerecording essentially creating a new version of the event.
+ * For example, if (in CPU time), stream X is asked to record E,
+ * stream Y waits on E, and stream X is asked to record E again, then Y will
+ * wait for X to finish the first call to record and not the second, because
+ * it's waiting on the first version of event E, not the second.
+ * Querying an event only returns the status of its most recent version.
+ *
+ * Backend-generic events are implemented by this class and
+ * impl::InlineEvent. In addition to these events there are also
+ * some backend-specific events, like ATen's CUDAEvent. Each of these
+ * classes has its own use.
+ *
+ * impl::InlineEvent<...> or a backend-specific event should be
+ * preferred when the backend is known at compile time and known to
+ * be compiled. Backend-specific events may have additional functionality.
+ *
+ * This Event should be used if a particular backend may not be available,
+ * or the backend required is not known at compile time.
+ *
+ * These generic events are built on top of DeviceGuardImpls, analogous
+ * to DeviceGuard and InlineDeviceGuard. The name "DeviceGuardImpls,"
+ * is no longer entirely accurate, as these classes implement the
+ * backend-specific logic for a generic backend interface.
+ *
+ * See DeviceGuardImplInterface.h for a list of all supported flags.
+ */
+
+struct Event final {
+  // Constructors
+  Event() = delete;
+  Event(
+      const DeviceType _device_type,
+      const EventFlag _flag = EventFlag::PYTORCH_DEFAULT)
+      : impl_{_device_type, _flag} {}
+
+  // Copy constructor and copy assignment operator (deleted)
+  Event(const Event&) = delete;
+  Event& operator=(const Event&) = delete;
+
+  // Move constructor and move assignment operator
+  Event(Event&&) noexcept = default;
+  Event& operator=(Event&&) noexcept = default;
+
+  // Destructor
+  ~Event() = default;
+
+  // Getters
+  Device device() const noexcept {
+    return Device(device_type(), device_index());
+  }
+  DeviceType device_type() const noexcept {
+    return impl_.device_type();
+  }
+  DeviceIndex device_index() const noexcept {
+    return impl_.device_index();
+  }
+  EventFlag flag() const noexcept {
+    return impl_.flag();
+  }
+  bool was_marked_for_recording() const noexcept {
+    return impl_.was_marked_for_recording();
+  }
+
+  /**
+   * Calls record() if and only if record() has never been called for this
+   * event. Note: because Event is not thread-safe recordOnce() may call
+   * record() multiple times if called from multiple threads.
+   */
+  void recordOnce(const Stream& stream) {
+    impl_.recordOnce(stream);
+  }
+
+  /**
+   * Increments the event's version and enqueues a job with this version
+   * in the stream's work queue. When the stream process that job
+   * it notifies all streams waiting on / blocked by that version of the
+   * event to continue and marks that version as recorded.
+   * */
+  void record(const Stream& stream) {
+    impl_.record(stream);
+  }
+
+  /**
+   * Does nothing if the event has not been scheduled to be recorded.
+   * If the event was previously enqueued to be recorded, a command
+   * to wait for the version of the event that exists at the time of this call
+   * is inserted in the stream's work queue.
+   * When the stream reaches this command it will stop processing
+   * additional commands until that version of the event is marked as recorded.
+   */
+  void block(const Stream& stream) const {
+    impl_.block(stream);
+  }
+
+  /**
+   * Returns true if (and only if)
+   *  (1) the event has never been scheduled to be recorded
+   *  (2) the current version is marked as recorded.
+   * Returns false otherwise.
+   */
+  bool query() const {
+    return impl_.query();
+  }
+
+  double elapsedTime(const Event& event) const {
+    return impl_.elapsedTime(event.impl_);
+  }
+
+  void* eventId() const {
+    return impl_.eventId();
+  }
+
+  void synchronize() const {
+    return impl_.synchronize();
+  }
+
+ private:
+  impl::InlineEvent<impl::VirtualGuardImpl> impl_;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/GeneratorImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/GeneratorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b0b78ef4601085a7f3e8303644d6365c976efec
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/GeneratorImpl.h
@@ -0,0 +1,111 @@
+#pragma once
+
+#include <cstdint>
+#include <mutex>
+
+#include <c10/core/Device.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/util/python_stub.h>
+
+/**
+ * Note [Generator]
+ * ~~~~~~~~~~~~~~~~
+ * A Pseudo Random Number Generator (PRNG) is an engine that uses an algorithm
+ * to generate a seemingly random sequence of numbers, that may be later be used
+ * in creating a random distribution. Such an engine almost always maintains a
+ * state and requires a seed to start off the creation of random numbers. Often
+ * times, users have found it beneficial to be able to explicitly create,
+ * retain, and destroy PRNG states and also be able to have control over the
+ * seed value.
+ *
+ * A Generator in ATen gives users the ability to read, write and modify a PRNG
+ * engine. For instance, it does so by letting users seed a PRNG engine, fork
+ * the state of the engine, etc.
+ *
+ * By default, there is one generator per device, and a device's generator is
+ * lazily created. A user can use the torch.Generator() api to create their own
+ * generator. Currently torch.Generator() can only create a CPUGeneratorImpl.
+ */
+
+/**
+ * Note [Acquire lock when using random generators]
+ * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ * Generator and its derived classes are NOT thread-safe. Please note that most
+ * of the places where we have inserted locking for generators are historically
+ * based, and we haven't actually checked that everything is truly thread safe
+ * (and it probably isn't). Please use the public mutex_ when using any methods
+ * from these classes, except for the read-only methods. You can learn about the
+ * usage by looking into the unittests (aten/src/ATen/cpu_generator_test.cpp)
+ * and other places where we have used lock_guard.
+ *
+ * TODO: Look into changing the threading semantics of Generators in ATen (e.g.,
+ * making them non-thread safe and instead making the generator state
+ * splittable, to accommodate forks into other threads).
+ */
+
+namespace c10 {
+
+// The default seed is selected to be a large number
+// with good distribution of 0s and 1s in bit representation
+constexpr uint64_t default_rng_seed_val = 67280421310721;
+
+struct C10_API GeneratorImpl : public c10::intrusive_ptr_target {
+  // Constructors
+  GeneratorImpl(Device device_in, DispatchKeySet key_set);
+
+  // Delete all copy and move assignment in favor of clone()
+  // method
+  GeneratorImpl(const GeneratorImpl& other) = delete;
+  GeneratorImpl(GeneratorImpl&& other) = delete;
+  GeneratorImpl& operator=(const GeneratorImpl& other) = delete;
+  GeneratorImpl& operator=(GeneratorImpl&& other) = delete;
+
+  ~GeneratorImpl() override = default;
+  c10::intrusive_ptr<GeneratorImpl> clone() const;
+
+  // Common methods for all generators
+  virtual void set_current_seed(uint64_t seed) = 0;
+  virtual void set_offset(uint64_t offset) = 0;
+  virtual uint64_t get_offset() const = 0;
+  virtual uint64_t current_seed() const = 0;
+  virtual uint64_t seed() = 0;
+  virtual void set_state(const c10::TensorImpl& new_state) = 0;
+  virtual c10::intrusive_ptr<c10::TensorImpl> get_state() const = 0;
+  virtual void graphsafe_set_state(
+      const c10::intrusive_ptr<c10::GeneratorImpl>& new_state);
+  virtual c10::intrusive_ptr<c10::GeneratorImpl> graphsafe_get_state() const;
+  Device device() const;
+
+  // See Note [Acquire lock when using random generators]
+  std::mutex mutex_;
+
+  DispatchKeySet key_set() const {
+    return key_set_;
+  }
+
+  inline void set_pyobj(PyObject* pyobj) noexcept {
+    pyobj_ = pyobj;
+  }
+
+  inline PyObject* pyobj() const noexcept {
+    return pyobj_;
+  }
+
+ protected:
+  Device device_;
+  DispatchKeySet key_set_;
+  PyObject* pyobj_ = nullptr;
+
+  virtual GeneratorImpl* clone_impl() const = 0;
+};
+
+namespace detail {
+
+C10_API uint64_t getNonDeterministicRandom(bool is_cuda = false);
+
+} // namespace detail
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/GradMode.h b/lib/python3.11/site-packages/torch/include/c10/core/GradMode.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8f6329cf83bdf59b8aa1777f9d628d4f675a563
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/GradMode.h
@@ -0,0 +1,52 @@
+#pragma once
+
+#include <c10/core/AutogradState.h>
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+struct C10_API GradMode {
+  static bool is_enabled();
+  static void set_enabled(bool enabled);
+};
+
+// A RAII, thread local (!) guard that enables or disables grad mode upon
+// construction, and sets it back to the original value upon destruction.
+struct C10_API AutoGradMode {
+  AutoGradMode(bool enabled) : prev_mode(GradMode::is_enabled()) {
+    GradMode::set_enabled(enabled);
+  }
+  AutoGradMode(const AutoGradMode&) = delete;
+  AutoGradMode(AutoGradMode&&) = delete;
+  AutoGradMode& operator=(const AutoGradMode&) = delete;
+  AutoGradMode& operator=(AutoGradMode&&) = delete;
+  ~AutoGradMode() {
+    GradMode::set_enabled(prev_mode);
+  }
+  bool prev_mode;
+};
+
+// A RAII, thread local (!) guard that stops future operations from building
+// gradients.
+struct C10_API NoGradGuard : public AutoGradMode {
+  NoGradGuard() : AutoGradMode(/*enabled=*/false) {}
+};
+
+// A RAII, thread local (!) guard that enables or disables forward grad mode
+// upon construction, and sets it back to the original value upon destruction.
+struct C10_API AutoFwGradMode {
+  AutoFwGradMode(bool enabled)
+      : prev_mode(AutogradState::get_tls_state().get_fw_grad_mode()) {
+    AutogradState::get_tls_state().set_fw_grad_mode(enabled);
+  }
+  AutoFwGradMode(const AutoFwGradMode&) = delete;
+  AutoFwGradMode(AutoFwGradMode&&) = delete;
+  AutoFwGradMode& operator=(const AutoFwGradMode&) = delete;
+  AutoFwGradMode& operator=(AutoFwGradMode&&) = delete;
+  ~AutoFwGradMode() {
+    AutogradState::get_tls_state().set_fw_grad_mode(prev_mode);
+  }
+  bool prev_mode;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/InferenceMode.h b/lib/python3.11/site-packages/torch/include/c10/core/InferenceMode.h
new file mode 100644
index 0000000000000000000000000000000000000000..a9cf2f0bf32e024fe1b70e7d53badc521ea11cd5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/InferenceMode.h
@@ -0,0 +1,91 @@
+#pragma once
+
+#include <c10/core/AutogradState.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/core/impl/LocalDispatchKeySet.h>
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+// A RAII, thread local (!) guard that enables or disables inference mode upon
+// construction, and sets it back to the original value upon destruction.
+struct C10_API InferenceMode {
+  // Note [Expected TLS state in InferenceMode]:
+  //   InferenceMode: ADInplaceOrView not in
+  //   raw_local_dispatch_key_set.included(),
+  //                  Autograd in raw_local_dispatch_key_set.excluded()
+  //                  GradMode is disabled.
+  //   NormalMode: ADInplaceOrView in raw_local_dispatch_key_set.included(),
+  //               Autograd not in raw_local_dispatch_key_set.excluded()
+  //               GradMode is enabled by default unless toggled manually
+  //               through other APIs, e.g. NoGradGuard.
+  //
+  // Invariant:
+  // - ADInplaceOrView is never in the excluded set
+  // - Autograd is never in the included set
+  // - Setting InferenceMode will set GradMode accordingly, but not vice versa.
+  //
+  //  1. Why do we put ADInplaceOrView in included set outside InferenceMode?
+  //
+  //     Inplace update to inference tensor outside InferenceMode is not
+  //     allowed. See Note [Inplace update inference tensor] for more details.
+  //     Without going through ADInplaceOrView kernel, we cannot throw error
+  //     for `inference_tensor.add_(1)` case.
+  //
+  // 2. Why not put ADInplaceOrView in the excluded set inside InferenceMode?
+  //
+  //    For example:
+  //    torch::Tensor a = torch::ones({1, 2, 3}).set_requires_grad(true);
+  //    torch::Tensor k = a + 2;
+  //    {
+  //      c10::InferenceMode guard(true);
+  //      k.add_(2);
+  //    }
+  //    `k.add_(2)` still need to go through ADInplaceOrView kernel so that it's
+  //    prepared for future autograd.
+  //
+  // 3. Why does setting InferenceMode also set GradMode?
+  //
+  //    This is required since InferenceMode is a faster and more restrictive
+  //    version of NoGradGuard. All runtime checks using GradMode::is_enabled()
+  //    are applicable to InferenceMode as well, e.g.
+  //    `tensorTypeInCurrentExecutionContext` in interpreter.cpp.
+  InferenceMode(bool enabled = true)
+      : prev_mode(AutogradState::get_tls_state()),
+        prev_keyset(c10::impl::tls_local_dispatch_key_set()) {
+    // Enabling inference mode means disabling grad modes
+    // And disabling inference mode means enabling grad modes
+    AutogradState::set_tls_state(AutogradState(
+        /* grad_mode */ !enabled,
+        /* inference_mode */ enabled,
+        /* fw_grad_mode */ !enabled,
+        /* multithreading_enabled*/ !enabled));
+    DispatchKeySet included = enabled
+        ? prev_keyset.included_.remove(c10::DispatchKey::ADInplaceOrView)
+        : prev_keyset.included_.add(c10::DispatchKey::ADInplaceOrView);
+    DispatchKeySet excluded = enabled
+        ? (prev_keyset.excluded_ | c10::autograd_dispatch_keyset)
+        : (prev_keyset.excluded_ - c10::autograd_dispatch_keyset);
+    c10::impl::PODLocalDispatchKeySet cur_keyset{};
+    cur_keyset.set_included(included);
+    cur_keyset.set_excluded(excluded);
+    c10::impl::_force_tls_local_dispatch_key_set(cur_keyset);
+  }
+
+  InferenceMode(const InferenceMode&) = delete;
+  InferenceMode(InferenceMode&&) = delete;
+  InferenceMode& operator=(const InferenceMode&) = delete;
+  InferenceMode& operator=(InferenceMode&&) = delete;
+
+  ~InferenceMode() {
+    AutogradState::set_tls_state(prev_mode);
+    c10::impl::_force_tls_local_dispatch_key_set(prev_keyset);
+  }
+  static bool is_enabled();
+
+ private:
+  AutogradState prev_mode;
+  c10::impl::LocalDispatchKeySet prev_keyset;
+};
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Layout.h b/lib/python3.11/site-packages/torch/include/c10/core/Layout.h
new file mode 100644
index 0000000000000000000000000000000000000000..82a9129501d9d21362e6b7fca207372013dd588a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Layout.h
@@ -0,0 +1,78 @@
+#pragma once
+
+#include <c10/core/Backend.h>
+#include <c10/util/Exception.h>
+
+#include <cstdint>
+#include <ostream>
+
+namespace c10 {
+enum class Layout : int8_t {
+  Strided,
+  Sparse,
+  SparseCsr,
+  Mkldnn,
+  SparseCsc,
+  SparseBsr,
+  SparseBsc,
+  Jagged,
+  NumOptions
+};
+
+constexpr auto kStrided = Layout::Strided;
+constexpr auto kSparse = Layout::Sparse;
+constexpr auto kSparseCsr = Layout::SparseCsr;
+constexpr auto kMkldnn = Layout::Mkldnn;
+constexpr auto kSparseCsc = Layout::SparseCsc;
+constexpr auto kSparseBsr = Layout::SparseBsr;
+constexpr auto kSparseBsc = Layout::SparseBsc;
+constexpr auto kJagged = Layout::Jagged;
+
+inline Layout layout_from_backend(Backend backend) {
+  switch (backend) {
+    case Backend::SparseCPU:
+    case Backend::SparseCUDA:
+    case Backend::SparseHIP:
+    case Backend::SparseVE:
+    case Backend::SparseXPU:
+    case Backend::SparsePrivateUse1:
+      return Layout::Sparse;
+    case Backend::MkldnnCPU:
+      return Layout::Mkldnn;
+    case Backend::SparseCsrCPU:
+    case Backend::SparseCsrCUDA:
+    case Backend::SparseCsrHIP:
+    case Backend::SparseCsrVE:
+    case Backend::SparseCsrXPU:
+      TORCH_CHECK(
+          false,
+          "Cannot map Backend SparseCsr(CPU|CUDA|HIP|VE|XPU) to a unique layout.");
+    default:
+      return Layout::Strided;
+  }
+}
+
+inline std::ostream& operator<<(std::ostream& stream, at::Layout layout) {
+  switch (layout) {
+    case at::kStrided:
+      return stream << "Strided";
+    case at::kSparse:
+      return stream << "Sparse";
+    case at::kSparseCsr:
+      return stream << "SparseCsr";
+    case at::kSparseCsc:
+      return stream << "SparseCsc";
+    case at::kSparseBsr:
+      return stream << "SparseBsr";
+    case at::kSparseBsc:
+      return stream << "SparseBsc";
+    case at::kMkldnn:
+      return stream << "Mkldnn";
+    case at::kJagged:
+      return stream << "Jagged";
+    default:
+      TORCH_CHECK(false, "Unknown layout");
+  }
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/MemoryFormat.h b/lib/python3.11/site-packages/torch/include/c10/core/MemoryFormat.h
new file mode 100644
index 0000000000000000000000000000000000000000..edc08bb1016c91a6fabdcbb0236fd46e5ff421f1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/MemoryFormat.h
@@ -0,0 +1,290 @@
+#pragma once
+
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+
+#include <cstdint>
+#include <ostream>
+#include <vector>
+
+// Memory format is not the property of a Tensor. It is the way to tell an
+// operator how the result should be organized in memory and nothing more. That
+// means memory format should never be used as return value for any tensor state
+// interrogation functions (internally and externally).
+//
+// Possible options are:
+//  Preserve:
+//    If any of the input tensors is in channels_last format, operator output
+//    should be in channels_last format
+//
+//  Contiguous:
+//    Regardless of input tensors format, the output should be contiguous
+//    Tensor.
+//
+//  ChannelsLast:
+//    Regardless of input tensors format, the output should be in channels_last
+//    format.
+
+namespace c10 {
+enum class MemoryFormat : int8_t {
+  Contiguous,
+  Preserve,
+  ChannelsLast,
+  ChannelsLast3d,
+  NumOptions
+};
+
+// If you are seeing this, it means that this call site was not checked if
+// the memory format could be preserved, and it was switched to old default
+// behaviour of contiguous
+#define LEGACY_CONTIGUOUS_MEMORY_FORMAT c10::get_contiguous_memory_format()
+
+inline MemoryFormat get_contiguous_memory_format() {
+  return MemoryFormat::Contiguous;
+}
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::MemoryFormat memory_format) {
+  switch (memory_format) {
+    case MemoryFormat::Preserve:
+      return stream << "Preserve";
+    case MemoryFormat::Contiguous:
+      return stream << "Contiguous";
+    case MemoryFormat::ChannelsLast:
+      return stream << "ChannelsLast";
+    case MemoryFormat::ChannelsLast3d:
+      return stream << "ChannelsLast3d";
+    default:
+      TORCH_CHECK(false, "Unknown memory format ", memory_format);
+  }
+}
+
+// Note: Hardcoded the channel last stride indices here to get better
+// performance
+template <typename T>
+inline std::vector<T> get_channels_last_strides_2d(ArrayRef<T> sizes) {
+  std::vector<T> strides(sizes.size());
+  switch (sizes.size()) {
+    case 4:
+      strides[1] = 1;
+      strides[3] = sizes[1];
+      strides[2] = strides[3] * sizes[3];
+      strides[0] = strides[2] * sizes[2];
+      return strides;
+    case 3:
+      strides[0] = 1;
+      strides[2] = sizes[0];
+      strides[1] = strides[2] * sizes[2];
+      return strides;
+    default:
+      TORCH_INTERNAL_ASSERT(
+          false, "ChannelsLast2d doesn't support size ", sizes.size());
+  }
+}
+
+inline std::vector<int64_t> get_channels_last_strides_2d(IntArrayRef sizes) {
+  return get_channels_last_strides_2d<int64_t>(sizes);
+}
+
+template <typename T>
+std::vector<T> get_channels_last_strides_3d(ArrayRef<T> sizes) {
+  std::vector<T> strides(sizes.size());
+  switch (sizes.size()) {
+    case 5:
+      strides[1] = 1;
+      strides[4] = sizes[1];
+      strides[3] = strides[4] * sizes[4];
+      strides[2] = strides[3] * sizes[3];
+      strides[0] = strides[2] * sizes[2];
+      return strides;
+    case 4:
+      strides[0] = 1;
+      strides[3] = sizes[0];
+      strides[2] = strides[3] * sizes[3];
+      strides[1] = strides[2] * sizes[2];
+      return strides;
+    default:
+      TORCH_INTERNAL_ASSERT(
+          false, "ChannelsLast3d doesn't support size ", sizes.size());
+  }
+}
+
+inline std::vector<int64_t> get_channels_last_strides_3d(IntArrayRef sizes) {
+  return get_channels_last_strides_3d<int64_t>(sizes);
+}
+
+// NOTE:
+// Below are Helper functions for is_channels_last_strides_xd.
+// 1. Please do not combine these helper functions, each helper function handles
+// exactly one case of sizes + memory_format, by doing this, the strides indices
+// will be a constant array and we can access it using constant index number,
+// the compiler will fully unroll the loop on strides indices to gain a better
+// performance.
+// 2. No error check in helper function, caller ensures the correctness of the
+// input
+// 3. All helper functions have similar comments, only 1st helper function is
+// commented here.
+template <typename T>
+inline bool is_channels_last_strides_2d_s4(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  T min = 0;
+  // special case for trivial C dimension. default to NCHW
+  if (strides[1] == 0) {
+    return false;
+  }
+  // loop strides indices
+  for (auto& d : {1, 3, 2, 0}) {
+    if (sizes[d] == 0) {
+      return false;
+    }
+    if (strides[d] < min) {
+      return false;
+    }
+    // Fallback to NCHW as default layout for ambiguous cases
+    // This is the flaw of implicit memory_format from strides.
+    // N111 tensor with identical strides for size 1 dimension;
+    // Two cases could lead us here:
+    // a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1])
+    // b. N11W contiguous Tensor sliced on the W-dimension.
+    // ([N,1,1,1]@[W,W,W,W])
+    if (d == 0 && min == strides[1]) {
+      return false;
+    }
+    // This is necessary to:
+    // 1. distinguish the memory_format of N1H1;
+    //     [H, 1, 1, 1] channels_last stride
+    //     [H, H, 1, 1] contiguous stride
+    // 2. permutation of 1C1W:
+    //     [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3)
+    //     [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as channels_last
+    min = strides[d];
+    if (sizes[d] > 1) {
+      min *= sizes[d];
+    }
+  }
+  return true;
+}
+
+template <typename T>
+inline bool is_channels_last_strides_3d_s5(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  T min = 0;
+  if (strides[1] == 0) {
+    return false;
+  }
+  for (auto& d : {1, 4, 3, 2, 0}) {
+    if (sizes[d] == 0) {
+      return false;
+    }
+    if (strides[d] < min) {
+      return false;
+    }
+    if (d == 0 && min == strides[1]) {
+      return false;
+    }
+    min = strides[d];
+    if (sizes[d] > 1) {
+      min *= sizes[d];
+    }
+  }
+  return true;
+}
+
+// Note [Ambiguous is_channels_last_strides_xd]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// The flaw of carrying memory_format implicitly through strides is very hard
+// to WAR properly. issue #24090
+// Without the history of permutation, we can't infer the memory_format of a
+// tensor from the snapshot of its size & stride
+// e.g.
+//
+// 1. We can NOT specify the memory_format of N111 tensor through strides in a
+//  meaningful way;
+//
+// 2. Two path that ended up with identical size/stride
+//  N11W contiguous tensor sliced at w-dimension becomes [N,1,1,1]@[W,W,W,W]
+//  NC11 channels_last tensor sliced at c-dimension becomes [N,1,1,1]@[C,C,C,C]
+//    So if we see a tensor [N,1,1,1]@[X,X,X,X], there's no way for us to infer
+//    the memory_format of the original tensor.
+//
+// Due to the limitations, our temporary WAR `is_channels_last_strides` does the
+// best effort to infer whether the original memory_format of a tensor is
+// at::MemoryFormat::ChannelsLast. The two objectives of this function (ordered
+// by their importance):
+//   1. Ensure that normal shape manipulation does not accidentally change the
+//      MemoryFormat of an existing tensor.
+//   2. Allows user to mark MemoryFormat::ChannelsLast to tensors;
+//
+// The function does so via checking strides of the tensor, including strides of
+// size-1 dimensions. Although conventionally PyTorch implies no restriction on
+// trivial stride (stride for size-1 dimension).
+//
+// Note that this approach is a compromise. We did not solve the problem
+// completely. Many cases we will not be able to infer the correct memory
+// format.
+// The implementation of `is_channels_last_strides` is to serve the objectives:
+// MemoryFormat::ChannelsLast has to be explicitly opted-in (no accidental
+// conversion); Best effort to maintain the ChannelsLast flag.
+//
+// Due to the fact that this is not a bulletproof solution, through testing
+// (aten/src/ATen/test/memory_format_test.cpp)
+//   a. we ensure that the common tasks are supported;
+//   a. we identify corner cases where the implementation compromises on.
+//
+// By the time accumulated permutation is enabled to replace implicit
+// memory_format through strides, we should be updating our tests and fix the
+// issues in our tests.
+//
+// We use Channels Last 2d as an example above.
+// This is a general problem for all the is_channels_last_strides_xd
+// implementation. Please check the helper functions
+// (is_channels_last_strides_*d_s*) for more details.
+
+template <typename T>
+inline bool is_channels_last_strides_2d(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  switch (sizes.size()) {
+    case 4:
+      return is_channels_last_strides_2d_s4(sizes, strides);
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 3:
+      // TODO dim == 3 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+inline bool is_channels_last_strides_3d(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  switch (sizes.size()) {
+    case 5:
+      return is_channels_last_strides_3d_s5(sizes, strides);
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 4:
+      // TODO dim == 4 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+inline bool is_channels_last_strides_2d(
+    const IntArrayRef sizes,
+    const IntArrayRef strides) {
+  return is_channels_last_strides_2d<int64_t>(sizes, strides);
+}
+
+inline bool is_channels_last_strides_3d(
+    const IntArrayRef sizes,
+    const IntArrayRef strides) {
+  return is_channels_last_strides_3d<int64_t>(sizes, strides);
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/OptionalRef.h b/lib/python3.11/site-packages/torch/include/c10/core/OptionalRef.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8743e6d55b558ef3e4c201448a319885bfe52fe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/OptionalRef.h
@@ -0,0 +1,31 @@
+#pragma once
+
+namespace c10 {
+
+template <typename T>
+class OptionalRef {
+ public:
+  OptionalRef() : data_(nullptr) {}
+  OptionalRef(const T* data) : data_(data) {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(data_);
+  }
+  OptionalRef(const T& data) : data_(&data) {}
+
+  bool has_value() const {
+    return data_ != nullptr;
+  }
+
+  const T& get() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(data_);
+    return *data_;
+  }
+
+  operator bool() const {
+    return has_value();
+  }
+
+ private:
+  const T* data_;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/PyHandleCache.h b/lib/python3.11/site-packages/torch/include/c10/core/PyHandleCache.h
new file mode 100644
index 0000000000000000000000000000000000000000..8861f568bd972746c533de79d8efae2875424653
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/PyHandleCache.h
@@ -0,0 +1,76 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/python_stub.h>
+
+#include <atomic>
+
+namespace c10 {
+
+// A PyHandleCache represents a cached pointer from a C++ object to
+// a Python object that represents that object analogously in Python.
+// Upon a cache hit, the relevant object can be retrieved after a test
+// and then a memory load.  Two conditions must hold to be able to use this
+// class:
+//
+//  - This must truly be a cache; e.g., the caller must be able to produce
+//    the object some other way if the cache hit misses.
+//
+//  - This must truly be a handle; e.g., the Python object referenced by
+//    this class must have static lifetime.  This means we don't have to
+//    maintain strong ownership or deallocate the object when the C++ object
+//    dies.  Static lifetime is a good idea in conjunction with the cache,
+//    since if you are producing a fresh object on miss you won't be
+//    maintaining object identity.  If you need bidirectional ownership,
+//    you will want to factor out the pattern in TensorImpl with
+//    resurrection.
+//
+// This cache is expected to not improve perf under torchdeploy, as one
+// interpreter will fill up the cache, and all the interpreters will be
+// unable to use the slot.  A potential improvement is to have multiple
+// slots (one per interpreter), which will work in deployment scenarios
+// where there a stable, fixed number of interpreters.  You can also store
+// the relevant state in the Python library, rather than in the non-Python
+// library (although in many cases, this is not convenient, as there may
+// not be a way to conveniently index based on the object.)
+class PyHandleCache {
+ public:
+  PyHandleCache() : pyinterpreter_(nullptr) {}
+
+  // Attempt to fetch the pointer from the cache, if the PyInterpreter
+  // matches.  If it doesn't exist, or the cache entry is not valid,
+  // use slow_accessor to get the real pointer value and return that
+  // (possibly writing it to the cache, if the cache entry is
+  // available.)
+  template <typename F>
+  PyObject* ptr_or(impl::PyInterpreter* self_interpreter, F slow_accessor)
+      const {
+    // Note [Memory ordering on Python interpreter tag]
+    impl::PyInterpreter* interpreter =
+        pyinterpreter_.load(std::memory_order_acquire);
+    if (C10_LIKELY(interpreter == self_interpreter)) {
+      return data_;
+    } else if (interpreter == nullptr) {
+      auto* r = slow_accessor();
+      impl::PyInterpreter* expected = nullptr;
+      // attempt to claim this cache entry with the specified interpreter tag
+      if (pyinterpreter_.compare_exchange_strong(
+              expected, self_interpreter, std::memory_order_acq_rel)) {
+        data_ = r;
+      }
+      // This shouldn't be possible, as you should be GIL protected
+      TORCH_INTERNAL_ASSERT(expected != self_interpreter);
+      return r;
+    } else {
+      return slow_accessor();
+    }
+  }
+
+ private:
+  mutable std::atomic<impl::PyInterpreter*> pyinterpreter_;
+  mutable PyObject* data_{nullptr};
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/QEngine.h b/lib/python3.11/site-packages/torch/include/c10/core/QEngine.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7809c8a62c0aa4c85388f46f8010bac9dd422ad
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/QEngine.h
@@ -0,0 +1,46 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <cstdint>
+#include <string>
+
+namespace c10 {
+
+/**
+ * QEngine is an enum that is used to select the engine to run quantized ops.
+ * Keep this enum in sync with get_qengine_id() in
+ * torch/backends/quantized/__init__.py
+ */
+enum class QEngine : uint8_t {
+  NoQEngine = 0,
+  FBGEMM = 1,
+  QNNPACK = 2,
+  ONEDNN = 3,
+  X86 = 4,
+};
+
+constexpr auto kNoQEngine = QEngine::NoQEngine;
+constexpr auto kFBGEMM = QEngine::FBGEMM;
+constexpr auto kQNNPACK = QEngine::QNNPACK;
+constexpr auto kONEDNN = QEngine::ONEDNN;
+constexpr auto kX86 = QEngine::X86;
+
+inline std::string toString(QEngine qengine) {
+  switch (qengine) {
+    case kNoQEngine:
+      return "NoQEngine";
+    case kFBGEMM:
+      return "FBGEMM";
+    case kQNNPACK:
+      return "QNNPACK";
+    case kONEDNN:
+      return "ONEDNN";
+    case kX86:
+      return "X86";
+    default:
+      TORCH_CHECK(
+          false, "Unrecognized Quantized Engine: ", static_cast<int>(qengine));
+  }
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/QScheme.h b/lib/python3.11/site-packages/torch/include/c10/core/QScheme.h
new file mode 100644
index 0000000000000000000000000000000000000000..559e68508c76e3c02b97eb7650cc1143ab0de2d1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/QScheme.h
@@ -0,0 +1,50 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <cstdint>
+#include <string>
+
+namespace c10 {
+
+/**
+ * QScheme is an enum that specifies the type of quantization. This has a one
+ * to one correspondence with Quantizer
+ * Please refer to ATen/quantized/Quantizer.h to see the Quantizers classes.
+ * Keep this file in sync with torch/nn/_qscheme.py
+ */
+enum class QScheme : uint8_t {
+  PER_TENSOR_AFFINE = 0,
+  PER_CHANNEL_AFFINE = 1,
+  PER_TENSOR_SYMMETRIC = 2,
+  PER_CHANNEL_SYMMETRIC = 3,
+  PER_CHANNEL_AFFINE_FLOAT_QPARAMS = 4,
+  COMPILE_TIME_NUM_QSCHEMES = 5,
+};
+
+constexpr auto kPerTensorAffine = QScheme::PER_TENSOR_AFFINE;
+constexpr auto kPerChannelAffine = QScheme::PER_CHANNEL_AFFINE;
+constexpr auto kPerTensorSymmetric = QScheme::PER_TENSOR_SYMMETRIC;
+constexpr auto kPerChannelSymmetric = QScheme::PER_CHANNEL_SYMMETRIC;
+constexpr auto kPerChannelAffineFloatQParams =
+    QScheme::PER_CHANNEL_AFFINE_FLOAT_QPARAMS;
+constexpr int COMPILE_TIME_NUM_QSCHEMES =
+    static_cast<int>(QScheme::COMPILE_TIME_NUM_QSCHEMES);
+
+inline std::string toString(QScheme qscheme) {
+  switch (qscheme) {
+    case kPerTensorAffine:
+      return "per_tensor_affine";
+    case kPerChannelAffine:
+      return "per_channel_affine";
+    case kPerTensorSymmetric:
+      return "per_tensor_symmetric";
+    case kPerChannelSymmetric:
+      return "per_channel_symmetric";
+    case kPerChannelAffineFloatQParams:
+      return "per_channel_affine_float_qparams";
+    default:
+      TORCH_CHECK(false, "Unrecognized qscheme: ", static_cast<int>(qscheme));
+  }
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/RefcountedDeleter.h b/lib/python3.11/site-packages/torch/include/c10/core/RefcountedDeleter.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce988864720a19758fc4dc5875d7402d36076d79
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/RefcountedDeleter.h
@@ -0,0 +1,52 @@
+#pragma once
+
+#include <c10/core/Storage.h>
+#include <c10/macros/Export.h>
+#include <c10/util/UniqueVoidPtr.h>
+
+#include <atomic>
+#include <memory>
+
+namespace c10 {
+
+// A RefcountedDeleterContext object is used as the `ctx` argument for DataPtr
+// to implement a shared DataPtr. Normally, a DataPtr is unique, but we use
+// this custom context and the `refcounted_deleter` function below to make the
+// DataPtr act like a non-unique DataPtr. This context object holds onto an
+// inner context and deleter function which handle the actual deletion of the
+// data when the refcount reaches 0.
+//
+// This shared DataPtr feature is only used when storages are shared between
+// multiple Python interpreters in MultiPy. Before storages had PyObject
+// preservation, interpreters could just share the same StorageImpl instance.
+// But now a StorageImpl can only be associated with one interpreter in order
+// to properly manage a zombie PyObject. So we share storages across Python
+// interpreters by creating a different StorageImpl instance for each one, but
+// they all point to the same data.
+struct C10_API RefcountedDeleterContext {
+  RefcountedDeleterContext(void* other_ctx, c10::DeleterFnPtr other_deleter)
+      : other_ctx(other_ctx, other_deleter), refcount(1) {}
+
+  std::unique_ptr<void, c10::DeleterFnPtr> other_ctx;
+  std::atomic_int refcount;
+};
+
+// `refcounted_deleter` is used as the `ctx_deleter` for DataPtr to implement
+// a shared DataPtr.
+//
+// Warning: This should only be called on a pointer to
+// a RefcountedDeleterContext that was allocated on the heap with `new`,
+// because when the refcount reaches 0, the context is deleted with `delete`
+C10_API void refcounted_deleter(void* ctx_);
+
+// If the storage's DataPtr does not use `refcounted_deleter`, replace it with
+// a DataPtr that does, so it can be shared between multiple StorageImpls
+C10_API void maybeApplyRefcountedDeleter(const c10::Storage& storage);
+
+// Create a new StorageImpl that points to the same data. If the original
+// StorageImpl's DataPtr does not use `refcounted_deleter`, it will be replaced
+// with one that does
+C10_API c10::Storage newStorageImplFromRefcountedDataPtr(
+    const c10::Storage& storage);
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SafePyObject.h b/lib/python3.11/site-packages/torch/include/c10/core/SafePyObject.h
new file mode 100644
index 0000000000000000000000000000000000000000..6102aed8c0ba9a044235bd06107a4cf829154f96
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SafePyObject.h
@@ -0,0 +1,120 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Export.h>
+#include <c10/util/python_stub.h>
+#include <utility>
+
+namespace c10 {
+
+// This is an safe owning holder for a PyObject, akin to pybind11's
+// py::object, with two major differences:
+//
+//  - It is in c10/core; i.e., you can use this type in contexts where
+//    you do not have a libpython dependency
+//
+//  - It is multi-interpreter safe (ala torchdeploy); when you fetch
+//    the underlying PyObject* you are required to specify what the current
+//    interpreter context is and we will check that you match it.
+//
+// It is INVALID to store a reference to a Tensor object in this way;
+// you should just use TensorImpl directly in that case!
+struct C10_API SafePyObject {
+  // Steals a reference to data
+  SafePyObject(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : data_(data), pyinterpreter_(pyinterpreter) {}
+  SafePyObject(SafePyObject&& other) noexcept
+      : data_(std::exchange(other.data_, nullptr)),
+        pyinterpreter_(other.pyinterpreter_) {}
+  // For now it's not used, so we just disallow it.
+  SafePyObject& operator=(SafePyObject&&) = delete;
+
+  SafePyObject(SafePyObject const& other)
+      : data_(other.data_), pyinterpreter_(other.pyinterpreter_) {
+    if (data_ != nullptr) {
+      (*pyinterpreter_)->incref(data_);
+    }
+  }
+
+  SafePyObject& operator=(SafePyObject const& other) {
+    if (this == &other) {
+      return *this; // Handle self-assignment
+    }
+    if (other.data_ != nullptr) {
+      (*other.pyinterpreter_)->incref(other.data_);
+    }
+    if (data_ != nullptr) {
+      (*pyinterpreter_)->decref(data_, /*has_pyobj_slot*/ false);
+    }
+    data_ = other.data_;
+    pyinterpreter_ = other.pyinterpreter_;
+    return *this;
+  }
+
+  ~SafePyObject() {
+    if (data_ != nullptr) {
+      (*pyinterpreter_)->decref(data_, /*has_pyobj_slot*/ false);
+    }
+  }
+
+  c10::impl::PyInterpreter& pyinterpreter() const {
+    return *pyinterpreter_;
+  }
+  PyObject* ptr(const c10::impl::PyInterpreter*) const;
+
+  // stop tracking the current object, and return it
+  PyObject* release() {
+    auto rv = data_;
+    data_ = nullptr;
+    return rv;
+  }
+
+ private:
+  PyObject* data_;
+  c10::impl::PyInterpreter* pyinterpreter_;
+};
+
+// A newtype wrapper around SafePyObject for type safety when a python object
+// represents a specific type. Note that `T` is only used as a tag and isn't
+// actually used for any true purpose.
+template <typename T>
+struct SafePyObjectT : private SafePyObject {
+  SafePyObjectT(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : SafePyObject(data, pyinterpreter) {}
+  ~SafePyObjectT() = default;
+  SafePyObjectT(SafePyObjectT&& other) noexcept : SafePyObject(other) {}
+  SafePyObjectT(SafePyObjectT const&) = delete;
+  SafePyObjectT& operator=(SafePyObjectT const&) = delete;
+  SafePyObjectT& operator=(SafePyObjectT&&) = delete;
+
+  using SafePyObject::ptr;
+  using SafePyObject::pyinterpreter;
+  using SafePyObject::release;
+};
+
+// Like SafePyObject, but non-owning.  Good for references to global PyObjects
+// that will be leaked on interpreter exit.  You get a copy constructor/assign
+// this way.
+struct C10_API SafePyHandle {
+  SafePyHandle() : data_(nullptr), pyinterpreter_(nullptr) {}
+  SafePyHandle(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : data_(data), pyinterpreter_(pyinterpreter) {}
+
+  c10::impl::PyInterpreter& pyinterpreter() const {
+    return *pyinterpreter_;
+  }
+  PyObject* ptr(const c10::impl::PyInterpreter*) const;
+  void reset() {
+    data_ = nullptr;
+    pyinterpreter_ = nullptr;
+  }
+  operator bool() {
+    return data_;
+  }
+
+ private:
+  PyObject* data_;
+  c10::impl::PyInterpreter* pyinterpreter_;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Scalar.h b/lib/python3.11/site-packages/torch/include/c10/core/Scalar.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d1dad2d993754aa3ac3e03653935011c15c6e98
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Scalar.h
@@ -0,0 +1,468 @@
+#pragma once
+
+#include <cstdint>
+#include <stdexcept>
+#include <type_traits>
+#include <utility>
+
+#include <c10/core/OptionalRef.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/SymBool.h>
+#include <c10/core/SymFloat.h>
+#include <c10/core/SymInt.h>
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Half.h>
+#include <c10/util/TypeCast.h>
+#include <c10/util/complex.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/util/overflows.h>
+
+namespace c10 {
+
+/**
+ * Scalar represents a 0-dimensional tensor which contains a single element.
+ * Unlike a tensor, numeric literals (in C++) are implicitly convertible to
+ * Scalar (which is why, for example, we provide both add(Tensor) and
+ * add(Scalar) overloads for many operations). It may also be used in
+ * circumstances where you statically know a tensor is 0-dim and single size,
+ * but don't know its type.
+ */
+class C10_API Scalar {
+ public:
+  Scalar() : Scalar(int64_t(0)) {}
+
+  void destroy() {
+    if (Tag::HAS_si == tag || Tag::HAS_sd == tag || Tag::HAS_sb == tag) {
+      raw::intrusive_ptr::decref(v.p);
+      v.p = nullptr;
+    }
+  }
+
+  ~Scalar() {
+    destroy();
+  }
+
+#define DEFINE_IMPLICIT_CTOR(type, name) \
+  Scalar(type vv) : Scalar(vv, true) {}
+
+  AT_FORALL_SCALAR_TYPES_AND7(
+      Half,
+      BFloat16,
+      Float8_e5m2,
+      Float8_e4m3fn,
+      Float8_e5m2fnuz,
+      Float8_e4m3fnuz,
+      ComplexHalf,
+      DEFINE_IMPLICIT_CTOR)
+  AT_FORALL_COMPLEX_TYPES(DEFINE_IMPLICIT_CTOR)
+
+  // Helper constructors to allow Scalar creation from long and long long types
+  // As std::is_same_v<long, long long> is false(except Android), one needs to
+  // provide a constructor from either long or long long in addition to one from
+  // int64_t
+#if defined(__APPLE__) || defined(__MACOSX)
+  static_assert(
+      std::is_same_v<long long, int64_t>,
+      "int64_t is the same as long long on MacOS");
+  Scalar(long vv) : Scalar(vv, true) {}
+#endif
+#if defined(_MSC_VER)
+  static_assert(
+      std::is_same_v<long long, int64_t>,
+      "int64_t is the same as long long on Windows");
+  Scalar(long vv) : Scalar(vv, true) {}
+#endif
+#if defined(__linux__) && !defined(__ANDROID__)
+  static_assert(
+      sizeof(void*) != 8 || std::is_same_v<long, int64_t>,
+      "int64_t is the same as long on 64 bit Linux");
+#if LONG_MAX != INT_MAX
+  Scalar(long long vv) : Scalar(vv, true) {}
+#endif /* not 32-bit system */
+#endif
+
+  Scalar(uint16_t vv) : Scalar(vv, true) {}
+  Scalar(uint32_t vv) : Scalar(vv, true) {}
+  Scalar(uint64_t vv) {
+    if (vv > static_cast<uint64_t>(INT64_MAX)) {
+      tag = Tag::HAS_u;
+      v.u = vv;
+    } else {
+      tag = Tag::HAS_i;
+      // NB: no need to use convert, we've already tested convertibility
+      v.i = static_cast<int64_t>(vv);
+    }
+  }
+
+#undef DEFINE_IMPLICIT_CTOR
+
+  // Value* is both implicitly convertible to SymbolicVariable and bool which
+  // causes ambiguity error. Specialized constructor for bool resolves this
+  // problem.
+  template <
+      typename T,
+      typename std::enable_if_t<std::is_same_v<T, bool>, bool>* = nullptr>
+  Scalar(T vv) : tag(Tag::HAS_b) {
+    v.i = convert<int64_t, bool>(vv);
+  }
+
+  template <
+      typename T,
+      typename std::enable_if_t<std::is_same_v<T, c10::SymBool>, bool>* =
+          nullptr>
+  Scalar(T vv) : tag(Tag::HAS_sb) {
+    v.i = convert<int64_t, c10::SymBool>(vv);
+  }
+
+#define DEFINE_ACCESSOR(type, name)                                   \
+  type to##name() const {                                             \
+    if (Tag::HAS_d == tag) {                                          \
+      return checked_convert<type, double>(v.d, #type);               \
+    } else if (Tag::HAS_z == tag) {                                   \
+      return checked_convert<type, c10::complex<double>>(v.z, #type); \
+    } else if (Tag::HAS_sd == tag) {                                  \
+      return checked_convert<type, double>(                           \
+          toSymFloat().guard_float(__FILE__, __LINE__), #type);       \
+    }                                                                 \
+    if (Tag::HAS_b == tag) {                                          \
+      return checked_convert<type, bool>(v.i, #type);                 \
+    } else if (Tag::HAS_i == tag) {                                   \
+      return checked_convert<type, int64_t>(v.i, #type);              \
+    } else if (Tag::HAS_u == tag) {                                   \
+      return checked_convert<type, uint64_t>(v.u, #type);             \
+    } else if (Tag::HAS_si == tag) {                                  \
+      return checked_convert<type, int64_t>(                          \
+          toSymInt().guard_int(__FILE__, __LINE__), #type);           \
+    } else if (Tag::HAS_sb == tag) {                                  \
+      return checked_convert<type, int64_t>(                          \
+          toSymBool().guard_bool(__FILE__, __LINE__), #type);         \
+    }                                                                 \
+    TORCH_CHECK(false)                                                \
+  }
+
+  // TODO: Support ComplexHalf accessor
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_ACCESSOR)
+  DEFINE_ACCESSOR(uint16_t, UInt16)
+  DEFINE_ACCESSOR(uint32_t, UInt32)
+  DEFINE_ACCESSOR(uint64_t, UInt64)
+
+#undef DEFINE_ACCESSOR
+
+  SymInt toSymInt() const {
+    if (Tag::HAS_si == tag) {
+      return c10::SymInt(intrusive_ptr<SymNodeImpl>::reclaim_copy(
+          static_cast<SymNodeImpl*>(v.p)));
+    } else {
+      return toLong();
+    }
+  }
+
+  SymFloat toSymFloat() const {
+    if (Tag::HAS_sd == tag) {
+      return c10::SymFloat(intrusive_ptr<SymNodeImpl>::reclaim_copy(
+          static_cast<SymNodeImpl*>(v.p)));
+    } else {
+      return toDouble();
+    }
+  }
+
+  SymBool toSymBool() const {
+    if (Tag::HAS_sb == tag) {
+      return c10::SymBool(intrusive_ptr<SymNodeImpl>::reclaim_copy(
+          static_cast<SymNodeImpl*>(v.p)));
+    } else {
+      return toBool();
+    }
+  }
+
+  // also support scalar.to<int64_t>();
+  // Deleted for unsupported types, but specialized below for supported types
+  template <typename T>
+  T to() const = delete;
+
+  // audit uses of data_ptr
+  const void* data_ptr() const {
+    TORCH_INTERNAL_ASSERT(!isSymbolic());
+    return static_cast<const void*>(&v);
+  }
+
+  bool isFloatingPoint() const {
+    return Tag::HAS_d == tag || Tag::HAS_sd == tag;
+  }
+
+  C10_DEPRECATED_MESSAGE(
+      "isIntegral is deprecated. Please use the overload with 'includeBool' parameter instead.")
+  bool isIntegral() const {
+    return Tag::HAS_i == tag || Tag::HAS_si == tag || Tag::HAS_u == tag;
+  }
+  bool isIntegral(bool includeBool) const {
+    return Tag::HAS_i == tag || Tag::HAS_si == tag || Tag::HAS_u == tag ||
+        (includeBool && isBoolean());
+  }
+
+  bool isComplex() const {
+    return Tag::HAS_z == tag;
+  }
+  bool isBoolean() const {
+    return Tag::HAS_b == tag || Tag::HAS_sb == tag;
+  }
+
+  // you probably don't actually want these; they're mostly for testing
+  bool isSymInt() const {
+    return Tag::HAS_si == tag;
+  }
+  bool isSymFloat() const {
+    return Tag::HAS_sd == tag;
+  }
+  bool isSymBool() const {
+    return Tag::HAS_sb == tag;
+  }
+
+  bool isSymbolic() const {
+    return Tag::HAS_si == tag || Tag::HAS_sd == tag || Tag::HAS_sb == tag;
+  }
+
+  C10_ALWAYS_INLINE Scalar& operator=(Scalar&& other) noexcept {
+    if (&other == this) {
+      return *this;
+    }
+
+    destroy();
+    moveFrom(std::move(other));
+    return *this;
+  }
+
+  C10_ALWAYS_INLINE Scalar& operator=(const Scalar& other) {
+    if (&other == this) {
+      return *this;
+    }
+
+    *this = Scalar(other);
+    return *this;
+  }
+
+  Scalar operator-() const;
+  Scalar conj() const;
+  Scalar log() const;
+
+  template <
+      typename T,
+      typename std::enable_if_t<!c10::is_complex<T>::value, int> = 0>
+  bool equal(T num) const {
+    if (isComplex()) {
+      TORCH_INTERNAL_ASSERT(!isSymbolic());
+      auto val = v.z;
+      return (val.real() == num) && (val.imag() == T());
+    } else if (isFloatingPoint()) {
+      return toDouble() == num;
+    } else if (tag == Tag::HAS_i) {
+      if (overflows<T>(v.i, /* strict_unsigned */ true)) {
+        return false;
+      } else {
+        return static_cast<T>(v.i) == num;
+      }
+    } else if (tag == Tag::HAS_u) {
+      if (overflows<T>(v.u, /* strict_unsigned */ true)) {
+        return false;
+      } else {
+        return static_cast<T>(v.u) == num;
+      }
+    } else if (tag == Tag::HAS_si) {
+      TORCH_INTERNAL_ASSERT(false, "NYI SymInt equality");
+    } else if (isBoolean()) {
+      // boolean scalar does not equal to a non boolean value
+      TORCH_INTERNAL_ASSERT(!isSymbolic());
+      return false;
+    } else {
+      TORCH_INTERNAL_ASSERT(false);
+    }
+  }
+
+  template <
+      typename T,
+      typename std::enable_if_t<c10::is_complex<T>::value, int> = 0>
+  bool equal(T num) const {
+    if (isComplex()) {
+      TORCH_INTERNAL_ASSERT(!isSymbolic());
+      return v.z == num;
+    } else if (isFloatingPoint()) {
+      return (toDouble() == num.real()) && (num.imag() == T());
+    } else if (tag == Tag::HAS_i) {
+      if (overflows<T>(v.i, /* strict_unsigned */ true)) {
+        return false;
+      } else {
+        return static_cast<T>(v.i) == num.real() && num.imag() == T();
+      }
+    } else if (tag == Tag::HAS_u) {
+      if (overflows<T>(v.u, /* strict_unsigned */ true)) {
+        return false;
+      } else {
+        return static_cast<T>(v.u) == num.real() && num.imag() == T();
+      }
+    } else if (tag == Tag::HAS_si) {
+      TORCH_INTERNAL_ASSERT(false, "NYI SymInt equality");
+    } else if (isBoolean()) {
+      // boolean scalar does not equal to a non boolean value
+      TORCH_INTERNAL_ASSERT(!isSymbolic());
+      return false;
+    } else {
+      TORCH_INTERNAL_ASSERT(false);
+    }
+  }
+
+  bool equal(bool num) const {
+    if (isBoolean()) {
+      TORCH_INTERNAL_ASSERT(!isSymbolic());
+      return static_cast<bool>(v.i) == num;
+    } else {
+      return false;
+    }
+  }
+
+  ScalarType type() const {
+    if (isComplex()) {
+      return ScalarType::ComplexDouble;
+    } else if (isFloatingPoint()) {
+      return ScalarType::Double;
+    } else if (isIntegral(/*includeBool=*/false)) {
+      // Represent all integers as long, UNLESS it is unsigned and therefore
+      // unrepresentable as long
+      if (Tag::HAS_u == tag) {
+        return ScalarType::UInt64;
+      }
+      return ScalarType::Long;
+    } else if (isBoolean()) {
+      return ScalarType::Bool;
+    } else {
+      throw std::runtime_error("Unknown scalar type.");
+    }
+  }
+
+  Scalar(Scalar&& rhs) noexcept : tag(rhs.tag) {
+    moveFrom(std::move(rhs));
+  }
+
+  Scalar(const Scalar& rhs) : tag(rhs.tag), v(rhs.v) {
+    if (isSymbolic()) {
+      c10::raw::intrusive_ptr::incref(v.p);
+    }
+  }
+
+  Scalar(c10::SymInt si) {
+    if (auto m = si.maybe_as_int()) {
+      tag = Tag::HAS_i;
+      v.i = *m;
+    } else {
+      tag = Tag::HAS_si;
+      v.p = std::move(si).release();
+    }
+  }
+
+  Scalar(c10::SymFloat sd) {
+    if (sd.is_symbolic()) {
+      tag = Tag::HAS_sd;
+      v.p = std::move(sd).release();
+    } else {
+      tag = Tag::HAS_d;
+      v.d = sd.as_float_unchecked();
+    }
+  }
+
+  Scalar(c10::SymBool sb) {
+    if (auto m = sb.maybe_as_bool()) {
+      tag = Tag::HAS_b;
+      v.i = *m;
+    } else {
+      tag = Tag::HAS_sb;
+      v.p = std::move(sb).release();
+    }
+  }
+
+  // We can't set v in the initializer list using the
+  // syntax v{ .member = ... } because it doesn't work on MSVC
+ private:
+  enum class Tag { HAS_d, HAS_i, HAS_u, HAS_z, HAS_b, HAS_sd, HAS_si, HAS_sb };
+
+  // Note [Meaning of HAS_u]
+  // ~~~~~~~~~~~~~~~~~~~~~~~
+  // HAS_u is a bit special.  On its face, it just means that we
+  // are holding an unsigned integer.  However, we generally don't
+  // distinguish between different bit sizes in Scalar (e.g., we represent
+  // float as double), instead, it represents a mathematical notion
+  // of some quantity (integral versus floating point).  So actually,
+  // HAS_u is used solely to represent unsigned integers that could
+  // not be represented as a signed integer.  That means only uint64_t
+  // potentially can get this tag; smaller types like uint8_t fits into a
+  // regular int and so for BC reasons we keep as an int.
+
+  // NB: assumes that self has already been cleared
+  // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+  C10_ALWAYS_INLINE void moveFrom(Scalar&& rhs) noexcept {
+    v = rhs.v;
+    tag = rhs.tag;
+    if (rhs.tag == Tag::HAS_si || rhs.tag == Tag::HAS_sd ||
+        rhs.tag == Tag::HAS_sb) {
+      // Move out of scalar
+      rhs.tag = Tag::HAS_i;
+      rhs.v.i = 0;
+    }
+  }
+
+  Tag tag;
+
+  union v_t {
+    double d{};
+    int64_t i;
+    // See Note [Meaning of HAS_u]
+    uint64_t u;
+    c10::complex<double> z;
+    c10::intrusive_ptr_target* p;
+    // NOLINTNEXTLINE(modernize-use-equals-default)
+    v_t() {} // default constructor
+  } v;
+
+  template <
+      typename T,
+      typename std::enable_if_t<
+          std::is_integral_v<T> && !std::is_same_v<T, bool>,
+          bool>* = nullptr>
+  Scalar(T vv, bool) : tag(Tag::HAS_i) {
+    v.i = convert<decltype(v.i), T>(vv);
+  }
+
+  template <
+      typename T,
+      typename std::enable_if_t<
+          !std::is_integral_v<T> && !c10::is_complex<T>::value,
+          bool>* = nullptr>
+  Scalar(T vv, bool) : tag(Tag::HAS_d) {
+    v.d = convert<decltype(v.d), T>(vv);
+  }
+
+  template <
+      typename T,
+      typename std::enable_if_t<c10::is_complex<T>::value, bool>* = nullptr>
+  Scalar(T vv, bool) : tag(Tag::HAS_z) {
+    v.z = convert<decltype(v.z), T>(vv);
+  }
+};
+
+using OptionalScalarRef = c10::OptionalRef<Scalar>;
+
+// define the scalar.to<int64_t>() specializations
+#define DEFINE_TO(T, name)         \
+  template <>                      \
+  inline T Scalar::to<T>() const { \
+    return to##name();             \
+  }
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(DEFINE_TO)
+DEFINE_TO(uint16_t, UInt16)
+DEFINE_TO(uint32_t, UInt32)
+DEFINE_TO(uint64_t, UInt64)
+#undef DEFINE_TO
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/ScalarType.h b/lib/python3.11/site-packages/torch/include/c10/core/ScalarType.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa0ef9be8412910efd044488111b2e53acc9413e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/ScalarType.h
@@ -0,0 +1,593 @@
+#pragma once
+
+#include <c10/util/BFloat16.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Float8_e4m3fn.h>
+#include <c10/util/Float8_e4m3fnuz.h>
+#include <c10/util/Float8_e5m2.h>
+#include <c10/util/Float8_e5m2fnuz.h>
+#include <c10/util/Half.h>
+#include <c10/util/bits.h>
+#include <c10/util/complex.h>
+#include <c10/util/qint32.h>
+#include <c10/util/qint8.h>
+#include <c10/util/quint2x4.h>
+#include <c10/util/quint4x2.h>
+#include <c10/util/quint8.h>
+
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+#include <unordered_map>
+
+namespace c10 {
+
+// dummy struct for uint1 to uint7, actual functionality
+// of these dtypes will be implemented in python with Tensor subclass
+template <unsigned int N>
+struct dummy_uint1_7_t {};
+
+// dummy struct for int1 to int7, actual functionality
+// of these dtypes will be implemented in python with Tensor subclass
+template <unsigned int N>
+struct dummy_int1_7_t {};
+
+// For the macros below:
+//
+// For users: If you want to macro some code for all non-QInt scalar types
+// (i.e. types with complete information, you probably want one of the
+// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND macros below, which are
+// designed to behave similarly to the Dispatch macros with the same name.
+//
+// For adding a new dtype: In the beginning, we had an idea that there was a
+// list of all scalar types, and you could use AT_FORALL_SCALAR_TYPES to
+// iterate over them.  But over the years we added weird types which couldn't
+// be handled uniformly everywhere and so in the end we ended up with some
+// mish-mosh of some helper macros, but mostly use sites making a call about
+// what dtypes they can or can't support.  So if you want to add a new dtype,
+// the preferred resolution is to find a dtype similar to what you want,
+// grep for it and edit all the sites you find this way.  If you need to add
+// a completely new kind of dtype, you're going to have to laboriously audit
+// all of the sites everywhere to figure out how it should work.  Consulting
+// some old PRs where we added new dtypes (check history of this file) can
+// help give you an idea where to start.
+
+// NB: Order matters for this macro; it is relied upon in
+// _promoteTypesLookup and the serialization format.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(_) \
+  _(uint8_t, Byte) /* 0 */                               \
+  _(int8_t, Char) /* 1 */                                \
+  _(int16_t, Short) /* 2 */                              \
+  _(int, Int) /* 3 */                                    \
+  _(int64_t, Long) /* 4 */                               \
+  _(at::Half, Half) /* 5 */                              \
+  _(float, Float) /* 6 */                                \
+  _(double, Double) /* 7 */                              \
+  _(c10::complex<c10::Half>, ComplexHalf) /* 8 */        \
+  _(c10::complex<float>, ComplexFloat) /* 9 */           \
+  _(c10::complex<double>, ComplexDouble) /* 10 */        \
+  _(bool, Bool) /* 11 */                                 \
+  _(c10::qint8, QInt8) /* 12 */                          \
+  _(c10::quint8, QUInt8) /* 13 */                        \
+  _(c10::qint32, QInt32) /* 14 */                        \
+  _(at::BFloat16, BFloat16) /* 15 */                     \
+  _(c10::quint4x2, QUInt4x2) /* 16 */                    \
+  _(c10::quint2x4, QUInt2x4) /* 17 */                    \
+  _(c10::bits1x8, Bits1x8) /* 18 */                      \
+  _(c10::bits2x4, Bits2x4) /* 19 */                      \
+  _(c10::bits4x2, Bits4x2) /* 20 */                      \
+  _(c10::bits8, Bits8) /* 21 */                          \
+  _(c10::bits16, Bits16) /* 22 */                        \
+  _(c10::Float8_e5m2, Float8_e5m2) /* 23 */              \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn) /* 24 */          \
+  _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz) /* 25 */      \
+  _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz) /* 26 */      \
+  _(uint16_t, UInt16) /* 27 */                           \
+  _(uint32_t, UInt32) /* 28 */                           \
+  _(uint64_t, UInt64) /* 29 */                           \
+  _(c10::dummy_uint1_7_t<1>, UInt1) /* 30 */             \
+  _(c10::dummy_uint1_7_t<2>, UInt2) /* 31 */             \
+  _(c10::dummy_uint1_7_t<3>, UInt3) /* 32 */             \
+  _(c10::dummy_uint1_7_t<4>, UInt4) /* 33 */             \
+  _(c10::dummy_uint1_7_t<5>, UInt5) /* 34 */             \
+  _(c10::dummy_uint1_7_t<6>, UInt6) /* 35 */             \
+  _(c10::dummy_uint1_7_t<7>, UInt7) /* 36 */             \
+  _(c10::dummy_int1_7_t<1>, Int1) /* 37 */               \
+  _(c10::dummy_int1_7_t<2>, Int2) /* 38 */               \
+  _(c10::dummy_int1_7_t<3>, Int3) /* 39 */               \
+  _(c10::dummy_int1_7_t<4>, Int4) /* 40 */               \
+  _(c10::dummy_int1_7_t<5>, Int5) /* 41 */               \
+  _(c10::dummy_int1_7_t<6>, Int6) /* 42 */               \
+  _(c10::dummy_int1_7_t<7>, Int7) /* 43 */
+
+// If you want to support ComplexHalf for real, add ComplexHalf
+// into this macro (and change the name).  But beware: convert()
+// doesn't work for all the conversions you need...
+//
+// TODO: To add unsigned int types here, we must define accumulate type.
+// But uint8 currently accumulates into int64, so we would have to make
+// an inconsistent choice for the larger types.  Difficult.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(_) \
+  _(uint8_t, Byte)                                                      \
+  _(int8_t, Char)                                                       \
+  _(int16_t, Short)                                                     \
+  _(int, Int)                                                           \
+  _(int64_t, Long)                                                      \
+  _(at::Half, Half)                                                     \
+  _(float, Float)                                                       \
+  _(double, Double)                                                     \
+  _(c10::complex<float>, ComplexFloat)                                  \
+  _(c10::complex<double>, ComplexDouble)                                \
+  _(bool, Bool)                                                         \
+  _(at::BFloat16, BFloat16)                                             \
+  _(at::Float8_e5m2, Float8_e5m2)                                       \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)
+
+// This macro controls many of our C++ APIs, including constructors
+// for Scalar as well as the data() and item() accessors on Tensor
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(_) \
+  _(uint8_t, Byte)                             \
+  _(int8_t, Char)                              \
+  _(int16_t, Short)                            \
+  _(int, Int)                                  \
+  _(int64_t, Long)                             \
+  _(at::Half, Half)                            \
+  _(float, Float)                              \
+  _(double, Double)                            \
+  _(c10::complex<c10::Half>, ComplexHalf)      \
+  _(c10::complex<float>, ComplexFloat)         \
+  _(c10::complex<double>, ComplexDouble)       \
+  _(bool, Bool)                                \
+  _(at::BFloat16, BFloat16)                    \
+  _(at::Float8_e5m2, Float8_e5m2)              \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)          \
+  _(at::Float8_e5m2fnuz, Float8_e5m2fnuz)      \
+  _(at::Float8_e4m3fnuz, Float8_e4m3fnuz)
+
+enum class ScalarType : int8_t {
+#define DEFINE_ST_ENUM_VAL_(_1, n) n,
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ST_ENUM_VAL_)
+#undef DEFINE_ENUM_ST_ENUM_VAL_
+      Undefined,
+  NumOptions
+};
+
+constexpr uint16_t NumScalarTypes =
+    static_cast<uint16_t>(ScalarType::NumOptions);
+
+namespace impl {
+
+// These are used to map ScalarTypes to C++ types.
+
+template <c10::ScalarType N>
+struct ScalarTypeToCPPType;
+
+#define SPECIALIZE_ScalarTypeToCPPType(cpp_type, scalar_type)                \
+  template <>                                                                \
+  struct ScalarTypeToCPPType<c10::ScalarType::scalar_type> {                 \
+    using type = cpp_type;                                                   \
+                                                                             \
+    /* This is a workaround for the CUDA bug which prevents */               \
+    /* ::detail::ScalarTypeToCType<T>::type being used directly due to */    \
+    /* ambiguous reference which can't to be resolved. For some reason it */ \
+    /* can't pick between at::detail and at::cuda::detail. */                \
+    /* For repro example, please see: */                                     \
+    /* https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba */    \
+    /* TODO: remove once the bug is fixed. */                                \
+    static type t;                                                           \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_ScalarTypeToCPPType)
+
+#undef SPECIALIZE_ScalarTypeToCPPType
+
+template <c10::ScalarType N>
+using ScalarTypeToCPPTypeT = typename ScalarTypeToCPPType<N>::type;
+
+} // namespace impl
+
+template <typename T>
+struct CppTypeToScalarType;
+
+#define SPECIALIZE_CppTypeToScalarType(cpp_type, scalar_type)                  \
+  template <>                                                                  \
+  struct CppTypeToScalarType<cpp_type>                                         \
+      : std::                                                                  \
+            integral_constant<c10::ScalarType, c10::ScalarType::scalar_type> { \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
+
+#undef SPECIALIZE_CppTypeToScalarType
+
+// NB: despite its generic sounding name, the macros that don't take _AND
+// are mostly only used by tensorexpr
+#define AT_FORALL_INT_TYPES(_) \
+  _(uint8_t, Byte)             \
+  _(int8_t, Char)              \
+  _(int16_t, Short)            \
+  _(int, Int)                  \
+  _(int64_t, Long)
+
+#define AT_FORALL_SCALAR_TYPES(_) \
+  _(uint8_t, Byte)                \
+  _(int8_t, Char)                 \
+  _(int16_t, Short)               \
+  _(int, Int)                     \
+  _(int64_t, Long)                \
+  _(float, Float)                 \
+  _(double, Double)
+
+// These macros are often controlling how many template instantiations we
+// create for kernels.  It is typically inappropriate to add new dtypes here,
+// instead, new types should be added to use sites on a case-by-case basis.
+// We generally are not accepting new dtypes due to binary size concerns.
+
+#define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE>::t),  \
+    SCALARTYPE)
+
+#define AT_FORALL_SCALAR_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, _) \
+  _(uint8_t, Byte)                                               \
+  _(int8_t, Char)                                                \
+  _(int16_t, Short)                                              \
+  _(int, Int)                                                    \
+  _(int64_t, Long)                                               \
+  _(float, Float)                                                \
+  _(double, Double)                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE1>::t),                \
+    SCALARTYPE1)                                                 \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE2>::t),                \
+    SCALARTYPE2)
+
+#define AT_FORALL_SCALAR_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, _) \
+  _(uint8_t, Byte)                                                            \
+  _(int8_t, Char)                                                             \
+  _(int16_t, Short)                                                           \
+  _(int, Int)                                                                 \
+  _(int64_t, Long)                                                            \
+  _(float, Float)                                                             \
+  _(double, Double)                                                           \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE1>::t),                             \
+    SCALARTYPE1)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE2>::t),                             \
+    SCALARTYPE2)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE3>::t),                             \
+    SCALARTYPE3)
+
+#define AT_FORALL_SCALAR_TYPES_AND7(              \
+    SCALARTYPE1,                                  \
+    SCALARTYPE2,                                  \
+    SCALARTYPE3,                                  \
+    SCALARTYPE4,                                  \
+    SCALARTYPE5,                                  \
+    SCALARTYPE6,                                  \
+    SCALARTYPE7,                                  \
+    _)                                            \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE1>::t), \
+    SCALARTYPE1)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE2>::t), \
+    SCALARTYPE2)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE3>::t), \
+    SCALARTYPE3)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE4>::t), \
+    SCALARTYPE4)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE5>::t), \
+    SCALARTYPE5)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE6>::t), \
+    SCALARTYPE6)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE7>::t), \
+    SCALARTYPE7)
+
+#define AT_FORALL_QINT_TYPES(_) \
+  _(c10::qint8, QInt8)          \
+  _(c10::quint8, QUInt8)        \
+  _(c10::qint32, QInt32)        \
+  _(c10::quint4x2, QUInt4x2)    \
+  _(c10::quint2x4, QUInt2x4)
+
+#define AT_FORALL_COMPLEX_TYPES(_)     \
+  _(c10::complex<float>, ComplexFloat) \
+  _(c10::complex<double>, ComplexDouble)
+
+#define DEFINE_CONSTANT(_, name) \
+  constexpr ScalarType k##name = ScalarType::name;
+
+// NOLINTNEXTLINE(clang-diagnostic-unused-const-variable)
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CONSTANT)
+#undef DEFINE_CONSTANT
+
+inline const char* toString(ScalarType t) {
+#define DEFINE_CASE(_, name) \
+  case ScalarType::name:     \
+    return #name;
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE)
+    default:
+      return "UNKNOWN_SCALAR";
+  }
+#undef DEFINE_CASE
+}
+
+inline size_t elementSize(ScalarType t) {
+#define CASE_ELEMENTSIZE_CASE(ctype, name) \
+  case ScalarType::name:                   \
+    return sizeof(ctype);
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(CASE_ELEMENTSIZE_CASE)
+    default:
+      TORCH_CHECK(false, "Unknown ScalarType");
+  }
+#undef CASE_ELEMENTSIZE_CASE
+}
+
+inline bool isIntegralType(ScalarType t, bool includeBool) {
+  bool isIntegral =
+      (t == ScalarType::Byte || t == ScalarType::Char || t == ScalarType::Int ||
+       t == ScalarType::Long || t == ScalarType::Short ||
+       t == ScalarType::UInt16 || t == ScalarType::UInt32 ||
+       t == ScalarType::UInt64);
+
+  return isIntegral || (includeBool && t == ScalarType::Bool);
+}
+
+C10_DEPRECATED_MESSAGE(
+    "isIntegralType is deprecated. Please use the overload with 'includeBool' parameter instead.")
+inline bool isIntegralType(ScalarType t) {
+  return isIntegralType(t, /*includeBool=*/false);
+}
+
+inline bool isFloat8Type(ScalarType t) {
+  return t == ScalarType::Float8_e5m2 || t == ScalarType::Float8_e5m2fnuz ||
+      t == ScalarType::Float8_e4m3fn || t == ScalarType::Float8_e4m3fnuz;
+}
+
+inline bool isReducedFloatingType(ScalarType t) {
+  return t == ScalarType::Half || t == ScalarType::BFloat16 || isFloat8Type(t);
+}
+
+inline bool isFloatingType(ScalarType t) {
+  return t == ScalarType::Double || t == ScalarType::Float ||
+      isReducedFloatingType(t);
+}
+
+inline bool isComplexType(ScalarType t) {
+  return (
+      t == ScalarType::ComplexHalf || t == ScalarType::ComplexFloat ||
+      t == ScalarType::ComplexDouble);
+}
+
+inline bool isQIntType(ScalarType t) {
+  // Don't forget to extend this when adding new QInt types
+  return t == ScalarType::QInt8 || t == ScalarType::QUInt8 ||
+      t == ScalarType::QInt32 || t == ScalarType::QUInt4x2 ||
+      t == ScalarType::QUInt2x4;
+}
+
+inline bool isBitsType(ScalarType t) {
+  return t == ScalarType::Bits1x8 || t == ScalarType::Bits2x4 ||
+      t == ScalarType::Bits4x2 || t == ScalarType::Bits8 ||
+      t == ScalarType::Bits16;
+}
+
+inline bool isBarebonesUnsignedType(ScalarType t) {
+  return t == ScalarType::UInt1 || t == ScalarType::UInt2 ||
+      t == ScalarType::UInt3 || t == ScalarType::UInt4 ||
+      t == ScalarType::UInt5 || t == ScalarType::UInt6 ||
+      t == ScalarType::UInt7 || t == ScalarType::UInt16 ||
+      t == ScalarType::UInt32 || t == ScalarType::UInt64;
+}
+
+inline ScalarType toQIntType(ScalarType t) {
+  switch (t) {
+    case ScalarType::Byte:
+      return ScalarType::QUInt8;
+    case ScalarType::Char:
+      return ScalarType::QInt8;
+    case ScalarType::Int:
+      return ScalarType::QInt32;
+    default:
+      return t;
+  }
+}
+
+inline ScalarType toUnderlying(ScalarType t) {
+  switch (t) {
+    case ScalarType::QUInt8:
+    case ScalarType::QUInt4x2:
+      [[fallthrough]];
+    case ScalarType::QUInt2x4:
+      return ScalarType::Byte;
+    case ScalarType::QInt8:
+      return ScalarType::Char;
+    case ScalarType::QInt32:
+      return ScalarType::Int;
+    default:
+      return t;
+  }
+}
+
+inline bool isSignedType(ScalarType t) {
+#define CASE_ISSIGNED(name)     \
+  case ScalarType::name:        \
+    return std::numeric_limits< \
+        ::c10::impl::ScalarTypeToCPPTypeT<ScalarType::name>>::is_signed;
+
+  switch (t) {
+    case ScalarType::QInt8:
+    case ScalarType::QUInt8:
+    case ScalarType::QInt32:
+    case ScalarType::QUInt4x2:
+    case ScalarType::QUInt2x4:
+      TORCH_CHECK(false, "isSignedType not supported for quantized types");
+    case ScalarType::Bits1x8:
+    case ScalarType::Bits2x4:
+    case ScalarType::Bits4x2:
+    case ScalarType::Bits8:
+    case ScalarType::Bits16:
+      TORCH_CHECK(false, "Bits types are undefined");
+      CASE_ISSIGNED(UInt16);
+      CASE_ISSIGNED(UInt32);
+      CASE_ISSIGNED(UInt64);
+      CASE_ISSIGNED(BFloat16);
+      CASE_ISSIGNED(Float8_e5m2);
+      CASE_ISSIGNED(Float8_e5m2fnuz);
+      CASE_ISSIGNED(Float8_e4m3fn);
+      CASE_ISSIGNED(Float8_e4m3fnuz);
+      CASE_ISSIGNED(Byte);
+      CASE_ISSIGNED(Char);
+      CASE_ISSIGNED(Short);
+      CASE_ISSIGNED(Int);
+      CASE_ISSIGNED(Long);
+      CASE_ISSIGNED(Half);
+      CASE_ISSIGNED(Float);
+      CASE_ISSIGNED(Double);
+      CASE_ISSIGNED(ComplexHalf);
+      CASE_ISSIGNED(ComplexFloat);
+      CASE_ISSIGNED(ComplexDouble);
+      CASE_ISSIGNED(Bool);
+    case ScalarType::Int1:
+    case ScalarType::Int2:
+    case ScalarType::Int3:
+    case ScalarType::Int4:
+    case ScalarType::Int5:
+    case ScalarType::Int6:
+    case ScalarType::Int7:
+      return true;
+    case ScalarType::UInt1:
+    case ScalarType::UInt2:
+    case ScalarType::UInt3:
+    case ScalarType::UInt4:
+    case ScalarType::UInt5:
+    case ScalarType::UInt6:
+    case ScalarType::UInt7:
+      return false;
+    case ScalarType::Undefined:
+    case ScalarType::NumOptions:
+      break;
+      // Do not add default here, but rather define behavior of every new entry
+      // here.  `-Wswitch-enum` would raise a warning in those cases.
+  }
+  TORCH_CHECK(false, "Unknown ScalarType ", t);
+#undef CASE_ISSIGNED
+}
+
+inline bool isUnderlying(ScalarType type, ScalarType qtype) {
+  return type == toUnderlying(qtype);
+}
+
+inline ScalarType toRealValueType(ScalarType t) {
+  switch (t) {
+    case ScalarType::ComplexHalf:
+      return ScalarType::Half;
+    case ScalarType::ComplexFloat:
+      return ScalarType::Float;
+    case ScalarType::ComplexDouble:
+      return ScalarType::Double;
+    default:
+      return t;
+  }
+}
+
+inline ScalarType toComplexType(ScalarType t) {
+  switch (t) {
+    case ScalarType::BFloat16:
+      // BFloat16 has range equivalent to Float,
+      // so we map it to ComplexFloat.
+      return ScalarType::ComplexFloat;
+    case ScalarType::Half:
+      return ScalarType::ComplexHalf;
+    case ScalarType::Float:
+      return ScalarType::ComplexFloat;
+    case ScalarType::Double:
+      return ScalarType::ComplexDouble;
+    case ScalarType::ComplexHalf:
+      return ScalarType::ComplexHalf;
+    case ScalarType::ComplexFloat:
+      return ScalarType::ComplexFloat;
+    case ScalarType::ComplexDouble:
+      return ScalarType::ComplexDouble;
+    default:
+      TORCH_CHECK(false, "Unknown Complex ScalarType for ", t);
+  }
+}
+
+// see tensor_attributes.rst for detailed explanation and examples
+// of casting rules.
+inline bool canCast(const ScalarType from, const ScalarType to) {
+  // We disallow complex -> non complex, e.g., float_tensor *= complex is
+  // disallowed.
+  if (isComplexType(from) && !isComplexType(to)) {
+    return false;
+  }
+  // We disallow float -> integral, e.g., int_tensor *= float is disallowed.
+  if (isFloatingType(from) && isIntegralType(to, false)) {
+    return false;
+  }
+
+  // Treat bool as a distinct "category," to be consistent with type promotion
+  // rules (e.g. `bool_tensor + 5 -> int64_tensor`). If `5` was in the same
+  // category as `bool_tensor`, we would not promote. Differing categories
+  // implies `bool_tensor += 5` is disallowed.
+  //
+  // NB: numpy distinguishes "unsigned" as a category to get the desired
+  // `bool_tensor + 5 -> int64_tensor` behavior. We don't, because:
+  // * We don't want the performance hit of checking the runtime sign of
+  // Scalars.
+  // * `uint8_tensor + 5 -> int64_tensor` would be undesirable.
+  if (from != ScalarType::Bool && to == ScalarType::Bool) {
+    return false;
+  }
+  return true;
+}
+
+C10_API ScalarType promoteTypes(ScalarType a, ScalarType b);
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::ScalarType scalar_type) {
+  return stream << toString(scalar_type);
+}
+
+// Returns a pair of strings representing the names for each dtype.
+// The returned pair is (name, legacy_name_if_applicable)
+C10_API std::pair<std::string, std::string> getDtypeNames(
+    c10::ScalarType scalarType);
+
+// Returns a map of string name to dtype.
+C10_API const std::unordered_map<std::string, ScalarType>& getStringToDtypeMap();
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/ScalarTypeToTypeMeta.h b/lib/python3.11/site-packages/torch/include/c10/core/ScalarTypeToTypeMeta.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f5e1af2aa37cada80fe4d1c254909d4e3a4032f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/ScalarTypeToTypeMeta.h
@@ -0,0 +1,57 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/util/Optional.h>
+#include <c10/util/typeid.h>
+
+// these just expose TypeMeta/ScalarType bridge functions in c10
+// TODO move to typeid.h (or codemod away) when TypeMeta et al
+// are moved from caffe2 to c10 (see note at top of typeid.h)
+
+namespace c10 {
+
+/**
+ * convert ScalarType enum values to TypeMeta handles
+ */
+inline caffe2::TypeMeta scalarTypeToTypeMeta(ScalarType scalar_type) {
+  return caffe2::TypeMeta::fromScalarType(scalar_type);
+}
+
+/**
+ * convert TypeMeta handles to ScalarType enum values
+ */
+inline ScalarType typeMetaToScalarType(caffe2::TypeMeta dtype) {
+  return dtype.toScalarType();
+}
+
+/**
+ * typeMetaToScalarType(), lifted to optional
+ */
+inline std::optional<at::ScalarType> optTypeMetaToScalarType(
+    std::optional<caffe2::TypeMeta> type_meta) {
+  if (!type_meta.has_value()) {
+    return std::nullopt;
+  }
+  return type_meta->toScalarType();
+}
+
+/**
+ * convenience: equality across TypeMeta/ScalarType conversion
+ */
+inline bool operator==(ScalarType t, caffe2::TypeMeta m) {
+  return m.isScalarType(t);
+}
+
+inline bool operator==(caffe2::TypeMeta m, ScalarType t) {
+  return t == m;
+}
+
+inline bool operator!=(ScalarType t, caffe2::TypeMeta m) {
+  return !(t == m);
+}
+
+inline bool operator!=(caffe2::TypeMeta m, ScalarType t) {
+  return !(t == m);
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Storage.h b/lib/python3.11/site-packages/torch/include/c10/core/Storage.h
new file mode 100644
index 0000000000000000000000000000000000000000..df86463dc449cac89ab4d185d238938a8195959a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Storage.h
@@ -0,0 +1,272 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/StorageImpl.h>
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <c10/util/ExclusivelyOwned.h>
+#include <c10/util/MaybeOwned.h>
+#include <c10/util/UniqueVoidPtr.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstddef>
+#include <utility>
+
+namespace c10 {
+
+struct Storage;
+
+C10_API bool isSharedStorageAlias(
+    const Storage& storage0,
+    const Storage& storage1);
+
+struct C10_API Storage {
+ public:
+  struct use_byte_size_t {};
+  struct unsafe_borrow_t {
+    explicit unsafe_borrow_t() = default;
+  };
+
+  Storage() = default;
+  Storage(c10::intrusive_ptr<StorageImpl> ptr)
+      : storage_impl_(std::move(ptr)) {}
+
+  // Allocates memory buffer using given allocator and creates a storage with it
+  Storage(
+      use_byte_size_t /*use_byte_size*/,
+      const SymInt& size_bytes,
+      Allocator* allocator = nullptr,
+      bool resizable = false)
+      : storage_impl_(c10::make_intrusive<StorageImpl>(
+            StorageImpl::use_byte_size_t(),
+            size_bytes,
+            allocator,
+            resizable)) {}
+
+  // Creates storage with pre-allocated memory buffer. Allocator is given for
+  // potential future reallocations, however it can be nullptr if the storage
+  // is non-resizable
+  Storage(
+      use_byte_size_t /*use_byte_size*/,
+      size_t size_bytes,
+      at::DataPtr data_ptr,
+      at::Allocator* allocator = nullptr,
+      bool resizable = false)
+      : storage_impl_(c10::make_intrusive<StorageImpl>(
+            StorageImpl::use_byte_size_t(),
+            size_bytes,
+            std::move(data_ptr),
+            allocator,
+            resizable)) {}
+
+ protected:
+  explicit Storage(unsafe_borrow_t, const Storage& rhs)
+      : storage_impl_(c10::intrusive_ptr<c10::StorageImpl>::reclaim(
+            rhs.storage_impl_.get())) {}
+
+  friend MaybeOwnedTraits<Storage>;
+
+ public:
+  // Legacy constructor for partially initialized (dtype or memory) storages
+  // that can be temporarily created with Caffe2 APIs. See the note on top of
+  // TensorImpl.h for details.
+  static Storage create_legacy(at::Device device) {
+    auto allocator = GetAllocator(device.type());
+    return Storage(c10::make_intrusive<StorageImpl>(
+        StorageImpl::use_byte_size_t(),
+        0,
+        allocator->allocate(0), // materialize a non-default Device.
+        allocator,
+        true));
+  }
+
+  // Mimic create_legacy, but without requiring a newly-created StorageImpl.
+  void reset_legacy() {
+    TORCH_CHECK(resizable() && allocator());
+    set_nbytes(0);
+    set_data_ptr_noswap(allocator()->allocate(0));
+  }
+
+  // TODO: remove later
+  void set_nbytes(size_t size_bytes) const {
+    storage_impl_->set_nbytes(size_bytes);
+  }
+
+  void set_nbytes(c10::SymInt size_bytes) const {
+    storage_impl_->set_nbytes(std::move(size_bytes));
+  }
+
+  bool resizable() const {
+    return storage_impl_->resizable();
+  }
+
+  size_t nbytes() const {
+    return storage_impl_->nbytes();
+  }
+
+  SymInt sym_nbytes() const {
+    return storage_impl_->sym_nbytes();
+  }
+  // get() use here is to get const-correctness
+
+  const void* data() const {
+    return storage_impl_->data();
+  }
+
+  void* mutable_data() const {
+    return storage_impl_->mutable_data();
+  }
+
+  at::DataPtr& mutable_data_ptr() const {
+    return storage_impl_->mutable_data_ptr();
+  }
+
+  const at::DataPtr& data_ptr() const {
+    return storage_impl_->data_ptr();
+  }
+
+  // Returns the previous data_ptr
+  at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) const {
+    return storage_impl_->set_data_ptr(std::move(data_ptr));
+  }
+
+  void set_data_ptr_noswap(at::DataPtr&& data_ptr) const {
+    return storage_impl_->set_data_ptr_noswap(std::move(data_ptr));
+  }
+
+  DeviceType device_type() const {
+    return storage_impl_->device_type();
+  }
+
+  at::Allocator* allocator() const {
+    return storage_impl_->allocator();
+  }
+
+  at::Device device() const {
+    return storage_impl_->device();
+  }
+
+  StorageImpl* unsafeReleaseStorageImpl() {
+    return storage_impl_.release();
+  }
+
+  StorageImpl* unsafeGetStorageImpl() const noexcept {
+    return storage_impl_.get();
+  }
+
+  c10::weak_intrusive_ptr<StorageImpl> getWeakStorageImpl() const {
+    return c10::weak_intrusive_ptr<StorageImpl>(storage_impl_);
+  }
+
+  operator bool() const {
+    return storage_impl_;
+  }
+
+  size_t use_count() const {
+    return storage_impl_.use_count();
+  }
+
+  inline bool unique() const {
+    return storage_impl_.unique();
+  }
+
+  bool is_alias_of(const Storage& other) const {
+    return (
+        storage_impl_ == other.storage_impl_ ||
+        isSharedStorageAlias(*this, other));
+  }
+
+  void UniqueStorageShareExternalPointer(
+      void* src,
+      size_t capacity,
+      DeleterFnPtr d = nullptr) {
+    if (!storage_impl_.unique()) {
+      TORCH_CHECK(
+          false,
+          "UniqueStorageShareExternalPointer can only be called when use_count == 1");
+    }
+    storage_impl_->UniqueStorageShareExternalPointer(src, capacity, d);
+  }
+
+  void UniqueStorageShareExternalPointer(
+      at::DataPtr&& data_ptr,
+      size_t capacity) {
+    if (!storage_impl_.unique()) {
+      TORCH_CHECK(
+          false,
+          "UniqueStorageShareExternalPointer can only be called when use_count == 1");
+    }
+    storage_impl_->UniqueStorageShareExternalPointer(
+        std::move(data_ptr), capacity);
+  }
+
+ protected:
+  c10::intrusive_ptr<StorageImpl> storage_impl_;
+};
+
+template <>
+struct MaybeOwnedTraits<c10::Storage> {
+  using owned_type = c10::Storage;
+  using borrow_type = c10::Storage;
+
+  static borrow_type createBorrow(const owned_type& from) {
+    return borrow_type(borrow_type::unsafe_borrow_t{}, from);
+  }
+
+  static void assignBorrow(borrow_type& lhs, const borrow_type& rhs) {
+    lhs.unsafeReleaseStorageImpl();
+    lhs = borrow_type(borrow_type::unsafe_borrow_t{}, rhs);
+  }
+
+  static void destroyBorrow(borrow_type& toDestroy) {
+    toDestroy.unsafeReleaseStorageImpl(); // "leak" it, but it was already +0.
+  }
+
+  static const owned_type& referenceFromBorrow(const borrow_type& borrow) {
+    return borrow;
+  }
+
+  static const owned_type* pointerFromBorrow(const borrow_type& borrow) {
+    return &borrow;
+  }
+
+  static bool debugBorrowIsValid(const borrow_type& /*borrow*/) {
+    return true;
+  }
+};
+
+template <>
+struct ExclusivelyOwnedTraits<c10::Storage> {
+  using repr_type = c10::Storage;
+  using pointer_type = c10::Storage*;
+  using const_pointer_type = const c10::Storage*;
+
+  static repr_type nullRepr() {
+    return c10::Storage();
+  }
+
+  template <class... Args>
+  static repr_type createInPlace(Args&&... args) {
+    return c10::Storage(std::forward<Args>(args)...);
+  }
+
+  static repr_type moveToRepr(c10::Storage&& x) {
+    return std::move(x);
+  }
+
+  static c10::Storage take(c10::Storage& x) {
+    return std::move(x);
+  }
+
+  static pointer_type getImpl(repr_type& x) {
+    return &x;
+  }
+
+  static const_pointer_type getImpl(const repr_type& x) {
+    return &x;
+  }
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/StorageImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/StorageImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..e45b4953b9c90ecc3d81163d370f49af652b1cb5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/StorageImpl.h
@@ -0,0 +1,368 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/SymInt.h>
+#include <c10/core/impl/COW.h>
+#include <c10/core/impl/COWDeleter.h>
+#include <c10/core/impl/PyObjectSlot.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <c10/util/UniqueVoidPtr.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstddef>
+#include <utility>
+
+namespace c10 {
+
+[[noreturn]] C10_API void throwNullDataPtrError();
+C10_API void warnDeprecatedDataPtr();
+
+// Used in StorageImpl to store extra metadata.
+// Currently used only for storing a custom error message
+// used when throwing an exception when data_ptr is accessed.
+struct C10_API StorageExtraMeta {
+  std::optional<std::string> custom_data_ptr_error_msg_ = std::nullopt;
+};
+
+// A storage represents the underlying backing data buffer for a
+// tensor.  This concept was inherited from the original Torch7
+// codebase; we'd kind of like to get rid of the concept
+// (see https://github.com/pytorch/pytorch/issues/14797) but
+// it's hard work and no one has gotten around to doing it.
+//
+// NB: storage is supposed to uniquely own a data pointer; e.g.,
+// two non-null data pointers alias if and only if they are from
+// the same storage.  Technically you can violate this invariant
+// (e.g., you can create a non-owning StorageImpl with at::from_blob)
+// but a lot of things won't work correctly, including:
+//
+// - An ordinary deleter on such a storage is wrong, because normal deleters
+//   assume unique ownership, but if you have two storages at the same data,
+//   that implies there is some sort of shared ownership. So your deleter would
+//   have to actually be internally doing some sort of refcount thing
+// - Deepcopy in Python side relies on storage equality and not data pointer
+//   equality; so if there are two separate storages pointing to the same data,
+//   the data will actually get duplicated in that case (one data ptr before,
+//   two data ptrs after)
+// - Version counts won't work correctly, because we do all VC tracking at the
+//   level of storages (unless you explicitly disconnect the VC with detach);
+//   mutation because data pointers are the same are totally untracked
+struct C10_API StorageImpl : public c10::intrusive_ptr_target {
+ public:
+  struct use_byte_size_t {};
+
+  StorageImpl(
+      use_byte_size_t /*use_byte_size*/,
+      SymInt size_bytes,
+      at::DataPtr data_ptr,
+      at::Allocator* allocator,
+      bool resizable)
+      : data_ptr_(std::move(data_ptr)),
+        size_bytes_(std::move(size_bytes)),
+        size_bytes_is_heap_allocated_(size_bytes_.is_heap_allocated()),
+        resizable_(resizable),
+        received_cuda_(false),
+        allocator_(allocator) {
+    if (resizable) {
+      TORCH_INTERNAL_ASSERT(
+          allocator_, "For resizable storage, allocator must be provided");
+    }
+    refresh_has_data_ptr_check();
+  }
+
+  StorageImpl(
+      use_byte_size_t /*use_byte_size*/,
+      const SymInt& size_bytes,
+      at::Allocator* allocator,
+      bool resizable)
+      : StorageImpl(
+            use_byte_size_t(),
+            size_bytes,
+            size_bytes.is_heap_allocated()
+                ? allocator->allocate(0)
+                : allocator->allocate(size_bytes.as_int_unchecked()),
+            allocator,
+            resizable) {}
+
+  StorageImpl& operator=(StorageImpl&& other) = delete;
+  StorageImpl& operator=(const StorageImpl&) = delete;
+  StorageImpl() = delete;
+  StorageImpl(StorageImpl&& other) = delete;
+  StorageImpl(const StorageImpl&) = delete;
+  ~StorageImpl() override = default;
+
+  void reset() {
+    data_ptr_.clear();
+    size_bytes_ = 0;
+    size_bytes_is_heap_allocated_ = false;
+  }
+
+  // Destructor doesn't call release_resources because it's
+  // unnecessary; don't forget to change that if needed!
+  void release_resources() override {
+    data_ptr_.clear();
+  }
+
+  size_t nbytes() const {
+    // OK to do this instead of maybe_as_int as nbytes is guaranteed positive
+    TORCH_CHECK(!size_bytes_is_heap_allocated_);
+    return size_bytes_.as_int_unchecked();
+  }
+
+  SymInt sym_nbytes() const {
+    return size_bytes_;
+  }
+
+  // TODO: remove later
+  void set_nbytes(size_t size_bytes) {
+    size_bytes_ = static_cast<int64_t>(size_bytes);
+    size_bytes_is_heap_allocated_ = false;
+  }
+
+  void set_nbytes(c10::SymInt size_bytes) {
+    size_bytes_ = std::move(size_bytes);
+  }
+
+  bool resizable() const {
+    return resizable_;
+  }
+
+  const at::DataPtr& data_ptr() const {
+    if (C10_UNLIKELY(throw_on_immutable_data_ptr_)) {
+      throw_data_ptr_access_error();
+    }
+    return data_ptr_;
+  }
+
+  at::DataPtr& mutable_data_ptr() {
+    if (C10_UNLIKELY(has_mutable_data_ptr_check_)) {
+      if (throw_on_immutable_data_ptr_) {
+        throw_data_ptr_access_error();
+      }
+      if (throw_on_mutable_data_ptr_) {
+        throwNullDataPtrError();
+      }
+      if (warn_deprecated_on_mutable_data_ptr_) {
+        warnDeprecatedDataPtr();
+      }
+      maybe_materialize_cow();
+    }
+    return data_ptr_;
+  }
+
+  // Returns the data_ptr. Bypasses all checks.
+  at::DataPtr& _mutable_data_ptr_no_checks() {
+    return data_ptr_;
+  }
+
+  // Returns the previous data_ptr
+  at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) {
+    // We need to materialize the old COW DataPtr because it is
+    // being returned as mutable.
+    maybe_materialize_cow();
+    return set_data_ptr_no_materialize_cow(std::move(data_ptr));
+  }
+
+  void set_data_ptr_noswap(at::DataPtr&& data_ptr) {
+    data_ptr_ = std::move(data_ptr);
+    refresh_has_data_ptr_check();
+  }
+
+  const void* data() const {
+    if (C10_UNLIKELY(throw_on_immutable_data_ptr_)) {
+      throw_data_ptr_access_error();
+    }
+    return data_ptr_.get();
+  }
+
+  void* mutable_data() {
+    if (C10_UNLIKELY(has_mutable_data_ptr_check_)) {
+      if (throw_on_immutable_data_ptr_) {
+        throw_data_ptr_access_error();
+      }
+      if (throw_on_mutable_data_ptr_) {
+        throwNullDataPtrError();
+      }
+      if (warn_deprecated_on_mutable_data_ptr_) {
+        warnDeprecatedDataPtr();
+      }
+      maybe_materialize_cow();
+    }
+    return data_ptr_.mutable_get();
+  }
+
+  at::DeviceType device_type() const {
+    return data_ptr_.device().type();
+  }
+
+  at::Allocator* allocator() {
+    return allocator_;
+  }
+
+  const at::Allocator* allocator() const {
+    return allocator_;
+  }
+
+  // You generally shouldn't use this method, but it is occasionally
+  // useful if you want to override how a tensor will be reallocated,
+  // after it was already allocated (and its initial allocator was
+  // set)
+  void set_allocator(at::Allocator* allocator) {
+    allocator_ = allocator;
+  }
+
+  Device device() const {
+    return data_ptr_.device();
+  }
+
+  void set_resizable(bool resizable) {
+    if (resizable) {
+      // We need an allocator to be resizable
+      AT_ASSERT(allocator_);
+    }
+    resizable_ = resizable;
+  }
+
+  /**
+   * Can only be called when use_count is 1
+   */
+  void UniqueStorageShareExternalPointer(
+      void* src,
+      size_t size_bytes,
+      DeleterFnPtr d = nullptr) {
+    UniqueStorageShareExternalPointer(
+        at::DataPtr(src, src, d, data_ptr_.device()), size_bytes);
+  }
+
+  /**
+   * Can only be called when use_count is 1
+   */
+  void UniqueStorageShareExternalPointer(
+      at::DataPtr&& data_ptr,
+      size_t size_bytes) {
+    data_ptr_ = std::move(data_ptr);
+    size_bytes_ = static_cast<int64_t>(size_bytes);
+    size_bytes_is_heap_allocated_ = false;
+    allocator_ = nullptr;
+    resizable_ = false;
+  }
+
+  // This method can be used only after storage construction and cannot be used
+  // to modify storage status
+  void set_received_cuda(bool received_cuda) {
+    received_cuda_ = received_cuda;
+  }
+
+  bool received_cuda() {
+    return received_cuda_;
+  }
+
+  impl::PyObjectSlot* pyobj_slot() {
+    return &pyobj_slot_;
+  }
+
+  const impl::PyObjectSlot* pyobj_slot() const {
+    return &pyobj_slot_;
+  }
+
+  StorageExtraMeta& get_extra_meta() {
+    if (!extra_meta_) {
+      extra_meta_ = std::make_unique<StorageExtraMeta>();
+    }
+    return *extra_meta_;
+  }
+
+  [[noreturn]] void throw_data_ptr_access_error() const;
+
+  void release_data_and_set_meta_custom_data_ptr_error_msg_(
+      std::optional<std::string> s) {
+    throw_on_immutable_data_ptr_ = true;
+    get_extra_meta().custom_data_ptr_error_msg_ = std::move(s);
+    refresh_has_data_ptr_check();
+  }
+
+  void set_throw_on_mutable_data_ptr() {
+    throw_on_mutable_data_ptr_ = true;
+    refresh_has_data_ptr_check();
+  }
+
+  void set_warn_deprecated_on_mutable_data_ptr() {
+    warn_deprecated_on_mutable_data_ptr_ = true;
+    refresh_has_data_ptr_check();
+  }
+
+ protected:
+  // materialize_cow_storage needs to call set_data_ptr_no_materlize_cow
+  friend void c10::impl::cow::materialize_cow_storage(StorageImpl& storage);
+
+  // Returns the previous data_ptr. If the old data_ptr was COW,
+  // this avoids materializing it
+  at::DataPtr set_data_ptr_no_materialize_cow(at::DataPtr&& data_ptr) {
+    at::DataPtr old_data_ptr(std::move(data_ptr_));
+    data_ptr_ = std::move(data_ptr);
+    refresh_has_data_ptr_check();
+    return old_data_ptr;
+  }
+
+ private:
+  void refresh_has_data_ptr_check() {
+    has_mutable_data_ptr_check_ = is_cow() || throw_on_mutable_data_ptr_ ||
+        warn_deprecated_on_mutable_data_ptr_ || throw_on_immutable_data_ptr_;
+  }
+
+  inline bool is_cow() const {
+    return c10::impl::cow::is_cow_data_ptr(data_ptr_);
+  }
+
+  // Triggers a copy if this is a copy-on-write tensor.
+  void maybe_materialize_cow() {
+    if (is_cow()) {
+      impl::cow::materialize_cow_storage(*this);
+    }
+  }
+
+  DataPtr data_ptr_;
+  SymInt size_bytes_;
+  bool size_bytes_is_heap_allocated_;
+  bool resizable_;
+  // Identifies that Storage was received from another process and doesn't have
+  // local to process cuda memory allocation
+  bool received_cuda_;
+  // All special checks in data/data_ptr calls are guarded behind this single
+  // boolean. This is for performance: .data/.data_ptr calls are commonly in the
+  // hot-path.
+  bool has_mutable_data_ptr_check_ = false;
+  // If we should throw when mutable_data_ptr() or mutable_data() is called.
+  bool throw_on_mutable_data_ptr_ = false;
+  // If we should throw when data_ptr() or data() is called.
+  bool throw_on_immutable_data_ptr_ = false;
+  // If we warn when mutable_data_ptr() or mutable_data() is called.
+  bool warn_deprecated_on_mutable_data_ptr_ = false;
+  Allocator* allocator_;
+  impl::PyObjectSlot pyobj_slot_;
+  std::unique_ptr<StorageExtraMeta> extra_meta_ = nullptr;
+};
+
+// Declare StorageImpl create function pointer types.
+using StorageImplCreateHelper = intrusive_ptr<StorageImpl> (*)(
+    StorageImpl::use_byte_size_t,
+    SymInt size_bytes,
+    DataPtr data_ptr,
+    Allocator* allocator,
+    bool resizable);
+
+C10_API void SetStorageImplCreate(DeviceType t, StorageImplCreateHelper fptr);
+
+C10_API StorageImplCreateHelper GetStorageImplCreate(DeviceType t);
+
+C10_API c10::intrusive_ptr<c10::StorageImpl> make_storage_impl(
+    c10::StorageImpl::use_byte_size_t use_byte_size,
+    c10::SymInt size_bytes,
+    c10::DataPtr data_ptr,
+    c10::Allocator* allocator,
+    bool resizable,
+    std::optional<at::Device> device_opt);
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/Stream.h b/lib/python3.11/site-packages/torch/include/c10/core/Stream.h
new file mode 100644
index 0000000000000000000000000000000000000000..a35e608202c7be4a1bc7b569051745a3f3074124
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/Stream.h
@@ -0,0 +1,176 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <ostream>
+
+namespace c10 {
+
+/// An index representing a specific stream.  A StreamId is not independently
+/// meaningful without knowing the Device it is associated with; try to
+/// use Stream rather than StreamId directly.
+///
+/// StreamIds are opaque; they are assigned by some DeviceType-specific
+/// numbering system which is not visible to the user.  HOWEVER, we
+/// guarantee that StreamId 0 is always a valid stream, and corresponds
+/// to some sort of "default" stream.
+using StreamId = int64_t;
+
+struct C10_API StreamData3 {
+  StreamId stream_id;
+  DeviceIndex device_index;
+  DeviceType device_type;
+};
+
+// NB: I decided not to call the above StreamIndex to avoid confusion with
+// DeviceIndex.  This way, you access device index with index(), and stream id
+// with id()
+
+/**
+ * A stream is a software mechanism used to synchronize launched kernels
+ * without requiring explicit synchronizations between kernels.  The basic
+ * model is that every kernel launch is associated with a stream: every
+ * kernel on the same stream is implicitly synchronized so that if I launch
+ * kernels A and B on the same stream, A is guaranteed to finish before B
+ * launches.  If I want B to run concurrently with A, I must schedule
+ * it on a different stream.
+ *
+ * The Stream class is a backend agnostic value class representing a stream
+ * which I may schedule a kernel on.  Every stream is associated with a device,
+ * which is recorded in stream, which is used to avoid confusion about which
+ * device a stream refers to.
+ *
+ * Streams are explicitly thread-safe, in the sense that it is OK to pass
+ * a Stream from one thread to another, and kernels queued from two different
+ * threads will still get serialized appropriately.  (Of course, the
+ * time when the kernels get queued is undetermined unless you synchronize
+ * host side ;)
+ *
+ * Stream does NOT have a default constructor.  Streams are for expert
+ * users; if you want to use Streams, we're going to assume you know
+ * how to deal with C++ template error messages if you try to
+ * resize() a vector of Streams.
+ *
+ * Known instances of streams in backends:
+ *
+ *  - cudaStream_t (CUDA)
+ *  - hipStream_t (HIP)
+ *  - cl_command_queue (OpenCL)  (NB: Caffe2's existing OpenCL integration
+ *    does NOT support command queues.)
+ *
+ * Because this class is device agnostic, it cannot provide backend-specific
+ * functionality (e.g., get the cudaStream_t of a CUDA stream.)  There are
+ * wrapper classes which provide this functionality, e.g., CUDAStream.
+ */
+class C10_API Stream final {
+ private:
+  Device device_;
+  StreamId id_;
+
+ public:
+  enum Unsafe { UNSAFE };
+  enum Default { DEFAULT };
+
+  /// Unsafely construct a stream from a Device and a StreamId.  In
+  /// general, only specific implementations of streams for a
+  /// backend should manufacture Stream directly in this way; other users
+  /// should use the provided APIs to get a stream.  In particular,
+  /// we don't require backends to give any guarantees about non-zero
+  /// StreamIds; they are welcome to allocate in whatever way they like.
+  explicit Stream(Unsafe, Device device, StreamId id)
+      : device_(device), id_(id) {}
+
+  /// Construct the default stream of a Device.  The default stream is
+  /// NOT the same as the current stream; default stream is a fixed stream
+  /// that never changes, whereas the current stream may be changed by
+  /// StreamGuard.
+  explicit Stream(Default, Device device) : device_(device), id_(0) {}
+
+  bool operator==(const Stream& other) const noexcept {
+    return this->device_ == other.device_ && this->id_ == other.id_;
+  }
+  bool operator!=(const Stream& other) const noexcept {
+    return !(*this == other);
+  }
+
+  Device device() const noexcept {
+    return device_;
+  }
+  DeviceType device_type() const noexcept {
+    return device_.type();
+  }
+  DeviceIndex device_index() const noexcept {
+    return device_.index();
+  }
+  StreamId id() const noexcept {
+    return id_;
+  }
+
+  // Enqueues a wait instruction in the stream's work queue.
+  // This instruction is a no-op unless the event is marked
+  // for recording. In that case the stream stops processing
+  // until the event is recorded.
+  template <typename T>
+  void wait(const T& event) const {
+    event.block(*this);
+  }
+
+  // Return whether all asynchronous work previously enqueued on this stream
+  // has completed running on the device.
+  bool query() const;
+
+  // Wait (by blocking the calling thread) until all asynchronous work enqueued
+  // on this stream has completed running on the device.
+  void synchronize() const;
+
+  // The purpose of this function is to more conveniently permit binding
+  // of Stream to and from Python.  Without packing, I have to setup a whole
+  // class with two fields (device and stream id); with packing I can just
+  // store a single uint64_t.
+  //
+  // The particular way we pack streams into a uint64_t is considered an
+  // implementation detail and should not be relied upon.
+  uint64_t hash() const noexcept {
+    // Concat these together into a 64-bit integer
+    uint64_t bits = static_cast<uint64_t>(device_type()) << 56 |
+        static_cast<uint64_t>(device_index()) << 48 |
+        // Remove the sign extension part of the 64-bit address because
+        // the id might be used to hold a pointer.
+        (static_cast<uint64_t>(id()) & ((1ull << 48) - 1));
+    return bits;
+  }
+
+  struct StreamData3 pack3() const {
+    return {id(), device_index(), device_type()};
+  }
+
+  static Stream unpack3(
+      StreamId stream_id,
+      DeviceIndex device_index,
+      DeviceType device_type) {
+    TORCH_CHECK(isValidDeviceType(device_type));
+    return Stream(UNSAFE, Device(device_type, device_index), stream_id);
+  }
+
+  // I decided NOT to provide setters on this class, because really,
+  // why would you change the device of a stream?  Just construct
+  // it correctly from the beginning dude.
+};
+
+C10_API std::ostream& operator<<(std::ostream& stream, const Stream& s);
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::Stream> {
+  size_t operator()(c10::Stream s) const noexcept {
+    return std::hash<uint64_t>{}(s.hash());
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/StreamGuard.h b/lib/python3.11/site-packages/torch/include/c10/core/StreamGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..d3057823a5cd1eeafb2b0e65b511c9a44082e731
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/StreamGuard.h
@@ -0,0 +1,173 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/Stream.h>
+#include <c10/core/impl/InlineStreamGuard.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Optional.h>
+
+namespace c10 {
+
+/**
+ * A StreamGuard is an RAII class that changes the current device
+ * to the device corresponding to some stream, and changes the
+ * default stream on that device to be this stream.
+ *
+ * Use of StreamGuard is HIGHLY discouraged in operator definitions.  In
+ * a single operator, you probably don't know enough about the global
+ * state of the world to profitably decide how to set streams.  Let
+ * the caller handle this appropriately, and just use the current stream
+ * in your operator code.
+ *
+ * This StreamGuard does NOT have an uninitialized state; it is guaranteed
+ * to reset the stream and device on exit.  If you are in a situation
+ * where you *might* want to setup a stream guard, see OptionalStreamGuard.
+ */
+struct StreamGuard {
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit StreamGuard() = delete;
+  ~StreamGuard() = default;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current  stream on that device to the passed stream.
+  explicit StreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Copy is disallowed
+  StreamGuard(const StreamGuard&) = delete;
+  StreamGuard& operator=(const StreamGuard&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  StreamGuard(StreamGuard&& other) = delete;
+  StreamGuard& operator=(StreamGuard&& other) = delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// on , use MultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the stream that was set at the time the guard was constructed.
+  Stream original_stream() const {
+    return guard_.original_stream();
+  }
+
+  /// Returns the most recent stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  Stream current_stream() const {
+    return guard_.current_stream();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Returns the device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+ private:
+  c10::impl::InlineStreamGuard<impl::VirtualGuardImpl> guard_;
+};
+
+/**
+ * An OptionalStreamGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ * See OptionalDeviceGuard for more guidance on how to use this class.
+ */
+struct OptionalStreamGuard {
+  /// Create an uninitialized guard.
+  explicit OptionalStreamGuard() = default;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  explicit OptionalStreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit OptionalStreamGuard(std::optional<Stream> stream_opt)
+      : guard_(stream_opt) {}
+
+  /// Copy is disallowed
+  OptionalStreamGuard(const OptionalStreamGuard&) = delete;
+  OptionalStreamGuard& operator=(const OptionalStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalStreamGuard(OptionalStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalStreamGuard& operator=(OptionalStreamGuard&& other) = delete;
+  ~OptionalStreamGuard() = default;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the guard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the stream that was set at the time the guard was most recently
+  /// initialized, or nullopt if the guard is uninitialized.
+  std::optional<Stream> original_stream() const {
+    return guard_.original_stream();
+  }
+
+  /// Returns the most recent  stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  std::optional<Stream> current_stream() const {
+    return guard_.current_stream();
+  }
+
+  /// Restore the original  device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalStreamGuard<impl::VirtualGuardImpl> guard_{};
+};
+
+/**
+ * A MultiStreamGuard is an RAII class that sets the current streams of a set of
+ * devices all at once, and resets them to their original values on destruction.
+ */
+struct MultiStreamGuard {
+  /// Set the current streams to the passed streams on each of their respective
+  /// devices.
+  explicit MultiStreamGuard(ArrayRef<Stream> streams) : guard_(streams) {}
+
+  /// Copy is disallowed
+  MultiStreamGuard(const MultiStreamGuard&) = delete;
+  MultiStreamGuard& operator=(const MultiStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  MultiStreamGuard(MultiStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  MultiStreamGuard& operator=(MultiStreamGuard&& other) = delete;
+  ~MultiStreamGuard() = default;
+
+ private:
+  c10::impl::InlineMultiStreamGuard<impl::VirtualGuardImpl> guard_;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymBool.h b/lib/python3.11/site-packages/torch/include/c10/core/SymBool.h
new file mode 100644
index 0000000000000000000000000000000000000000..348ac47d8e2e71b70f1b2e723b88494fd28cb9dc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymBool.h
@@ -0,0 +1,110 @@
+#pragma once
+
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstdint>
+#include <optional>
+#include <ostream>
+#include <utility>
+
+namespace c10 {
+
+class C10_API SymBool {
+ public:
+  /*implicit*/ SymBool(bool b) : data_(b) {}
+  SymBool(SymNode ptr) : data_(false), ptr_(std::move(ptr)) {
+    TORCH_CHECK(ptr_->is_bool());
+  }
+  SymBool() : data_(false) {}
+
+  SymNodeImpl* toSymNodeImplUnowned() const {
+    return ptr_.get();
+  }
+
+  SymNodeImpl* release() && {
+    return std::move(ptr_).release();
+  }
+
+  // Only valid if is_heap_allocated()
+  SymNode toSymNodeImpl() const;
+
+  // Guaranteed to return a SymNode, wrapping using base if necessary
+  SymNode wrap_node(const SymNode& base) const;
+
+  bool expect_bool() const {
+    std::optional<bool> c = maybe_as_bool();
+    TORCH_CHECK(c.has_value());
+    return *c;
+  }
+
+  SymBool sym_and(const SymBool&) const;
+  SymBool sym_or(const SymBool&) const;
+  SymBool sym_not() const;
+
+  SymBool operator&(const SymBool& other) const {
+    return sym_and(other);
+  }
+  SymBool operator|(const SymBool& other) const {
+    return sym_or(other);
+  }
+  SymBool operator~() const {
+    return sym_not();
+  }
+
+  // Insert a guard for the bool to be its concrete value, and then return
+  // that value.  Note that C++ comparison operations default to returning
+  // bool, so it's not so common to have to call this
+  bool guard_bool(const char* file, int64_t line) const;
+  bool expect_true(const char* file, int64_t line) const;
+  bool guard_size_oblivious(const char* file, int64_t line) const;
+
+  bool has_hint() const;
+
+  bool as_bool_unchecked() const {
+    return data_;
+  }
+
+  std::optional<bool> maybe_as_bool() const {
+    if (!is_heap_allocated()) {
+      return data_;
+    }
+    return toSymNodeImplUnowned()->constant_bool();
+  }
+
+  bool is_heap_allocated() const {
+    return ptr_;
+  }
+
+ private:
+  // TODO: optimize to union
+  bool data_;
+  SymNode ptr_;
+};
+
+C10_API std::ostream& operator<<(std::ostream& os, const SymBool& s);
+
+#define TORCH_SYM_CHECK(cond, ...) \
+  TORCH_CHECK((cond).expect_true(__FILE__, __LINE__), __VA_ARGS__)
+#define TORCH_SYM_INTERNAL_ASSERT(cond, ...) \
+  TORCH_INTERNAL_ASSERT((cond).expect_true(__FILE__, __LINE__), __VA_ARGS__)
+
+inline bool guard_size_oblivious(
+    bool b,
+    const char* file [[maybe_unused]],
+    int64_t line [[maybe_unused]]) {
+  return b;
+}
+
+inline bool guard_size_oblivious(
+    const c10::SymBool& b,
+    const char* file,
+    int64_t line) {
+  return b.guard_size_oblivious(file, line);
+}
+
+#define TORCH_GUARD_SIZE_OBLIVIOUS(cond) \
+  c10::guard_size_oblivious((cond), __FILE__, __LINE__)
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymFloat.h b/lib/python3.11/site-packages/torch/include/c10/core/SymFloat.h
new file mode 100644
index 0000000000000000000000000000000000000000..d30b646a653fd08ffc24acb89a4fa59a6605959f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymFloat.h
@@ -0,0 +1,118 @@
+#pragma once
+
+#include <c10/core/SymBool.h>
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+
+#include <cstdint>
+#include <limits>
+#include <ostream>
+#include <utility>
+
+namespace c10 {
+
+// NB: this is actually double precision; we're using the Python naming here
+class C10_API SymFloat {
+ public:
+  /*implicit*/ SymFloat(double d) : data_(d) {}
+  SymFloat(SymNode ptr)
+      : data_(std::numeric_limits<double>::quiet_NaN()), ptr_(std::move(ptr)) {
+    TORCH_CHECK(ptr_->is_float());
+  }
+  SymFloat() : data_(0.0) {}
+
+  SymNodeImpl* toSymNodeImplUnowned() const {
+    return ptr_.get();
+  }
+
+  SymNodeImpl* release() && {
+    return std::move(ptr_).release();
+  }
+
+  // Only valid if is_symbolic()
+  SymNode toSymNodeImpl() const;
+
+  // Guaranteed to return a SymNode, wrapping using base if necessary
+  SymNode wrap_node(const SymNode& base) const;
+
+  double expect_float() const {
+    TORCH_CHECK(!is_symbolic());
+    return data_;
+  }
+
+  SymFloat operator+(const SymFloat&) const;
+  SymFloat operator-(const SymFloat&) const;
+  SymFloat operator*(const SymFloat&) const;
+  SymFloat operator/(const SymFloat&) const;
+
+  SymBool sym_eq(const SymFloat&) const;
+  SymBool sym_ne(const SymFloat&) const;
+  SymBool sym_lt(const SymFloat&) const;
+  SymBool sym_le(const SymFloat&) const;
+  SymBool sym_gt(const SymFloat&) const;
+  SymBool sym_ge(const SymFloat&) const;
+
+  bool operator==(const SymFloat& o) const {
+    return sym_eq(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator!=(const SymFloat& o) const {
+    return sym_ne(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<(const SymFloat& o) const {
+    return sym_lt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<=(const SymFloat& o) const {
+    return sym_le(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>(const SymFloat& o) const {
+    return sym_gt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>=(const SymFloat& o) const {
+    return sym_ge(o).guard_bool(__FILE__, __LINE__);
+  }
+
+  SymFloat min(const SymFloat& sci) const;
+  SymFloat max(const SymFloat& sci) const;
+
+  // Need guidance on where to put this code
+  SymFloat sqrt() const;
+
+  // Insert a guard for the float to be its concrete value, and then return
+  // that value.  This operation always works, even if the float is symbolic,
+  // so long as we know what the underlying value is. Don't blindly put this
+  // everywhere; you can cause overspecialization of PyTorch programs with
+  // this method.
+  //
+  // It should be called as guard_float(__FILE__, __LINE__).  The file and line
+  // number can be used to diagnose overspecialization.
+  double guard_float(const char* file, int64_t line) const;
+
+  bool has_hint() const;
+
+  // N.B. It's important to keep this definition in the header
+  // as we expect if checks to be folded for mobile builds
+  // where `is_symbolic` is always false
+  C10_ALWAYS_INLINE bool is_symbolic() const {
+    return ptr_;
+  }
+
+  // UNSAFELY coerce this SymFloat into a double.  You MUST have
+  // established that this is a non-symbolic by some other means,
+  // typically by having tested is_symbolic().  You will get garbage
+  // from this function if is_symbolic()
+  double as_float_unchecked() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!is_symbolic());
+    return data_;
+  }
+
+ private:
+  // TODO: optimize to union
+  double data_;
+  SymNode ptr_;
+};
+
+C10_API std::ostream& operator<<(std::ostream& os, const SymFloat& s);
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymInt.h b/lib/python3.11/site-packages/torch/include/c10/core/SymInt.h
new file mode 100644
index 0000000000000000000000000000000000000000..f965d909c9e1945115964389026d11a8b4a80be9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymInt.h
@@ -0,0 +1,424 @@
+#pragma once
+
+#include <c10/core/SymBool.h>
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+
+#include <cstdint>
+#include <iterator>
+#include <numeric>
+#include <optional>
+#include <ostream>
+#include <type_traits>
+
+namespace c10 {
+
+class SymFloat;
+
+// SymInt represents either a regular int64_t, or a symbolic integer
+// (represented in a type erased way as SymNode).  The intention is for SymInt
+// to represent symbolic sizes that arise when doing shape computation in
+// operator kernels. This allows for tracing through programs without baking in
+// concrete sizes into kernel calls.
+//
+// SymInt has an API equivalent to int64_t.  In particular, it is a value type.
+// Internally, SymInt is represented in a clever packed way, so that it only
+// occupies one word of space; but morally, it is a union between an int64_t
+// and an intrusive pointer to SymNodeImpl.
+//
+// Invariant: the referenced SymNodeImpl is guaranteed to be a SymNode where
+// is_int() returns true
+
+class C10_API SymInt {
+ public:
+  enum Unchecked {
+    UNCHECKED,
+  };
+
+  /*implicit*/ SymInt(int64_t d) : data_(d) {
+    if (is_heap_allocated()) {
+      // Large negative number, heap allocate it
+      promote_to_negative();
+    }
+  }
+  SymInt() : data_(0) {}
+  SymInt(SymNode n);
+
+  // unchecked c-tor accepting raw `data_`
+  // One appropriate use for this is when you are constructing a symint
+  // in a situation where you know it is non-negative (or, if it is negative,
+  // the negative value is -1; i.e., not user controlled)
+  SymInt(Unchecked, int64_t d) : data_(d) {}
+
+  // TODO: these implementations are not optimal because they allocate a
+  // temporary and then use the move constructor/assignment
+  SymInt(const SymInt& s) : data_(0) {
+    if (s.is_heap_allocated()) {
+      *this = SymInt(s.toSymNode());
+    } else {
+      data_ = s.data_;
+    }
+  }
+  SymInt(SymInt&& s) noexcept : data_(s.data_) {
+    s.data_ = 0;
+  }
+
+  SymInt& operator=(const SymInt& s) {
+    if (this != &s) {
+      if (s.is_heap_allocated()) {
+        *this = SymInt(s.toSymNode());
+      } else {
+        data_ = s.data_;
+      }
+    }
+    return *this;
+  }
+  SymInt& operator=(SymInt&& s) noexcept {
+    if (this != &s) {
+      release_(); // release the current SymNode if any
+      data_ = s.data_;
+      if (s.is_heap_allocated())
+        s.data_ = 0;
+    };
+    return *this;
+  }
+
+  SymNodeImpl* toSymNodeImplUnowned() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(is_heap_allocated());
+    uint64_t unextended_bits = static_cast<uint64_t>(data_) & ~MASK;
+    uint64_t sign_bit_mask = 1ULL << (62 - 1);
+    // https://stackoverflow.com/questions/42534749/signed-extension-from-24-bit-to-32-bit-in-c
+    uint64_t extended_bits = (unextended_bits ^ sign_bit_mask) - sign_bit_mask;
+    return static_cast<SymNodeImpl*>(
+        // NOLINTNEXTLINE(performance-no-int-to-ptr, bugprone*)
+        reinterpret_cast<void*>(static_cast<uintptr_t>(extended_bits)));
+  }
+
+  void release_() {
+    if (is_heap_allocated()) {
+      SymNode::reclaim(toSymNodeImplUnowned()); // steal
+    }
+  }
+
+  SymNodeImpl* release() && {
+#ifndef C10_MOBILE
+    TORCH_INTERNAL_ASSERT(is_heap_allocated());
+    auto* r = toSymNodeImplUnowned();
+    data_ = 0; // transfer ownership
+    return r;
+#else
+    TORCH_INTERNAL_ASSERT(false);
+#endif
+  }
+
+  // Only valid if is_heap_allocated()
+  SymNode toSymNode() const;
+
+  // Guaranteed to return a SymNode, wrapping using base if necessary
+  SymNode wrap_node(const SymNode& base) const;
+
+  ~SymInt() {
+    release_();
+  }
+
+  // Require the int to be non-symbolic, and if it is symbolic raise an
+  // error.  This is safe to use for C++ code that doesn't work for symbolic
+  // shapes, and you don't have time to fix it immediately, as if we
+  // try to trigger the path in C++ you'll appropriately get an error
+  int64_t expect_int() const {
+    if (auto r = maybe_as_int()) {
+      return *r;
+    }
+    TORCH_CHECK_ALWAYS_SHOW_CPP_STACKTRACE(
+        false, "when unpacking SymInt, expected int but got ", *this);
+  }
+
+  // Test if we have a hint for this int (e.g., guard_int would work).
+  // Most of the time this is true; it is only false when you have
+  // an unbacked SymInt.
+  bool has_hint() const;
+
+  // Insert a guard for the int to be its concrete value, and then return
+  // that value.  This operation always works, even if the int is symbolic,
+  // so long as we know what the underlying value is (e.g., this won't work
+  // if you call it on the size of nonzero output).  Don't blindly put this
+  // everywhere; you can cause overspecialization of PyTorch programs with
+  // this method.
+  //
+  // It should be called as guard_int(__FILE__, __LINE__).  The file and line
+  // number can be used to diagnose overspecialization.
+  int64_t guard_int(const char* file, int64_t line) const;
+
+  // Insert a guard that this SymInt must be size-like, returning true if
+  // the integer actually is >= 0.  Unlike manually performing a >= 0 test,
+  // if the SymInt in question is an unbacked SymInt (or, potentially in the
+  // future, if it contains unbacked SymInts), we will also treat the
+  // unbacked SymInt as statically testing >= 2 (which will prevent us from
+  // choking on, e.g., contiguity checks.)
+  bool expect_size(const char* file, int64_t line) const;
+
+  // Distinguish actual symbolic values from constants stored on the heap
+  bool is_symbolic() const {
+    return is_heap_allocated() &&
+        !toSymNodeImplUnowned()->constant_int().has_value();
+  }
+
+  // N.B. It's important to keep this definition in the header
+  // as we expect if checks to be folded for mobile builds
+  // where `is_heap_allocated` is always false and optimize dead code paths
+  C10_ALWAYS_INLINE bool is_heap_allocated() const {
+#ifdef C10_MOBILE
+    return false;
+#else
+    return !check_range(data_);
+#endif
+  }
+
+  SymInt operator+(const SymInt& sci) const;
+  SymInt operator-(const SymInt& sci) const;
+  SymInt operator*(const SymInt& sci) const;
+  SymInt operator/(const SymInt& sci) const;
+  SymInt operator%(const SymInt& sci) const;
+  void operator*=(const SymInt& sci);
+  void operator+=(const SymInt& sci);
+  void operator/=(const SymInt& sci);
+
+  SymInt clone() const;
+
+  SymBool sym_eq(const SymInt&) const;
+  SymBool sym_ne(const SymInt&) const;
+  SymBool sym_lt(const SymInt&) const;
+  SymBool sym_le(const SymInt&) const;
+  SymBool sym_gt(const SymInt&) const;
+  SymBool sym_ge(const SymInt&) const;
+
+  bool operator==(const SymInt& o) const {
+    return sym_eq(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator!=(const SymInt& o) const {
+    return sym_ne(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<(const SymInt& o) const {
+    return sym_lt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<=(const SymInt& o) const {
+    return sym_le(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>(const SymInt& o) const {
+    return sym_gt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>=(const SymInt& o) const {
+    return sym_ge(o).guard_bool(__FILE__, __LINE__);
+  }
+
+  SymInt min(const SymInt& sci) const;
+  SymInt max(const SymInt& sci) const;
+
+  // If both are symbolic, this checks if
+  // they share the same node.
+  // If both are not symbolic this just checks normal equality.
+  bool is_same(const SymInt& other) const;
+
+  operator SymFloat() const;
+
+  // Don't use this.  Prefer maybe_as_int instead
+  int64_t as_int_unchecked() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!is_heap_allocated());
+    return data_;
+  }
+
+  std::optional<int64_t> maybe_as_int() const {
+    if (!is_heap_allocated()) {
+      return data_;
+    }
+    auto* node = toSymNodeImplUnowned();
+    if (auto c = node->constant_int()) {
+      return c;
+    }
+    return node->maybe_as_int();
+  }
+
+  // Return whether the integer is directly coercible to a SymInt
+  // without requiring heap allocation.  You don't need to use this
+  // to check if you can pass an integer to SymInt; this is guaranteed
+  // to work (it just might heap allocate!)
+  static bool check_range(int64_t i) {
+    return i > MAX_UNREPRESENTABLE_INT;
+  }
+
+  // Return the min representable integer as a SymInt without
+  // heap allocation.  For quantities that count bytes (or larger),
+  // this is still much larger than you need, so you may consider
+  // using this as a more efficient version of MIN_INT
+  static constexpr int64_t min_representable_int() {
+    return MAX_UNREPRESENTABLE_INT + 1;
+  }
+
+ private:
+  void promote_to_negative();
+
+  // Constraints on the internal representation:
+  //
+  // - Should represent positive and small negative ints
+  // - No conversion necessary for operations on ints
+  // - Must represent valid 64-bit pointers
+  // - Is symbolic test should be FAST (two arithmetic instructions is too
+  // much).
+  //   This code being a hotpath is based on Strobelight profiles of
+  //   is_heap_allocated().  FB only: https://fburl.com/strobelight/5l50ncxd
+  //   (you will need to change the time window).
+  //
+  // So, the scheme is to reserve large negative numbers (assuming
+  // two's complement):
+  //
+  // - 0b0.... means we are a positive int
+  // - 0b11... means we are a small negative int
+  // - 0b10... means we are are a pointer. This means that
+  //           [-2^63, -2^62-1] are not representable as ints.
+  //           We don't actually need all of this space as on x86_64
+  //           as the top 16bits aren't used for anything
+  static constexpr uint64_t MASK = 1ULL << 63 | 1ULL << 62 | 1ULL << 61;
+  static constexpr uint64_t IS_SYM = 1ULL << 63 | 1ULL << 61;
+  // We must manually translate the bit pattern test into a greater
+  // than test because compiler doesn't figure it out:
+  // https://godbolt.org/z/356aferaW
+  static constexpr int64_t MAX_UNREPRESENTABLE_INT =
+      -1LL & static_cast<int64_t>(~(1ULL << 62));
+  int64_t data_;
+};
+
+/// Sum of a list of SymInt; accumulates into the c10::SymInt expression
+template <
+    typename C,
+    typename std::enable_if_t<
+        std::is_same_v<typename C::value_type, c10::SymInt>,
+        int> = 0>
+inline c10::SymInt multiply_integers(const C& container) {
+  return std::accumulate(
+      container.begin(),
+      container.end(),
+      c10::SymInt(1),
+      [](const c10::SymInt& a, const c10::SymInt& b) { return a * b; });
+}
+
+template <
+    typename Iter,
+    typename = std::enable_if_t<std::is_same_v<
+        typename std::iterator_traits<Iter>::value_type,
+        c10::SymInt>>>
+inline c10::SymInt multiply_integers(Iter begin, Iter end) {
+  return std::accumulate(
+      begin,
+      end,
+      c10::SymInt(1),
+      [](const c10::SymInt& a, const c10::SymInt& b) { return a * b; });
+}
+
+#define DECLARE_SYMINT_OP_INTONLY(scalar_t, RetTy)      \
+  C10_API RetTy operator%(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator%(scalar_t a, const SymInt& b);
+
+#define DECLARE_SYMINT_OP(scalar_t, RetTy)              \
+  C10_API RetTy operator+(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator-(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator*(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator/(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator+(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator-(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator*(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator/(scalar_t a, const SymInt& b); \
+  C10_API bool operator==(const SymInt& a, scalar_t b); \
+  C10_API bool operator!=(const SymInt& a, scalar_t b); \
+  C10_API bool operator<(const SymInt& a, scalar_t b);  \
+  C10_API bool operator<=(const SymInt& a, scalar_t b); \
+  C10_API bool operator>(const SymInt& a, scalar_t b);  \
+  C10_API bool operator>=(const SymInt& a, scalar_t b); \
+  C10_API bool operator==(scalar_t a, const SymInt& b); \
+  C10_API bool operator!=(scalar_t a, const SymInt& b); \
+  C10_API bool operator<(scalar_t a, const SymInt& b);  \
+  C10_API bool operator<=(scalar_t a, const SymInt& b); \
+  C10_API bool operator>(scalar_t a, const SymInt& b);  \
+  C10_API bool operator>=(scalar_t a, const SymInt& b);
+
+DECLARE_SYMINT_OP_INTONLY(int64_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(int32_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(uint64_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(uint32_t, SymInt)
+DECLARE_SYMINT_OP(int64_t, SymInt)
+DECLARE_SYMINT_OP(int32_t, SymInt) // make sure constants work
+DECLARE_SYMINT_OP(uint64_t, SymInt)
+DECLARE_SYMINT_OP(uint32_t, SymInt)
+DECLARE_SYMINT_OP(double, SymFloat)
+DECLARE_SYMINT_OP(float, SymFloat) // just for completeness
+
+// On OSX size_t is different than uint64_t so we have to
+// define it separately
+#if defined(__APPLE__)
+DECLARE_SYMINT_OP_INTONLY(size_t, SymInt)
+DECLARE_SYMINT_OP(size_t, SymInt)
+#endif
+
+#undef DECLARE_SYMINT_OP
+
+C10_API std::ostream& operator<<(std::ostream& os, const SymInt& s);
+C10_API SymInt operator-(const SymInt& s);
+
+inline bool sym_eq(int64_t a, int64_t b) {
+  return a == b;
+}
+
+inline SymBool sym_eq(const SymInt& a, const SymInt& b) {
+  return a.sym_eq(b);
+}
+
+inline bool sym_ne(int64_t a, int64_t b) {
+  return a != b;
+}
+
+inline SymBool sym_ne(const SymInt& a, const SymInt& b) {
+  return a.sym_ne(b);
+}
+
+inline bool sym_lt(int64_t a, int64_t b) {
+  return a < b;
+}
+
+inline SymBool sym_lt(const SymInt& a, const SymInt& b) {
+  return a.sym_lt(b);
+}
+
+inline bool sym_le(int64_t a, int64_t b) {
+  return a <= b;
+}
+
+inline SymBool sym_le(const SymInt& a, const SymInt& b) {
+  return a.sym_le(b);
+}
+
+inline bool sym_gt(int64_t a, int64_t b) {
+  return a > b;
+}
+
+inline SymBool sym_gt(const SymInt& a, const SymInt& b) {
+  return a.sym_gt(b);
+}
+
+inline bool sym_ge(int64_t a, int64_t b) {
+  return a >= b;
+}
+
+inline SymBool sym_ge(const SymInt& a, const SymInt& b) {
+  return a.sym_ge(b);
+}
+
+inline bool definitely_true(
+    const c10::SymBool& b,
+    const char* file,
+    int64_t line) {
+  return b.has_hint() && b.guard_bool(file, line);
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymIntArrayRef.h b/lib/python3.11/site-packages/torch/include/c10/core/SymIntArrayRef.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf050f461f4a85a295140d5d542908ca707469ef
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymIntArrayRef.h
@@ -0,0 +1,89 @@
+#pragma once
+
+#include <c10/core/SymInt.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/DimVector.h>
+#include <c10/util/Exception.h>
+#include <c10/util/irange.h>
+#include <cstdint>
+#include <optional>
+
+namespace c10 {
+using SymIntArrayRef = ArrayRef<SymInt>;
+
+inline at::IntArrayRef asIntArrayRefUnchecked(c10::SymIntArrayRef ar) {
+  return IntArrayRef(reinterpret_cast<const int64_t*>(ar.data()), ar.size());
+}
+
+// TODO: a SymIntArrayRef containing a heap allocated large negative integer
+// can actually technically be converted to an IntArrayRef... but not with
+// the non-owning API we have here.  We can't reinterpet cast; we have to
+// allocate another buffer and write the integers into it.  If you need it,
+// we can do it.  But I don't think you need it.
+
+inline std::optional<at::IntArrayRef> asIntArrayRefSlowOpt(
+    c10::SymIntArrayRef ar) {
+  for (const c10::SymInt& sci : ar) {
+    if (sci.is_heap_allocated()) {
+      return std::nullopt;
+    }
+  }
+
+  return {asIntArrayRefUnchecked(ar)};
+}
+
+inline at::IntArrayRef asIntArrayRefSlow(
+    c10::SymIntArrayRef ar,
+    const char* file,
+    int64_t line) {
+  for (const c10::SymInt& sci : ar) {
+    TORCH_CHECK(
+        !sci.is_heap_allocated(),
+        file,
+        ":",
+        line,
+        ": SymIntArrayRef expected to contain only concrete integers");
+  }
+  return asIntArrayRefUnchecked(ar);
+}
+
+// Even slower than asIntArrayRefSlow, as it forces an allocation for a
+// destination int, BUT it is able to force specialization (it never errors)
+inline c10::DimVector asIntArrayRefSlowAlloc(
+    c10::SymIntArrayRef ar,
+    const char* file,
+    int64_t line) {
+  c10::DimVector res(ar.size(), 0);
+  for (const auto i : c10::irange(ar.size())) {
+    res[i] = ar[i].guard_int(file, line);
+  }
+  return res;
+}
+
+#define C10_AS_INTARRAYREF_SLOW(a) c10::asIntArrayRefSlow(a, __FILE__, __LINE__)
+#define C10_AS_INTARRAYREF_SLOW_ALLOC(a) \
+  c10::asIntArrayRefSlowAlloc(a, __FILE__, __LINE__)
+
+// Prefer using a more semantic constructor, like
+// fromIntArrayRefKnownNonNegative
+inline SymIntArrayRef fromIntArrayRefUnchecked(IntArrayRef array_ref) {
+  return SymIntArrayRef(
+      reinterpret_cast<const SymInt*>(array_ref.data()), array_ref.size());
+}
+
+inline SymIntArrayRef fromIntArrayRefKnownNonNegative(IntArrayRef array_ref) {
+  return fromIntArrayRefUnchecked(array_ref);
+}
+
+inline SymIntArrayRef fromIntArrayRefSlow(IntArrayRef array_ref) {
+  for (long i : array_ref) {
+    TORCH_CHECK(
+        SymInt::check_range(i),
+        "IntArrayRef contains an int that cannot be represented as a SymInt: ",
+        i);
+  }
+  return SymIntArrayRef(
+      reinterpret_cast<const SymInt*>(array_ref.data()), array_ref.size());
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymNodeImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/SymNodeImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..36652e1800ac873ee07d9d47cf85ac1e65d4f1c4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymNodeImpl.h
@@ -0,0 +1,242 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstdint>
+#include <optional>
+#include <ostream>
+#include <string>
+
+C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-parameter")
+
+namespace c10 {
+
+class SymNodeImpl;
+using SymNode = c10::intrusive_ptr<SymNodeImpl>;
+
+// When you add a method, you also need to edit
+// torch/csrc/jit/python/init.cpp
+// torch/csrc/utils/python_symnode.h
+// c10/core/ConstantSymNodeImpl.h
+class C10_API SymNodeImpl : public c10::intrusive_ptr_target {
+ public:
+  ~SymNodeImpl() override = default;
+
+  template <typename T>
+  c10::intrusive_ptr<T> dyn_cast() const {
+    return c10::intrusive_ptr<T>::reclaim_copy(dynamic_cast<T*>(this));
+  }
+
+  // these could be pure virtual when we implement LTC versions
+  virtual bool is_int() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_bool() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_float() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_nested_int() const {
+    return false;
+  }
+  virtual SymNode add(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sub(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode mul(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  // NB: legacy, prefer float_truediv or int_truediv
+  virtual SymNode truediv(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode float_truediv(const SymNode& other) {
+    return truediv(other);
+  }
+  virtual SymNode int_truediv(const SymNode& other) {
+    return truediv(other);
+  }
+  // NB: legacy, prefer float_pow or pow_by_natural
+  virtual SymNode pow(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode float_pow(const SymNode& other) {
+    return pow(other);
+  }
+  virtual SymNode pow_by_natural(const SymNode& other) {
+    return pow(other);
+  }
+  // NB: legacy, prefer int_floordiv
+  virtual SymNode floordiv(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode int_floordiv(const SymNode& other) {
+    return floordiv(other);
+  }
+  virtual SymNode mod(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode eq(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ne(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode gt(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode lt(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode le(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ge(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ceil() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode floor() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode neg() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_min(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_max(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_or(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_and(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_not() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_ite(const SymNode& then_val, const SymNode& else_val) {
+    TORCH_CHECK(false, "NYI");
+  }
+  // NB: self is ignored here, only the arguments are used
+  virtual SymNode is_contiguous(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode is_channels_last_contiguous_2d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode is_channels_last_contiguous_3d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode is_channels_last_strides_2d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode is_channels_last_strides_3d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode is_non_overlapping_and_dense(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode clone() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sym_float() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode wrap_int(int64_t num) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode wrap_float(double num) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode wrap_bool(bool num) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual int64_t guard_int(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool guard_bool(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual double guard_float(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool guard_size_oblivious(const char* file, int64_t line) {
+    // No improvement for unbacked SymBools by default, replace this
+    // with a better implementation!
+    return guard_bool(file, line);
+  }
+  virtual bool expect_true(const char* file, int64_t line) {
+    // No improvement for unbacked SymBools by default, replace this
+    // with a better implementation!
+    return guard_bool(file, line);
+  }
+  virtual bool expect_size(const char* file, int64_t line) {
+    // No improvement for unbacked SymInts by default, replace this
+    // with a better implementation!
+    return ge(wrap_int(0))->guard_bool(file, line);
+  }
+  virtual int64_t int_() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool bool_() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool has_hint() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual std::string str() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual std::string _graph_repr() {
+    return str();
+  }
+  virtual std::optional<int64_t> nested_int() {
+    return std::nullopt;
+  }
+  virtual std::optional<int64_t> nested_int_coeff() {
+    return std::nullopt;
+  }
+  virtual std::optional<int64_t> constant_int() {
+    return std::nullopt;
+  }
+  virtual std::optional<bool> constant_bool() {
+    return std::nullopt;
+  }
+  virtual std::optional<int64_t> maybe_as_int() {
+    return std::nullopt;
+  }
+  virtual bool is_constant() {
+    return false;
+  }
+  virtual bool is_symbolic() {
+    return true;
+  }
+  std::ostream& operator<<(std::ostream& os) {
+    os << str();
+    return os;
+  }
+};
+
+} // namespace c10
+C10_DIAGNOSTIC_POP()
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/SymbolicShapeMeta.h b/lib/python3.11/site-packages/torch/include/c10/core/SymbolicShapeMeta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce0769a8074f7d75d2104cbbf37027119cfb2fed
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/SymbolicShapeMeta.h
@@ -0,0 +1,218 @@
+#pragma once
+#include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/DimVector.h>
+
+#include <atomic>
+#include <cstdint>
+#include <mutex>
+#include <utility>
+
+namespace c10 {
+
+class C10_API SymbolicShapeMeta {
+ public:
+  // Basic metadata from which other quantities are derived
+  SymDimVector sizes_ = {0};
+  SymDimVector strides_ = {1};
+  SymInt storage_offset_ = 0;
+
+  bool strides_valid_ = true; // e.g. for sparse where there are no strides
+
+  SymbolicShapeMeta() = default;
+  ~SymbolicShapeMeta() = default;
+  SymbolicShapeMeta(const SymbolicShapeMeta& other);
+  SymbolicShapeMeta(SymbolicShapeMeta&& other) = delete;
+  SymbolicShapeMeta& operator=(const SymbolicShapeMeta& other) = delete;
+  SymbolicShapeMeta& operator=(SymbolicShapeMeta&& other) = delete;
+
+  void refresh_numel() {
+    // Non-const, don't need to hold mutables_ lock
+    available_.fetch_and(~numel_avail);
+    numel_ = 1;
+  }
+
+  void refresh_contiguous() {
+    // Non-const, don't need to hold mutables_ lock
+    available_.fetch_and(numel_avail);
+    is_contiguous_ = false;
+    is_channels_last_contiguous_ = false;
+    is_channels_last_3d_contiguous_ = false;
+    is_channels_last_ = false;
+    is_channels_last_3d_ = false;
+    is_non_overlapping_and_dense_ = false;
+  }
+
+  int64_t dim() const {
+    return static_cast<int64_t>(sizes_.size());
+  }
+
+  // Accessors for derived quantities, computed lazily on first access
+
+  bool has_numel() const {
+    return available_.load() & numel_avail;
+  }
+  bool has_is_contiguous() const {
+    return available_.load() & is_contiguous_avail;
+  }
+  bool has_is_channels_last_contiguous() const {
+    return available_.load() & is_channels_last_contiguous_avail;
+  }
+  bool has_is_channels_last_3d_contiguous() const {
+    return available_.load() & is_channels_last_3d_contiguous_avail;
+  }
+  bool has_is_channels_last() const {
+    return available_.load() & is_channels_last_avail;
+  }
+  bool has_is_channels_last_3d() const {
+    return available_.load() & is_channels_last_3d_avail;
+  }
+  bool has_is_non_overlapping_and_dense() const {
+    return available_.load() & is_non_overlapping_and_dense_avail;
+  }
+
+  // Accessors to cached derived properties
+  // DO NOT call with mutables_ lock held
+  const SymInt& numel() const {
+    if (C10_UNLIKELY(!has_numel())) {
+      init_numel();
+    }
+    return numel_;
+  }
+
+  const SymBool& is_contiguous() const {
+    if (C10_UNLIKELY(!has_is_contiguous())) {
+      init_is_contiguous();
+    }
+    return is_contiguous_;
+  }
+
+  const SymBool& is_channels_last_contiguous() const {
+    if (C10_UNLIKELY(!has_is_channels_last_contiguous())) {
+      init_is_channels_last_contiguous();
+    }
+    return is_channels_last_contiguous_;
+  }
+
+  const SymBool& is_channels_last_3d_contiguous() const {
+    if (C10_UNLIKELY(!has_is_channels_last_3d_contiguous())) {
+      init_is_channels_last_3d_contiguous();
+    }
+    return is_channels_last_3d_contiguous_;
+  }
+
+  const SymBool& is_channels_last() const {
+    if (C10_UNLIKELY(!has_is_channels_last())) {
+      init_is_channels_last();
+    }
+    return is_channels_last_;
+  }
+
+  const SymBool& is_channels_last_3d() const {
+    if (C10_UNLIKELY(!has_is_channels_last_3d())) {
+      init_is_channels_last_3d();
+    }
+    return is_channels_last_3d_;
+  }
+
+  const SymBool& is_non_overlapping_and_dense() const {
+    if (C10_UNLIKELY(!has_is_non_overlapping_and_dense())) {
+      init_is_non_overlapping_and_dense();
+    }
+    return is_non_overlapping_and_dense_;
+  }
+
+  // Assumptions so we can short-circuit computation
+  // NOTE: Don't need to lock mutables_ since these aren't const
+  void assume_contiguous(SymBool val = true) {
+    is_contiguous_ = std::move(val);
+    available_.fetch_or(is_contiguous_avail);
+  }
+  void assume_channels_last_contiguous(SymBool val = true) {
+    is_contiguous_ = std::move(val);
+    available_.fetch_or(is_channels_last_contiguous_avail);
+  }
+  void assume_channels_last_3d_contiguous(SymBool val = true) {
+    is_channels_last_3d_contiguous_ = std::move(val);
+    available_.fetch_or(is_channels_last_3d_contiguous_avail);
+  }
+  void assume_channels_last(SymBool val = true) {
+    is_channels_last_ = std::move(val);
+    available_.fetch_or(is_channels_last_avail);
+  }
+  void assume_channels_last_3d(SymBool val = true) {
+    is_channels_last_3d_ = std::move(val);
+    available_.fetch_or(is_channels_last_3d_avail);
+  }
+  void assume_non_overlapping_and_dense(SymBool val = true) {
+    is_non_overlapping_and_dense_ = std::move(val);
+    available_.fetch_or(is_non_overlapping_and_dense_avail);
+  }
+
+ private:
+  SymBool compute_contiguous() const;
+  SymBool compute_channels_last_contiguous_2d() const;
+  SymBool compute_channels_last_contiguous_3d() const;
+  SymBool compute_strides_like_channels_last_2d() const;
+  SymBool compute_strides_like_channels_last_3d() const;
+  SymBool compute_non_overlapping_and_dense() const;
+
+  // These are little wrappers over the real compute_ functions that
+  // can make use of other contiguity fields to short circuit.
+  // They need to be implemented separately for SymBool, as SymBool does
+  // not short circuit.
+  // TODO: should the SymBool cases avoid the short circuit?  Need to reason
+  // if its correct, and reason if the simpler expressions are better for
+  // analysis (maybe not!)
+
+  SymBool compute_channels_last_contiguous_3d_dim5() const;
+  SymBool compute_channels_last_2d_dim5() const;
+  SymBool compute_channels_last_3d_dim5() const;
+  SymBool compute_is_non_overlapping_and_dense_dim4() const;
+  SymBool compute_is_non_overlapping_and_dense_dim5() const;
+  SymBool compute_is_non_overlapping_and_dense_anydim() const;
+
+  void init_numel() const;
+  void init_is_contiguous() const;
+  void init_is_channels_last_contiguous() const;
+  void init_is_channels_last_3d_contiguous() const;
+  void init_is_channels_last() const;
+  void init_is_channels_last_3d() const;
+  void init_is_non_overlapping_and_dense() const;
+
+  // NOTE: These only set if !has_foo()
+  void set_numel(SymInt val) const;
+  void set_is_contiguous(SymBool val) const;
+  void set_is_channels_last_contiguous(SymBool val) const;
+  void set_is_channels_last_3d_contiguous(SymBool val) const;
+  void set_is_channels_last(SymBool val) const;
+  void set_is_channels_last_3d(SymBool val) const;
+  void set_is_non_overlapping_and_dense(SymBool val) const;
+
+  // Lazily initialized variables, with the corresponding available_ flag
+  // indicating whether the value has been initialized
+  mutable std::atomic<int> available_{0};
+  enum avail {
+    numel_avail = 1 << 0,
+    is_contiguous_avail = 1 << 1,
+    is_channels_last_contiguous_avail = 1 << 2,
+    is_channels_last_3d_contiguous_avail = 1 << 3,
+    is_channels_last_avail = 1 << 4,
+    is_channels_last_3d_avail = 1 << 5,
+    is_non_overlapping_and_dense_avail = 1 << 6,
+  };
+
+  // Mutex to prevent races when initializing the variable from const accessors
+  mutable std::mutex mutables_;
+  mutable SymInt numel_ = 1;
+  mutable SymBool is_contiguous_{true};
+  mutable SymBool is_channels_last_contiguous_{false};
+  mutable SymBool is_channels_last_3d_contiguous_{false};
+  mutable SymBool is_channels_last_{false};
+  mutable SymBool is_channels_last_3d_{false};
+  mutable SymBool is_non_overlapping_and_dense_{true};
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/TensorImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/TensorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae600a9bddb536334f5105bdaf5968f8967163d6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/TensorImpl.h
@@ -0,0 +1,3266 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/core/InferenceMode.h>
+#include <c10/core/Layout.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/ScalarTypeToTypeMeta.h>
+#include <c10/core/Storage.h>
+#include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
+#include <c10/core/SymIntArrayRef.h>
+#include <c10/core/SymbolicShapeMeta.h>
+#include <c10/core/WrapDimMinimal.h>
+#include <c10/core/impl/PyObjectSlot.h>
+#include <c10/core/impl/SizesAndStrides.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/DimVector.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Flags.h>
+#include <c10/util/accumulate.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/util/irange.h>
+#include <c10/util/safe_numerics.h>
+#include <c10/util/typeid.h>
+#include <optional>
+
+#include <algorithm>
+#include <atomic>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <memory>
+#include <string>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+// A global boolean variable to control whether we free memory when a Tensor
+// is shrunk to a smaller size. As a result, a Tensor is always going to
+// keep the memory allocated for its maximum capacity reshaped to so far.
+//
+// This parameter is respected "upper-case" methods which call Resize()
+// (e.g., CopyFrom, ResizeLike); it is NOT respected by Tensor::resize_
+// or ShrinkTo, both of which guarantee to never to free memory.
+C10_DECLARE_bool(caffe2_keep_on_shrink);
+
+// Since we can have high variance in blob memory allocated across different
+// inputs in the same run, we will shrink the blob only if the memory gain
+// is larger than this flag in bytes.  This only applies to functions which
+// respect caffe2_keep_on_shrink.
+C10_DECLARE_int64(caffe2_max_keep_on_shrink_memory);
+
+namespace at {
+class Tensor;
+class TensorBase;
+} // namespace at
+
+namespace c10 {
+
+/**
+ * A utility function to convert vector<int> to vector<int64_t>.
+ */
+inline std::vector<int64_t> ToVectorint64_t(const ArrayRef<int>& src) {
+  return std::vector<int64_t>(src.begin(), src.end());
+}
+
+/**
+ * Return product of all dimensions starting from k
+ */
+inline int64_t size_from_dim_(int k, IntArrayRef dims) {
+  int64_t r = 1;
+  for (const auto i : c10::irange(k, dims.size())) {
+    r *= dims[i];
+  }
+  return r;
+}
+
+// Product of all dims up to k (not including dims[k])
+inline int64_t size_to_dim_(int k, IntArrayRef dims) {
+  TORCH_CHECK(k >= 0 && static_cast<size_t>(k) <= dims.size());
+  int64_t r = 1;
+  for (const auto i : c10::irange(k)) {
+    r *= dims[i];
+  }
+  return r;
+}
+
+// Product of all dims between k and l (not including dims[k] and dims[l])
+inline int64_t size_between_dim_(int k, int l, IntArrayRef dims) {
+  TORCH_CHECK((unsigned)l < dims.size() && (unsigned)k < dims.size());
+  int64_t r = 1;
+  if (k < l) {
+    for (int i = k + 1; i < l; ++i) {
+      r *= dims[i];
+    }
+  } else {
+    for (int i = l + 1; i < k; ++i) {
+      r *= dims[i];
+    }
+  }
+  return r;
+}
+
+// Wrap around axis_index if it is negative, s.t., -1 is the last dim
+inline int canonical_axis_index_(int axis_index, int ndims) {
+  TORCH_CHECK(axis_index >= -ndims);
+  TORCH_CHECK(axis_index < ndims);
+  if (axis_index < 0) {
+    return axis_index + ndims;
+  }
+  return axis_index;
+}
+
+using PlacementDtor = void (*)(void*, size_t);
+
+/*
+ * A Context that will call extra placement deleter during
+ * deconstruction.
+ *
+ * Accept a already constructed DataPtr and store it as member
+ * during destruction, we'll call extra deleter on the underlying
+ * data pointer before the DataPtr is destructed.
+ * `data_ptr_` owns the memory.
+ */
+struct C10_API PlacementDeleteContext {
+  DataPtr data_ptr_;
+  PlacementDtor placement_dtor_;
+  size_t size_;
+
+  PlacementDeleteContext(
+      DataPtr&& data_ptr,
+      PlacementDtor placement_dtor,
+      size_t size)
+      : data_ptr_(std::move(data_ptr)),
+        placement_dtor_(placement_dtor),
+        size_(size) {}
+
+  PlacementDeleteContext(PlacementDeleteContext&&) noexcept = delete;
+  PlacementDeleteContext(const PlacementDeleteContext&) = delete;
+  PlacementDeleteContext& operator=(const PlacementDeleteContext&) = delete;
+  PlacementDeleteContext& operator=(PlacementDeleteContext&&) = delete;
+  static DataPtr makeDataPtr(
+      DataPtr&& data_ptr,
+      PlacementDtor placement_dtor,
+      size_t size,
+      Device device);
+  ~PlacementDeleteContext() {
+    placement_dtor_(data_ptr_.get(), size_);
+    // original memory will be freed when data_ptr_ is destructed
+  }
+};
+
+struct C10_API AutogradMetaInterface {
+  virtual void set_requires_grad(
+      bool requires_grad,
+      at::TensorImpl* self_impl) = 0;
+  virtual bool requires_grad() const = 0;
+  virtual at::Tensor& mutable_grad() = 0;
+  virtual const at::Tensor& grad() const = 0;
+  virtual const at::Tensor& fw_grad(uint64_t level, const at::TensorBase& self)
+      const = 0;
+  virtual void set_fw_grad(
+      const at::TensorBase& new_grad,
+      const at::TensorBase& self,
+      uint64_t level,
+      bool is_inplace_op) = 0;
+  virtual ~AutogradMetaInterface();
+};
+
+namespace impl {
+
+// Unfortunately, the definition of AutogradMeta lives in a separate
+// compilation unit than TensorImpl (libtorch.so versus libc10.so)
+// which means that we cannot construct an AutogradMeta from TensorImpl,
+// not even from the cpp file.  So we have to indirect it through a factory
+// function which will be initialized when we load libtorch.so.
+
+struct C10_API AutogradMetaFactory {
+  virtual ~AutogradMetaFactory() = default;
+  virtual std::unique_ptr<AutogradMetaInterface> make() const = 0;
+  // This method is the dumbest method.  But I don't have access
+  // to Tensor (not TensorImpl) which is undefined in this header.
+  virtual const at::Tensor& undefined_tensor() const = 0;
+};
+
+C10_API void SetAutogradMetaFactory(AutogradMetaFactory* factory);
+C10_API AutogradMetaFactory* GetAutogradMetaFactory();
+
+struct C10_API AutogradMetaFactoryRegisterer {
+  explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory* factory) {
+    SetAutogradMetaFactory(factory);
+  }
+};
+
+} // namespace impl
+
+struct C10_API NamedTensorMetaInterface {
+  virtual ~NamedTensorMetaInterface() = default;
+  virtual std::unique_ptr<NamedTensorMetaInterface> clone() const {
+    TORCH_INTERNAL_ASSERT(
+        false, "Not implemented: NamedTensorMetaInterface::clone");
+  }
+  virtual int64_t slow_dim() const {
+    TORCH_INTERNAL_ASSERT(
+        false, "Not implemented: NamedTensorMetaInterface::slow_dim");
+  }
+};
+
+// For ease of copy pasting
+#if 0
+is_contiguous
+is_channels_last_contiguous
+is_channels_last_3d_contiguous
+is_channels_last
+is_channels_last_3d
+is_non_overlapping_and_dense
+#endif
+
+/**
+ * This structure is intended to hold additional metadata of the specific device
+ * backend.
+ **/
+struct C10_API BackendMeta : intrusive_ptr_target {
+  ~BackendMeta() override = default;
+  virtual intrusive_ptr<BackendMeta> clone(
+      const intrusive_ptr<BackendMeta>& ptr) const {
+    return ptr;
+  }
+};
+
+struct C10_API ExtraMeta {
+  std::unique_ptr<c10::SymbolicShapeMeta> symbolic_shape_meta_ = nullptr;
+  std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta_ = nullptr;
+  intrusive_ptr<c10::BackendMeta> backend_meta_ = nullptr;
+  std::optional<std::string> custom_data_ptr_error_msg_ = std::nullopt;
+  std::optional<std::string> custom_storage_error_msg_ = std::nullopt;
+
+  ExtraMeta() = default;
+  ~ExtraMeta() = default;
+  ExtraMeta(const ExtraMeta& other) {
+    if (other.symbolic_shape_meta_) {
+      symbolic_shape_meta_ =
+          std::make_unique<c10::SymbolicShapeMeta>(*other.symbolic_shape_meta_);
+    }
+    if (other.named_tensor_meta_) {
+      named_tensor_meta_ = other.named_tensor_meta_->clone();
+    }
+    if (other.backend_meta_) {
+      backend_meta_ = other.backend_meta_->clone(other.backend_meta_);
+    }
+    if (other.custom_data_ptr_error_msg_) {
+      custom_data_ptr_error_msg_ = other.custom_data_ptr_error_msg_;
+    }
+    if (other.custom_storage_error_msg_) {
+      custom_storage_error_msg_ = other.custom_storage_error_msg_;
+    }
+  }
+  ExtraMeta& operator=(const ExtraMeta& other) = delete;
+  ExtraMeta(ExtraMeta&& other) = delete;
+  ExtraMeta& operator=(ExtraMeta&& other) = delete;
+
+  ExtraMeta(
+      std::unique_ptr<c10::SymbolicShapeMeta> symbolic_shape_meta,
+      std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta,
+      intrusive_ptr<c10::BackendMeta> backend_meta,
+      std::optional<std::string> custom_data_ptr_error_msg = std::nullopt,
+      std::optional<std::string> custom_storage_access_error_msg = std::nullopt)
+      : symbolic_shape_meta_(std::move(symbolic_shape_meta)),
+        named_tensor_meta_(std::move(named_tensor_meta)),
+        backend_meta_(std::move(backend_meta)),
+        custom_data_ptr_error_msg_(std::move(custom_data_ptr_error_msg)),
+        custom_storage_error_msg_(std::move(custom_storage_access_error_msg)) {}
+
+  std::unique_ptr<ExtraMeta> clone() const {
+    return std::make_unique<ExtraMeta>(*this);
+  }
+};
+
+// NOTE [ Version Counter Sharing ]
+//
+// Every Tensor has a version counter. Version counters are incremented whenever
+// the data or size of a tensor changes through in-place Variable operations.
+// Version counters are used to detect modifications to saved variables which
+// would result in incorrect gradient calculations. Version counters may be
+// shared between Variables:
+//
+// 1. A view shares the version counter of the base Variable,
+// 2. `x.detach()` shares the version counter of `x`,
+// 3. Unpacked saved variables share the version counter of the source.
+//
+// Version counters are not shared in these scenarios:
+//
+// 1. When we replace a `Variable`'s underlying `Tensor` by calling
+// `set_data(...)`,
+// 2. `x.data` does not share the version counter of `x`. (See discussion at
+// https://github.com/pytorch/pytorch/issues/5396)
+//
+// Question: Why do we put the version counter in TensorImpl instead of
+// AutogradMeta?
+//
+// Answer: After the Variable/Tensor merge, a tensor will not have AutogradMeta
+// when its `requires_grad_` is false, but when we use this tensor in the
+// forward pass of a function that requires saving this tensor for backward, we
+// need to keep track of this tensor's version to make sure it's always valid in
+// the autograd graph.
+//
+// To achieve this goal, we put the version counter in TensorImpl instead of
+// AutogradMeta, and have it always be available. This allows us to have the
+// optimization of not carrying AutogradMeta when a tensor doesn't require
+// gradient.
+//
+// A hypothetical alternative way to achieve this goal is to initialize
+// AutogradMeta and create the version counter for the non-requires-grad tensor
+// only when it's saved for backward. However, since saving a tensor for
+// backward happens in the forward pass, and our invariant is that forward pass
+// needs to be thread-safe, lazy-initializing AutogradMeta when saving a tensor
+// can introduce race conditions when we are running the forward pass in
+// multi-thread scenarios, thus making the forward pass not thread-safe anymore,
+// which breaks the invariant.
+struct C10_API VariableVersion {
+ private:
+  struct VersionCounter : intrusive_ptr_target {
+    VersionCounter(uint32_t version) : version_(version) {}
+    std::atomic<uint32_t> version_;
+  };
+  c10::intrusive_ptr<VersionCounter> version_counter_;
+
+ public:
+  // Note [Disabled VariableVersion]
+  // VariableVersion struct has an intrusive_ptr pointing VersionCounter struct
+  // with an atomic variable. Thus `VariableVersion(/*version=*/0)` is not as
+  // cheap as we expected. In some cases constructing a VariableVersion with
+  // version 0 is not necessary so we add a cheap constructor which
+  // doesn't allocate the intrusive_ptr.
+  // Example use cases are:
+  //  - Inference tensors don't track version counter, so they'll just always
+  //    have disabled VariableVersion.
+  //  - In SavedVariable class we override version_counter_ inside its
+  //  constructor
+  //    so that we can use the cheap constructor there.
+  enum Disabled { DISABLED };
+  // It's okay to return true even for inference tensor which
+  // doesn't have version counter enabled.
+  // We want to be permissive here since in many cases (e.g. make_variable)
+  // we can std::move a TensorImpl if there's no other uses which saves us
+  // an additional TensorImpl allocation.
+  bool unique() const {
+    return version_counter_ ? 1 == version_counter_.use_count() : true;
+  }
+  // NOTE: As of C++11 and 14, default-constructing a std::atomic variable
+  // leaves it in a persistently undefined state. See
+  // https://cplusplus.github.io/LWG/issue2334.
+  VariableVersion(uint32_t version)
+      : version_counter_(c10::make_intrusive<VersionCounter>(version)) {}
+  VariableVersion(Disabled = DISABLED) {}
+
+  bool enabled() const {
+    return version_counter_;
+  }
+
+  // Note [Inplace update inference tensor]
+  // 1. Inplace update to inference tensor is forbidden in normal mode.
+  //   For example:
+  //     inference_tensor.copy_(normal_tensor_requires_grad)
+  //   This inplace makes inference_tensor have requires_grad=True and
+  //   have a grad_fn.  This is bad because views of `inference_tensor`
+  //   created in InferenceMode won't be able to know the grad_fn since
+  //   their ViewMeta were not recorded. To match NoGradMode behavior
+  //   that "inplace update to a view created in NoGradMode raise an error",
+  //   we just ban inplace update to inference tensor since we can't tell
+  //   if an inference tensor is a view created in InferenceMode.
+  //
+  //   Note that views of normal tensor created in InferenceMode has proper
+  //   ViewMeta so that they're aware of the grad_fn correctly.
+  //
+  // 2. Inplace update to inference tensor in inference tensor doesn't bump
+  //    version counter.
+  //    * It either doesn't call bump() by skipping ADInplaceOrView kernel,
+  //      - e.g. inference_tensor.add_(1)
+  //    * or bump() is a no-op for inference tensor.
+  //      - e.g. inference_tensor.add_(normal_tensor)
+  void bump() {
+    // TODO: Replace the link to the documentation once it's available.
+    TORCH_CHECK(
+        version_counter_ || InferenceMode::is_enabled(),
+        "Inplace update to inference tensor outside InferenceMode is not allowed."
+        "You can make a clone to get a normal tensor before doing inplace update."
+        "See https://github.com/pytorch/rfcs/pull/17 for more details.");
+    if (version_counter_) {
+      ++version_counter_->version_;
+    }
+  }
+
+  void set_version(int64_t i) {
+    TORCH_CHECK(
+        version_counter_,
+        "Tried to call torch.autograd._unsafe_set_version() on a tensor "
+        "that does not have a version counter. Was it created in inference mode?");
+    TORCH_CHECK(i >= 0, "Cannot set a version_counter to a value below 0: ", i);
+    version_counter_->version_ = i;
+  }
+
+  // Inference tensor doesn't have version counter so it shouldn't be
+  // accessed.
+  uint32_t current_version() const {
+    TORCH_CHECK(
+        version_counter_, "Inference tensors do not track version counter.");
+    return version_counter_->version_;
+  }
+};
+
+// Forward declaration of TensorImpl needed for forward declaration of
+// C10_TensorImpl_Size_Check_Dummy_Class
+struct C10_API TensorImpl;
+
+/**
+ * NOTE: Some TensorImpl methods are small and not overridden in the
+ * PyTorch codebase itself, but may theoretically need to be
+ * overridden by third-party TensorImpl subclasses. This macro allows
+ * users that need maximum performance and don't need these extension
+ * points to disable them with a build-time flag. (In particular,
+ * XLA's XLATensorImpl currently overrides these methods, so we can't
+ * enable this flag by default.)
+ */
+#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
+#define TENSORIMPL_MAYBE_VIRTUAL
+#else
+#define TENSORIMPL_MAYBE_VIRTUAL virtual
+#endif
+
+/**
+ * The low-level representation of a tensor, which contains a pointer
+ * to a storage (which contains the actual data) and metadata (e.g., sizes and
+ * strides) describing this particular view of the data as a tensor.
+ *
+ * Some basic characteristics about our in-memory representation of
+ * tensors:
+ *
+ *  - It contains a pointer to a storage struct (Storage/StorageImpl)
+ *    which contains the pointer to the actual data and records the
+ *    data type and device of the view.  This allows multiple tensors
+ *    to alias the same underlying data, which allows to efficiently
+ *    implement differing *views* on a tensor.
+ *
+ *  - The tensor struct itself records view-specific metadata about
+ *    the tensor, e.g., sizes, strides and offset into storage.
+ *    Each view of a storage can have a different size or offset.
+ *
+ *  - This class is intrusively refcounted.  It is refcounted so that
+ *    we can support prompt deallocation of large tensors; it is
+ *    intrusively refcounted so that we can still perform reference
+ *    counted operations on raw pointers, which is often more convenient
+ *    when passing tensors across language boundaries.
+ *
+ *  - For backwards-compatibility reasons, a tensor may be in an
+ *    uninitialized state.  A tensor may be uninitialized in the following
+ *    two ways:
+ *
+ *      - A tensor may be DTYPE UNINITIALIZED.  A tensor of this
+ *        form has an uninitialized dtype.  This situation most
+ *        frequently arises when a user writes Tensor x(CPU).  The dtype
+ *        is subsequently initialized when mutable_data<T>() is
+ *        invoked for the first time.
+ *
+ *      - A tensor may be STORAGE UNINITIALIZED.  A tensor of this form
+ *        has non-zero size, but has a storage with a null data pointer.
+ *        This situation most frequently arises when a user calls
+ *        Resize() or FreeMemory().  This is because Caffe2 historically
+ *        does lazy allocation: allocation of data doesn't occur until
+ *        mutable_data<T>() is invoked.  A tensor with zero size is
+ *        always storage initialized, because no allocation is necessary
+ *        in this case.
+ *
+ *    All combinations of these two uninitialized states are possible.
+ *    Consider the following transcript in idiomatic Caffe2 API:
+ *
+ *      Tensor x(CPU); // x is storage-initialized, dtype-UNINITIALIZED
+ *      x.Resize(4); // x is storage-UNINITIALIZED, dtype-UNINITIALIZED
+ *      x.mutable_data<float>(); // x is storage-initialized, dtype-initialized
+ *      x.FreeMemory(); // x is storage-UNINITIALIZED, dtype-initialized.
+ *
+ *    All other fields on tensor are always initialized.  In particular,
+ *    size is always valid. (Historically, a tensor declared as Tensor x(CPU)
+ *    also had uninitialized size, encoded as numel == -1, but we have now
+ *    decided to default to zero size, resulting in numel == 0).
+ *
+ *    Uninitialized storages MUST be uniquely owned, to keep our model
+ *    simple.  Thus, we will reject operations which could cause an
+ *    uninitialized storage to become shared (or a shared storage to
+ *    become uninitialized, e.g., from FreeMemory).
+ *
+ *    In practice, tensors which are storage-UNINITIALIZED and
+ *    dtype-UNINITIALIZED are *extremely* ephemeral: essentially,
+ *    after you do a Resize(), you basically always call mutable_data()
+ *    immediately afterwards.  Most functions are not designed to
+ *    work if given a storage-UNINITIALIZED, dtype-UNINITIALIZED tensor.
+ *
+ *    We intend to eliminate all uninitialized states, so that every
+ *    tensor is fully initialized in all fields.  Please do not write new code
+ *    that depends on these uninitialized states.
+ */
+struct C10_API TensorImpl : public c10::intrusive_ptr_target {
+  TensorImpl() = delete;
+  ~TensorImpl() override;
+  // Note [Enum ImplType]
+  // This enum is temporary. In the followup refactor we should
+  // think about how to specialize TensorImpl creation for view
+  // tensors. Currently we only special case its key_set_ but
+  // there's also potential to share version_counter_ directly
+  // without creating first and then override in as_view.
+  enum ImplType { VIEW };
+
+  /**
+   * Construct a 1-dim 0-size tensor backed by the given storage.
+   */
+  TensorImpl(
+      Storage&& storage,
+      DispatchKeySet,
+      const caffe2::TypeMeta data_type);
+
+  // See Note [Enum ImplType]
+  TensorImpl(
+      ImplType,
+      Storage&& storage,
+      DispatchKeySet,
+      const caffe2::TypeMeta data_type);
+
+  /**
+   * Construct a 1-dim 0 size tensor that doesn't have a storage.
+   */
+  TensorImpl(
+      DispatchKeySet,
+      const caffe2::TypeMeta data_type,
+      std::optional<c10::Device> device_opt);
+
+  // Legacy constructors so I don't have to go update call sites.
+  // TODO: When Variable is added, delete these constructors
+  TensorImpl(
+      Storage&& storage,
+      DispatchKey dispatch_key,
+      const caffe2::TypeMeta data_type)
+      : TensorImpl(
+            std::move(storage),
+            DispatchKeySet(dispatch_key),
+            data_type) {}
+  TensorImpl(
+      DispatchKey dispatch_key,
+      const caffe2::TypeMeta data_type,
+      std::optional<c10::Device> device_opt)
+      : TensorImpl(DispatchKeySet(dispatch_key), data_type, device_opt) {}
+
+ private:
+  // This constructor is private, because the data_type is redundant with
+  // storage.  Still, we pass it in separately because it's easier to write
+  // the initializer list if we're not worried about storage being moved out
+  // from under us.
+  TensorImpl(
+      Storage&& storage,
+      DispatchKeySet,
+      const caffe2::TypeMeta data_type,
+      std::optional<c10::Device>);
+
+ public:
+  TensorImpl(const TensorImpl&) = delete;
+  TensorImpl& operator=(const TensorImpl&) = delete;
+  TensorImpl(TensorImpl&&) = delete;
+  TensorImpl& operator=(TensorImpl&&) = delete;
+
+  /**
+   * Release (decref) storage, and any other external allocations.  This
+   * override is for `intrusive_ptr_target` and is used to implement weak
+   * tensors.
+   */
+  void release_resources() override;
+
+ public:
+  /**
+   * Return the DispatchKeySet corresponding to this Tensor, specifying
+   * all of the DispatchKeys that this Tensor identifies as.  This is the
+   * information used to dispatch operations on this tensor.
+   */
+  DispatchKeySet key_set() const {
+    return key_set_;
+  }
+
+ private:
+  [[noreturn]] void throw_cannot_call_with_symbolic(const char* meth) const;
+
+  // NOTE: The general recipe for customizable methods is that the fastpath
+  // function (e.g., sizes()) does an unlikely policy test, and if doesn't
+  // trigger, it does the fast path implementation with no checks and going
+  // directly to on-TensorImpl fields.  In particular, you never need to
+  // check ExtraMeta if the policy doesn't trigger, as non-trivial ExtraMeta
+  // implies the policy will always match.
+  //
+  // The default implementations of methods are "safe": they do extra tests
+  // to make sure the internal state is consistent no matter if you are
+  // doing symbolic shapes or not.  If you don't want the tests, directly
+  // override the custom method (e.g., custom_sizes()) to do your preferred
+  // behavior.
+
+ public:
+  /**
+   * Return a reference to the sizes of this tensor.  This reference remains
+   * valid as long as the tensor is live and not resized.
+   */
+  IntArrayRef sizes() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return sizes_custom();
+    }
+    return sizes_and_strides_.sizes_arrayref();
+  }
+
+  SymIntArrayRef sym_sizes() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return sym_sizes_custom();
+    }
+    // Sizes guaranteed to be non-negative, so unchecked cast is OK
+    return c10::fromIntArrayRefKnownNonNegative(
+        sizes_and_strides_.sizes_arrayref());
+  }
+
+  IntArrayRef sizes_default() const {
+    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
+      throw_cannot_call_with_symbolic("sizes");
+    }
+    return sizes_and_strides_.sizes_arrayref();
+  }
+
+  SymIntArrayRef sym_sizes_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return symbolic_shape_meta().sizes_;
+    } else {
+      // Sizes guaranteed to be non-negative, so unchecked cast is OK
+      return c10::fromIntArrayRefKnownNonNegative(sizes_default());
+    }
+  }
+
+  // From https://stackoverflow.com/a/3057522/23845
+  // TODO: does C++14 have a stdlib template for this?
+  template <typename T>
+  struct identity {
+    typedef T type;
+  };
+
+  template <typename T>
+  ArrayRef<T> generic_sizes() {
+    return _generic_sizes(identity<T>());
+  }
+
+  ArrayRef<int64_t> _generic_sizes(identity<int64_t>) {
+    return sizes();
+  }
+  ArrayRef<c10::SymInt> _generic_sizes(identity<c10::SymInt>) {
+    return sym_sizes();
+  }
+
+  template <typename T>
+  ArrayRef<T> generic_strides() {
+    return _generic_strides(identity<T>());
+  }
+
+  ArrayRef<int64_t> _generic_strides(identity<int64_t>) {
+    return strides();
+  }
+  ArrayRef<c10::SymInt> _generic_strides(identity<c10::SymInt>) {
+    return sym_strides();
+  }
+
+  template <typename T>
+  T generic_storage_offset() {
+    return _generic_storage_offset(identity<T>());
+  }
+
+  int64_t _generic_storage_offset(identity<int64_t>) {
+    return storage_offset();
+  }
+  c10::SymInt _generic_storage_offset(identity<c10::SymInt>) {
+    return sym_storage_offset();
+  }
+
+  /**
+   * The number of elements in a tensor.
+   *
+   * WARNING: Previously, if you were using the Caffe2 API, you could
+   * test numel() == -1 to see if a tensor was uninitialized.  This
+   * is no longer true; numel always accurately reports the product
+   * of sizes of a tensor.
+   */
+  int64_t numel() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return numel_custom();
+    }
+    return numel_;
+  }
+
+  c10::SymInt sym_numel() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return sym_numel_custom();
+    }
+    return c10::SymInt(SymInt::UNCHECKED, numel_);
+  }
+
+  int64_t numel_default() const {
+    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
+      throw_cannot_call_with_symbolic("numel");
+    }
+    return numel_;
+  }
+
+  c10::SymInt sym_numel_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return symbolic_shape_meta().numel();
+    } else {
+      return c10::SymInt(SymInt::UNCHECKED, numel_);
+    }
+  }
+
+  /**
+   * Return the number of dimensions of this tensor.  Note that 0-dimension
+   * represents a Tensor that is a Scalar, e.g., one that has a single element.
+   */
+  int64_t dim() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return dim_custom();
+    }
+    return static_cast<int64_t>(sizes_and_strides_.size());
+  }
+
+  int64_t dim_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return static_cast<int64_t>(symbolic_shape_meta().sizes_.size());
+    } else {
+      return static_cast<int64_t>(sizes_and_strides_.size());
+    }
+  }
+
+  /**
+   * Return the offset in number of elements into the storage that this
+   * tensor points to.  Most tensors have storage_offset() == 0, but,
+   * for example, an index into a tensor will have a non-zero storage_offset().
+   *
+   * WARNING: This is NOT computed in bytes.
+   */
+  int64_t storage_offset() const {
+    // TODO: maybe this should be toggled by strides
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return storage_offset_custom();
+    }
+    return storage_offset_;
+  }
+
+  c10::SymInt sym_storage_offset() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return sym_storage_offset_custom();
+    }
+    return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
+  }
+
+  int64_t storage_offset_default() const {
+    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
+      throw_cannot_call_with_symbolic("storage_offset");
+    }
+    return storage_offset_;
+  }
+
+  c10::SymInt sym_storage_offset_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return symbolic_shape_meta().storage_offset_;
+    } else {
+      return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
+    }
+  }
+
+  /**
+   * Return a reference to the strides of this tensor.  This reference remains
+   * valid as long as the tensor is live and not restrided.
+   */
+  IntArrayRef strides() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      return strides_custom();
+    }
+    return sizes_and_strides_.strides_arrayref();
+  }
+
+  c10::SymIntArrayRef sym_strides() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      return sym_strides_custom();
+    }
+    return c10::fromIntArrayRefKnownNonNegative(strides_default());
+  }
+
+  IntArrayRef strides_default() const {
+    if (C10_UNLIKELY(has_symbolic_sizes_strides_)) {
+      throw_cannot_call_with_symbolic("strides");
+    }
+    return sizes_and_strides_.strides_arrayref();
+  }
+
+  c10::SymIntArrayRef sym_strides_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return symbolic_shape_meta().strides_;
+    } else {
+      return c10::fromIntArrayRefKnownNonNegative(strides_default());
+    }
+  }
+
+  /**
+   * Whether or not a tensor is laid out in contiguous memory.
+   *
+   * Tensors with non-trivial strides are not contiguous.  See
+   * compute_contiguous() for the exact definition of whether or not
+   * a tensor is contiguous or not.
+   */
+  bool is_contiguous(
+      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      return is_contiguous_custom(memory_format);
+    }
+    return is_contiguous_default(memory_format);
+  }
+
+  // These are factored into separate functions in case subclasses
+  // want to use them
+  bool is_contiguous_default(at::MemoryFormat memory_format) const {
+    if (has_symbolic_sizes_strides_) {
+      if (memory_format == at::MemoryFormat::ChannelsLast) {
+        return symbolic_shape_meta().is_channels_last_contiguous().guard_bool(
+            __FILE__, __LINE__);
+      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
+        return symbolic_shape_meta()
+            .is_channels_last_3d_contiguous()
+            .guard_bool(__FILE__, __LINE__);
+      }
+      return symbolic_shape_meta().is_contiguous().guard_bool(
+          __FILE__, __LINE__);
+    }
+
+    if (memory_format == at::MemoryFormat::ChannelsLast) {
+      return is_channels_last_contiguous_;
+    } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
+      return is_channels_last_3d_contiguous_;
+    }
+    return is_contiguous_;
+  }
+
+  bool is_strides_like_default(at::MemoryFormat memory_format) const {
+    if (has_symbolic_sizes_strides_) {
+      if (memory_format == at::MemoryFormat::ChannelsLast) {
+        return symbolic_shape_meta().is_channels_last().guard_bool(
+            __FILE__, __LINE__);
+      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
+        return symbolic_shape_meta().is_channels_last_3d().guard_bool(
+            __FILE__, __LINE__);
+      } else {
+        return false;
+      }
+    }
+
+    if (memory_format == at::MemoryFormat::ChannelsLast) {
+      return is_channels_last_;
+    } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
+      return is_channels_last_3d_;
+    } else {
+      return false;
+    }
+  }
+
+  bool is_non_overlapping_and_dense_default() const {
+    if (has_symbolic_sizes_strides_) {
+      return symbolic_shape_meta().is_non_overlapping_and_dense().guard_bool(
+          __FILE__, __LINE__);
+    } else {
+      return is_non_overlapping_and_dense_;
+    }
+  }
+
+  // NB: these dim accessor functions don't have _default(), as you can use
+  // sizes_default/strides_default
+  /**
+   * Return the size of a tensor at some dimension, wrapping the dimension if
+   * necessary.
+   *
+   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
+   * be faster
+   */
+  int64_t size(int64_t d) const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return size_custom(d);
+    }
+    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
+    return sizes_and_strides_.size_at_unchecked(d);
+  }
+
+  c10::SymInt sym_size(int64_t d) const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
+      return sym_size_custom(d);
+    }
+    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
+    const auto sizes = this->sym_sizes();
+    return sizes[d];
+  }
+
+  /**
+   * Return the stride of a tensor at some dimension, wrapping the dimension
+   * if necessary.
+   *
+   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
+   * be faster
+   */
+  int64_t stride(int64_t d) const {
+    d = maybe_wrap_dim(d, dim(), false);
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      // TODO: provide stride_custom, symmetrically with size_custom.
+      // There is presently no user for it; only NestedTensor is using
+      // size_custom overrideability
+      return strides_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
+    }
+    // Intentionally don't call default, which also handles symbolic
+    return sizes_and_strides_.stride_at_unchecked(d);
+  }
+
+  enum class SizesStridesPolicy : uint8_t {
+    // Default behavior, e.g., dense tensor.
+    //
+    // Can override: nothing
+    Default = 0,
+    // Customizable strides behavior, e.g., sparse tensor,
+    // mkldnn tensor.
+    //
+    // Can override: strides(), is_contiguous()
+    CustomStrides = 1,
+    // Customizable sizes behavior, e.g., nested tensor
+    //
+    // Can override: strides(), is_contiguous(), sizes(), dim(), numel()
+    CustomSizes = 2
+  };
+
+ protected:
+  inline bool matches_policy(SizesStridesPolicy policy) const {
+    return sizes_strides_policy_ >= static_cast<uint8_t>(policy);
+  }
+
+  inline bool matches_custom(SizesStridesPolicy policy) const {
+    return custom_sizes_strides_ >= static_cast<uint8_t>(policy);
+  }
+
+  inline bool matches_python_custom(SizesStridesPolicy policy) const {
+    auto r = python_custom_sizes_strides_ >= static_cast<uint8_t>(policy);
+    if (r) {
+      TORCH_INTERNAL_ASSERT(is_python_dispatch())
+    }
+    return r;
+  }
+
+  /**
+   * Customization points for the functions above.  sizes_strides_policy_
+   * must be set to enable these.
+   *
+   * NB: dim is overrideable separately from sizes because it is possible
+   * for a tensor to have rank, but not well defined sizes.
+   */
+  // sizes_strides_policy_ >= CustomStrides
+  virtual bool is_contiguous_custom(at::MemoryFormat memory_format) const;
+  virtual bool is_strides_like_custom(at::MemoryFormat memory_format) const;
+  virtual bool is_non_overlapping_and_dense_custom() const;
+  // sizes_strides_policy_ >= CustomSizes
+  // Currently this method only exists to be overwritten by subclasses such as
+  // NestedTensorImpl.
+  virtual int64_t size_custom(int64_t d) const {
+    // TODO: We could add support to Python dispatch here.
+    // TODO: We could call into aten::size.int instead of
+    // sizes_custom()[d] and enable use of the dispatcher.
+    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
+    return sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
+  }
+
+  virtual c10::SymInt sym_size_custom(int64_t d) const {
+    // TODO: We could add support to Python dispatch here.
+    // TODO: We could call into aten::size.int instead of
+    // sym_sizes_custom()[d] and enable use of the dispatcher.
+    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
+    return sym_sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
+  }
+
+  virtual IntArrayRef sizes_custom() const;
+  virtual IntArrayRef strides_custom() const;
+  virtual int64_t numel_custom() const;
+  virtual int64_t storage_offset_custom() const;
+  virtual int64_t dim_custom() const;
+  virtual Device device_custom() const;
+  virtual Layout layout_custom() const;
+
+  virtual c10::SymIntArrayRef sym_sizes_custom() const;
+  virtual c10::SymIntArrayRef sym_strides_custom() const;
+  virtual c10::SymInt sym_numel_custom() const;
+  virtual c10::SymInt sym_storage_offset_custom() const;
+
+ public:
+  /**
+   * True if this tensor has storage. See storage() for details.
+   */
+#ifdef DEBUG
+  // Allow subclasses to check that their storage_ is never getting set in debug
+  // builds.
+  virtual
+#else
+  TENSORIMPL_MAYBE_VIRTUAL
+#endif
+      bool
+      has_storage() const
+  // NOTE: we devirtualize this because it arguably shouldn't be an
+  // error just to ask subclasses if they have storage.
+  // This used to throw for most subclasses, but OpaqueTensorImpl
+  // wanted it to successfully return false, so we went ahead and made
+  // it a non-error.
+#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
+  {
+    return storage_;
+  }
+#else
+      ;
+#endif
+
+  /**
+   * Return the underlying storage of a Tensor.  Multiple tensors may share
+   * a single storage.  A Storage is an impoverished, Tensor-like class
+   * which supports far less operations than Tensor.
+   *
+   * Avoid using this method if possible; try to use only Tensor APIs to perform
+   * operations.
+   */
+  TENSORIMPL_MAYBE_VIRTUAL const Storage& storage() const {
+    if (C10_UNLIKELY(storage_access_should_throw_)) {
+      throw_storage_access_error();
+    }
+    return storage_;
+  }
+
+  /**
+   * Return the underlying storage, unsafely assuming this is a basic strided
+   * tensor. In cases where `storage` access would throw, this returns a
+   * default-constructed Storage.
+   */
+  inline const Storage& unsafe_storage() const {
+    return storage_;
+  }
+
+  bool unique_version() const {
+    return version_counter_.unique();
+  }
+
+ protected:
+  virtual Layout layout_impl() const {
+    TORCH_CHECK(
+        false, "layout_impl is only implemented for TensorImpl subclasses.");
+  }
+
+ public:
+  // Whether a tensor is sparse COO or not.
+  bool is_sparse() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    return key_set_.has_all(c10::sparse_ks);
+  }
+
+  // Whether a tensor is sparse CSR or not.
+  bool is_sparse_csr() const {
+    return layout() == kSparseCsr;
+  }
+
+  // Whether a tensor is sparse CSR/CSC/BSR/BSC or not.
+  bool is_sparse_compressed() const {
+    return key_set_.has_all(c10::sparse_csr_ks);
+  }
+
+  bool is_quantized() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    constexpr auto quantized_ks = DispatchKeySet(DispatchKey::Quantized);
+    return key_set_.has_all(quantized_ks);
+  }
+
+  bool is_meta() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_meta();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kMeta;
+  }
+
+  bool is_cpu() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_cpu();
+    }
+    // Note: we cannot rely on dispatch keys to determine the device type
+    // of a tensor, because "wrapper" tensors (like FunctionalTensorWrapper)
+    // don't include backend dispatch keys.
+    return device_opt_.has_value() && device_opt_->type() == kCPU;
+  }
+
+  bool is_cuda() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_cuda();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kCUDA;
+  }
+
+  bool is_xpu() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_xpu();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kXPU;
+  }
+
+  bool is_ipu() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_ipu();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kIPU;
+  }
+
+  bool is_xla() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_xla();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kXLA;
+  }
+
+  bool is_mtia() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_mtia();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kMTIA;
+  }
+
+  bool is_hpu() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_hpu();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kHPU;
+  }
+
+  bool is_lazy() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_lazy();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kLazy;
+  }
+
+  bool is_hip() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_hip();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kHIP;
+  }
+
+  bool is_ve() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_ve();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kVE;
+  }
+
+  bool is_privateuseone() const {
+    // NB: This method is not virtual and avoid dispatches for performance
+    // reasons.
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_privateuseone();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kPrivateUse1;
+  }
+
+  bool is_mkldnn() const {
+    return key_set_.has_all(c10::mkldnn_ks);
+  }
+
+  bool is_vulkan() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_vulkan();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kVulkan;
+  }
+
+  bool is_metal() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_metal();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kMetal;
+  }
+
+  bool is_mps() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_mps();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kMPS;
+  }
+
+  bool is_maia() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().is_maia();
+    }
+    return device_opt_.has_value() && device_opt_->type() == kMAIA;
+  }
+
+  bool is_nested() const {
+    return key_set_.has(DispatchKey::NestedTensor);
+  }
+
+  // TODO: remove this once we don't automatically enabled Autograd dispatch
+  // keys
+  //       in TensorImpl constructor.
+  // DON'T USE THIS API!! It's only created for testing purpose in
+  // file aten/src/ATen/core/boxing/impl/test_helpers.h
+  void remove_autograd_key() {
+    key_set_ = key_set_ - autograd_dispatch_keyset;
+  }
+
+  // Inference tensor doesn't have autograd or ADInplaceOrView key.
+  // Invariant:
+  //   Inference tensor has version_counter_.enabled() == false
+  bool is_inference() {
+    bool no_ADInplaceOrView = !key_set_.has_any(c10::inplace_or_view_ks);
+    bool no_Autograd = !key_set_.has_any(c10::autograd_dispatch_keyset);
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        no_ADInplaceOrView == no_Autograd,
+        "ADInplaceOrView and Autograd keys must be on/off at the same time.");
+    return no_ADInplaceOrView && no_Autograd;
+  }
+
+  DeviceIndex get_device() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom().index();
+    }
+    return device_default().index();
+  }
+
+  Device device() const {
+    if (C10_UNLIKELY(device_policy_)) {
+      return device_custom();
+    }
+    return device_default();
+  }
+
+ protected:
+  c10::Device device_default() const {
+    TORCH_CHECK(device_opt_.has_value(), "tensor does not have a device");
+    // See NOTE [std::optional operator usage in CUDA]
+    return *device_opt_;
+  }
+
+ public:
+  Layout layout() const {
+    if (C10_UNLIKELY(layout_policy_)) {
+      return layout_custom();
+    }
+
+    // NB: This method is not virtual and avoid dispatches for perf.
+    // strided is also the most common layout type, so we check for
+    // strided case first.
+    // This keyset must also be kept in sync with the logic in
+    // is_sparse() / is_sparse_csr() / is_mkldnn()
+    constexpr auto sparse_and_sparsecsr_and_mkldnn_ks =
+        c10::sparse_ks | c10::sparse_csr_ks | c10::mkldnn_ks;
+    if (!key_set_.has_any(sparse_and_sparsecsr_and_mkldnn_ks)) {
+      return kStrided;
+    } else if (is_sparse()) {
+      return kSparse;
+    } else if (is_sparse_compressed()) {
+      // Typically, the tensor dispatch keys define the tensor layout
+      // uniquely. This allows using non-virtual layout method for
+      // better performance. However, when tensor's layout depends,
+      // say, on tensor attributes, one must use this execution path
+      // where the corresponding tensor impl class overwrites virtual
+      // layout_impl() method.
+      //
+      // TODO: implement layout() as native function/method so that
+      // __torch_dispatch__ users will be able to redefine the
+      // layout() method.
+      return layout_impl();
+    } else {
+      TORCH_INTERNAL_ASSERT(
+          is_mkldnn(), "There is an error in the layout calculation logic.");
+      return kMkldnn;
+    }
+  }
+
+  /**
+   * True if a tensor was auto-wrapped from a C++ or Python number.
+   * For example, when you write 't + 2', 2 is auto-wrapped into a Tensor
+   * with `is_wrapped_number_` set to true.
+   *
+   * Wrapped numbers do not participate in the result type computation for
+   * mixed-type operations if there are any Tensors that are not wrapped
+   * numbers.  This is useful, because we want 't + 2' to work with
+   * any type of tensor, not just LongTensor (which is what integers
+   * in Python represent).
+   *
+   * Otherwise, they behave like their non-wrapped equivalents.
+   * See [Result type computation] in TensorIterator.h.
+   *
+   * Why did we opt for wrapped numbers, as opposed to just having
+   * an extra function add(Tensor, Scalar)?  This helps greatly reduce
+   * the amount of code we have to write for add, when actually
+   * a Tensor-Scalar addition is really just a Tensor-Tensor
+   * addition when the RHS is 0-dim (except for promotion behavior.)
+   */
+  bool is_wrapped_number() const {
+    return is_wrapped_number_;
+  }
+
+  /**
+   * Set whether or not a tensor was auto-wrapped from a C++ or Python
+   * number.  You probably don't want to call this, unless you are
+   * writing binding code.
+   */
+  void set_wrapped_number(bool value) {
+    TORCH_INTERNAL_ASSERT(dim() == 0);
+    is_wrapped_number_ = value;
+  }
+
+  /**
+   * Returns true if Tensor supports as_strided and as_strided_backward.
+   * This is used in autograd to perform inplace update on view Tensors.
+   * See Note [View + Inplace update for base tensor] and
+   * [View + Inplace update for view tensor] for details.
+   * Note this method only returns true for XLA backend, where it
+   * simulates strided Tensor to support most view ops, but it cannot
+   * fully support general `as_strided` case.
+   * It can be expanded as needed in the future, e.g sparse Tensor.
+   */
+  inline bool support_as_strided() const {
+    if (is_nested()) {
+      return false;
+    }
+    if (key_set_.has(DispatchKey::Functionalize)) {
+      return false;
+    }
+    return device().supports_as_strided();
+  }
+
+  // ~~~~~ Autograd API ~~~~~
+  // Some methods below are defined in TensorImpl.cpp because Tensor is an
+  // incomplete type.
+
+  /**
+   * Set whether or not a tensor requires gradient.
+   */
+  void set_requires_grad(bool requires_grad);
+
+  /**
+   * True if a tensor requires gradient.  Tensors which require gradient
+   * have history tracked for any operations performed on them, so that
+   * we can automatically differentiate back to them.  A tensor that
+   * requires gradient and has no history is a "leaf" tensor, which we
+   * accumulate gradients into.
+   */
+  bool requires_grad() const;
+
+  /**
+   * Return a mutable reference to the gradient.  This is conventionally
+   * used as `t.grad() = x` to set a gradient to a completely new tensor.
+   */
+  at::Tensor& mutable_grad();
+
+  /**
+   * Return the accumulated gradient of a tensor.  This gradient is written
+   * into when performing backwards, when this tensor is a leaf tensor.
+   */
+  const at::Tensor& grad() const;
+
+  /**
+   * Whether or not the imaginary part of the tensor should be negated
+   */
+  inline bool is_conj() const {
+    constexpr auto conjugate_ks = DispatchKeySet(DispatchKey::Conjugate);
+    return key_set_.has_all(conjugate_ks);
+  }
+
+  /**
+   * Set whether or not to take the conjugate of the tensor (flip the imaginary
+   * bit).
+   */
+  void _set_conj(bool value) {
+    if (value) {
+      key_set_ = key_set_.add(DispatchKey::Conjugate);
+      TORCH_INTERNAL_ASSERT(isComplexType(typeMetaToScalarType(dtype())));
+    } else {
+      key_set_ = key_set_.remove(DispatchKey::Conjugate);
+    }
+  }
+
+  /**
+   * XXX: do not use, private api!
+   * Update the backend component related keys to the backend component
+   * corresponding to this device.
+   */
+  void _change_backend_component_keys(c10::Device device);
+
+  /**
+   * Whether or not the tensor is a zerotensor
+   */
+  inline bool _is_zerotensor() const {
+    constexpr auto zerotensor_ks = DispatchKeySet(DispatchKey::ZeroTensor);
+    return key_set_.has_all(zerotensor_ks);
+  }
+
+  /**
+   Set whether or not the tensor is a zero tensor
+  */
+  void _set_zero(bool value) {
+    if (value) {
+      TORCH_INTERNAL_ASSERT(
+          false,
+          "Please call `torch._efficientzerotensor` if you want to create a tensor with no storage.");
+    } else {
+      key_set_ = key_set_.remove(DispatchKey::ZeroTensor);
+    }
+  }
+
+  /**
+   * Whether or not the tensor should be negated
+   */
+  inline bool is_neg() const {
+    constexpr auto negative_ks = DispatchKeySet(DispatchKey::Negative);
+    return key_set_.has_all(negative_ks);
+  }
+
+  /**
+   * Set whether or not to take the conjugate of the tensor (flip the imaginary
+   * bit).
+   */
+  void _set_neg(bool value) {
+    if (value) {
+      key_set_ = key_set_.add(DispatchKey::Negative);
+    } else {
+      key_set_ = key_set_.remove(DispatchKey::Negative);
+    }
+  }
+
+  /**
+   * Return the accumulated gradient of a tensor. This gradient is computed
+   * using forward mode AD.
+   *
+   * This is an internal API that should never be used by end users.
+   *
+   * The API is as follows:
+   *   - "level" allows to specify the level of forward AD nesting for which the
+   *     gradient should be returned. Note that since levels are not fully
+   *     supported yet, this argument should be 0. See documentation for
+   *     torch::autograd::enter_dual_level for more details about forward AD
+   * nesting.
+   *   - "self" should represent the Tensor whose forward grad is accessed. It
+   * is required when dealing with view.
+   */
+  const at::Tensor& _fw_grad(uint64_t level, const at::TensorBase& self) const;
+
+  /**
+   * Sets the forward gradient for this Tensor.
+   * The given Tensor might not be used directly and its content will be copied.
+   *
+   * This is an internal API that should never be used by end users.
+   *
+   * The API is as follows:
+   *   - "new_grad" is a Tensor containing the new value of the gradient that
+   * should be set
+   *   - "self" should represent the Tensor whose forward grad is accessed. It
+   * is required when dealing with view.
+   *   - "level" allows to specify the level of forward AD nesting for which the
+   *     gradient should be set. Note that since levels are not fully supported
+   *     yet, this argument should be 0. See documentation for
+   * torch::autograd::enter_dual_level for more details about forward AD
+   * nesting.
+   *   - "is_inplace_op" is a boolean flag that tells if this gradient was
+   * generated by an inplace operation or an out of place one. This allows
+   * better error checking.
+   */
+  void _set_fw_grad(
+      const at::TensorBase& new_grad,
+      const at::TensorBase& self,
+      uint64_t level,
+      bool is_inplace_op);
+
+  /**
+   * Return a typed data pointer to the actual data which this tensor refers to.
+   * This checks that the requested type (from the template parameter) matches
+   * the internal type of the tensor.
+   *
+   * It is invalid to call data() on a dtype-uninitialized tensor, even if
+   * the size is 0.
+   *
+   * WARNING: If a tensor is not contiguous, you MUST use strides when
+   * performing index calculations to determine the location of elements in
+   * the tensor.  We recommend using 'TensorAccessor' to handle this computation
+   * for you; this class is available from 'Tensor'.
+   */
+  template <typename T>
+  const T* data_dtype_initialized() const {
+    return data_dtype_initialized_impl<const T>(
+        [this] { return static_cast<const T*>(storage_.data()); });
+  }
+
+  /**
+   * Return a mutable typed data pointer to the actual data which this
+   * tensor refers to. This checks that the requested type (from the
+   * template parameter) matches the internal type of the tensor.
+   *
+   * It is invalid to call data() on a dtype-uninitialized tensor, even if
+   * the size is 0.
+   *
+   * WARNING: If a tensor is not contiguous, you MUST use strides when
+   * performing index calculations to determine the location of elements in
+   * the tensor.  We recommend using 'TensorAccessor' to handle this computation
+   * for you; this class is available from 'Tensor'.
+   */
+  template <typename T>
+  T* mutable_data_dtype_initialized() {
+    return data_dtype_initialized_impl<T>(
+        [this] { return static_cast<T*>(storage_.mutable_data()); });
+  }
+
+ private:
+  // Shared implementation of data_dtype_initialized() and
+  // mutable_data_dtype_initialized().
+  template <typename T, typename Func>
+  T* data_dtype_initialized_impl(const Func& get_data) const {
+    TORCH_CHECK(
+        data_type_.Match<std::remove_const_t<T>>(),
+        "Tensor type mismatch, caller expects elements to be ",
+        caffe2::TypeMeta::TypeName<std::remove_const_t<T>>(),
+        ", while tensor contains ",
+        data_type_.name(),
+        ". ");
+    return data_ptr_impl_impl<T>(get_data);
+  }
+
+ public:
+  /**
+   * More efficient helper for Tensor::data_ptr(). Like data<T>(), but
+   * does not do a type check. Unlike the untemplated data(), does
+   * check has_storage() and storage_initialized().
+   */
+  template <typename T>
+  inline const T* data_ptr_impl() const {
+    return data_ptr_impl_impl<const T>(
+        [this] { return static_cast<const T*>(storage_.data()); });
+  }
+
+  /**
+   * More efficient helper for Tensor::data_ptr(). Like data<T>(), but
+   * does not do a type check. Unlike the untemplated data(), does
+   * check has_storage() and storage_initialized().
+   */
+  template <typename T>
+  inline T* mutable_data_ptr_impl() {
+    return data_ptr_impl_impl<T>(
+        [this] { return static_cast<T*>(storage_.mutable_data()); });
+  }
+
+ private:
+  // Shared implementation of mutable_data_ptr_impl() and the future
+  // mutable_data_ptr_impl().
+  template <typename T, typename Func>
+  __ubsan_ignore_pointer_overflow__ T* data_ptr_impl_impl(
+      const Func& get_data) const {
+    if (C10_UNLIKELY(!has_storage())) {
+      throw_data_ptr_access_error();
+    }
+    TORCH_CHECK(
+        storage_initialized(),
+        "The tensor has a non-zero number of elements, but its data is not allocated yet.\n"
+        "If you're using torch.compile/export/fx, it is likely that we are erroneously "
+        "tracing into a custom kernel. To fix this, please wrap the custom kernel into "
+        "an opaque custom op. Please see the following for details: "
+        "https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html\n"
+        "If you're using Caffe2, Caffe2 uses a lazy allocation, so you will need to call "
+        "mutable_data() or raw_mutable_data() to actually allocate memory.");
+    // Caller does the type check.
+    // Note: storage_offset_ can be non-null even for zero-elements tensors
+    // (for example if created as `torch.empty(5)[10:]`) that triggers
+    // applying non-zero offset to null pointer in UBSan
+    return get_data() + storage_offset_;
+  }
+
+ public:
+  /**
+   * Return a const void* data pointer to the actual data which this
+   * tensor refers to.
+   *
+   * It is invalid to call data() on a dtype-uninitialized tensor, even if the
+   * size is 0.
+   *
+   * WARNING: The data pointed to by this tensor may not contiguous; do NOT
+   * assume that itemsize() * numel() is sufficient to compute the bytes that
+   * can be validly read from this tensor.
+   */
+  inline const void* data() const {
+    return data_impl<const void>(
+        [this] { return static_cast<const char*>(storage_.data()); });
+  }
+
+  /**
+   * Return a void* data pointer to the actual data which this tensor refers to.
+   *
+   * It is invalid to call mutable_data() on a dtype-uninitialized
+   * tensor, even if the size is 0.
+   *
+   * WARNING: The data pointed to by this tensor may not contiguous; do NOT
+   * assume that itemsize() * numel() is sufficient to compute the bytes that
+   * can be validly read from this tensor.
+   */
+  inline void* mutable_data() {
+    return data_impl<void>(
+        [this] { return static_cast<char*>(storage_.mutable_data()); });
+  }
+
+ private:
+  /// Shared implementation of data() and mutable_data().
+  ///
+  /// get_data must return a byte-addressed pointer, e.g. char*,
+  /// std::byte const*, etc.
+  template <typename Void, typename Func>
+  Void* data_impl(const Func& get_data) const {
+    if (C10_UNLIKELY(!has_storage())) {
+      throw_data_ptr_access_error();
+    }
+    TORCH_CHECK(
+        dtype_initialized(),
+        "Cannot access data pointer of Tensor that doesn't have initialized dtype "
+        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
+    auto* data = get_data();
+    static_assert(
+        sizeof(*data) == 1, "get_data must return a byte-addressed pointer.");
+    // Computing an offset into an empty tensor would be UB, since an empty
+    // tensor's storage will be nullptr, and adding a nonzero offset to nullptr
+    // is UB.  So we skip the offset computation in this case.
+    if (is_empty()) {
+      return nullptr;
+    }
+    return data + data_type_.itemsize() * storage_offset_;
+  }
+
+ public:
+  /**
+   * Returns the TypeMeta of a tensor, which describes what data type
+   * it is (e.g., int, float, ...)
+   */
+  const caffe2::TypeMeta dtype() const {
+    return data_type_;
+  }
+
+  /**
+   * Return the size of a single element of this tensor in bytes.
+   */
+  size_t itemsize() const {
+    TORCH_CHECK(
+        dtype_initialized(),
+        "Cannot report itemsize of Tensor that doesn't have initialized dtype "
+        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
+    return data_type_.itemsize();
+  }
+
+  void set_backend_meta(intrusive_ptr<c10::BackendMeta> backend_meta) {
+    get_extra_meta().backend_meta_ = std::move(backend_meta);
+  }
+
+  c10::BackendMeta* get_backend_meta() {
+    if (!extra_meta_) {
+      return nullptr;
+    }
+    return extra_meta_->backend_meta_.get();
+  }
+
+  intrusive_ptr<c10::BackendMeta> get_backend_meta_intrusive_ptr() const {
+    if (!extra_meta_) {
+      return nullptr;
+    }
+    return extra_meta_->backend_meta_;
+  }
+
+  void release_storage_and_set_meta_custom_data_ptr_error_msg_(
+      std::optional<std::string> s) {
+    storage_ = {};
+    set_storage_access_should_throw();
+    get_extra_meta().custom_data_ptr_error_msg_ = s;
+    get_extra_meta().custom_storage_error_msg_ = std::move(s);
+  }
+
+ protected:
+  /**
+   * Returns the human-readable name of the actual type of this object (e.g.,
+   * TensorImpl, BatchedTensorImpl, etc.). Used for error messages.
+   */
+  virtual const char* tensorimpl_type_name() const {
+    return "TensorImpl";
+  }
+
+ private:
+  [[noreturn]] void throw_storage_access_error() const;
+  [[noreturn]] void throw_data_ptr_access_error() const;
+
+  ExtraMeta& get_extra_meta() {
+    if (!extra_meta_) {
+      extra_meta_ = std::make_unique<ExtraMeta>();
+    }
+    return *extra_meta_;
+  }
+
+  c10::SymbolicShapeMeta& symbolic_shape_meta() {
+    TORCH_INTERNAL_ASSERT(extra_meta_ && extra_meta_->symbolic_shape_meta_);
+    return *extra_meta_->symbolic_shape_meta_;
+  }
+
+  const c10::SymbolicShapeMeta& symbolic_shape_meta() const {
+    TORCH_INTERNAL_ASSERT(extra_meta_ && extra_meta_->symbolic_shape_meta_);
+    return *extra_meta_->symbolic_shape_meta_;
+  }
+
+ public:
+  /**
+   * True if a tensor has no elements (e.g., numel() == 0).
+   */
+  inline bool is_empty() const {
+    return numel() == 0;
+  }
+
+  // if we are going to use sym sizes, we should be setting sym strides at the
+  // same time, otherwise it's very easy to misuse this API
+  void set_sizes_and_strides(
+      c10::SymIntArrayRef sizes,
+      c10::SymIntArrayRef strides,
+      std::optional<c10::SymInt> storage_offset = std::nullopt);
+  // This is renamed to avoid breaking overload BC
+  void generic_set_sizes_contiguous(c10::SymIntArrayRef sizes);
+  void generic_set_sizes_contiguous(c10::IntArrayRef sizes) {
+    set_sizes_contiguous(sizes);
+  }
+
+  /**
+   * Change the size at some dimension.  This DOES NOT update strides;
+   * thus, most changes to size will not preserve contiguity.  You probably
+   * also want to call set_stride() when you call this.
+   *
+   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
+   * which is harder to misuse.
+   */
+  virtual void set_size(int64_t dim, int64_t new_size) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_size ",
+        err_msg_tensor_metadata_change_not_allowed);
+    TORCH_CHECK(
+        !matches_policy(SizesStridesPolicy::CustomSizes),
+        "set_size() called on tensor with dynamic shapes or customized size behavior")
+    sizes_and_strides_.size_at(dim) = new_size;
+    refresh_numel();
+    refresh_contiguous();
+  }
+
+  /**
+   * Change the stride at some dimension.
+   *
+   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
+   * which is harder to misuse.
+   */
+  virtual void set_stride(int64_t dim, int64_t new_stride) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_stride ",
+        err_msg_tensor_metadata_change_not_allowed);
+    TORCH_CHECK(
+        !has_symbolic_sizes_strides_,
+        "set_stride() called on tensor with symbolic shape")
+    sizes_and_strides_.stride_at_unchecked(dim) = new_stride;
+    refresh_contiguous();
+  }
+
+  /**
+   * Set the offset into the storage of this tensor.
+   *
+   * WARNING: This does NOT check if the tensor is in bounds for the new
+   * location at the storage; the caller is responsible for checking this
+   * (and resizing if necessary.)
+   */
+  virtual void set_storage_offset(int64_t storage_offset) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_storage_offset ",
+        err_msg_tensor_metadata_change_not_allowed);
+    // TODO: this should probably consult policy
+    TORCH_CHECK(
+        !has_symbolic_sizes_strides_,
+        "set_storage_offset() called on tensor with symbolic shape")
+    storage_offset_ = storage_offset;
+  }
+
+  /**
+   * Like set_sizes_and_strides but assumes contiguous strides.
+   *
+   * WARNING: This function does not check if the requested
+   * sizes/strides are in bounds for the storage that is allocated;
+   * this is the responsibility of the caller
+   */
+  void set_sizes_contiguous(IntArrayRef new_size) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_sizes_contiguous ",
+        err_msg_tensor_metadata_change_not_allowed);
+    TORCH_CHECK(
+        !matches_policy(SizesStridesPolicy::CustomStrides),
+        "tried to directly modify sizes for customized tensor");
+    sizes_and_strides_.set_sizes(new_size);
+
+    refresh_numel();
+    empty_tensor_restride(
+        MemoryFormat::Contiguous); // calls refresh_contiguous()
+  }
+
+  /**
+   * Set the sizes and strides of a tensor.
+   *
+   * WARNING: This function does not check if the requested
+   * sizes/strides are in bounds for the storage that is allocated;
+   * this is the responsibility of the caller
+   */
+  void set_sizes_and_strides(
+      IntArrayRef new_size,
+      IntArrayRef new_stride,
+      std::optional<int64_t> storage_offset = std::nullopt) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_sizes_and_strides ",
+        err_msg_tensor_metadata_change_not_allowed);
+    TORCH_CHECK(
+        !has_symbolic_sizes_strides_,
+        "set_sizes_and_strides() called on tensor with symbolic shape")
+    TORCH_CHECK(
+        new_size.size() == new_stride.size(),
+        "dimensionality of sizes (",
+        new_size.size(),
+        ") must match dimensionality of strides (",
+        new_stride.size(),
+        ")");
+    const auto new_dim = new_size.size();
+    bool overflowed = false;
+    sizes_and_strides_.set_sizes(new_size);
+
+    if (new_dim > 0) {
+      for (size_t dim = new_dim - 1;; dim--) {
+        if (new_stride[dim] >= 0) {
+          sizes_and_strides_.stride_at_unchecked(dim) = new_stride[dim];
+        } else {
+          // XXX: This behavior is surprising and may need to be removed to
+          // support negative strides. Some pytorch functions rely on it:
+          // for example, torch.cat (run TestTorch.test_cat_empty).
+          if (dim == new_dim - 1) {
+            sizes_and_strides_.stride_at_unchecked(dim) = 1;
+          } else {
+            // Keep stride monotonically increasing to match NumPy.
+            overflowed |= c10::mul_overflows(
+                sizes_and_strides_.stride_at_unchecked(dim + 1),
+                std::max<int64_t>(
+                    sizes_and_strides_.size_at_unchecked(dim + 1), 1),
+                std::addressof(sizes_and_strides_.stride_at_unchecked(dim)));
+          }
+        }
+        if (dim == 0)
+          break;
+      }
+      TORCH_CHECK(!overflowed, "Stride calculation overflowed");
+    }
+
+    refresh_numel();
+    refresh_contiguous();
+
+    if (storage_offset.has_value()) {
+      storage_offset_ = *storage_offset;
+    }
+  }
+
+  /**
+   * Set whether a tensor allows changes to its metadata (e.g. sizes / strides /
+   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
+   * ] for details.
+   */
+  void set_allow_tensor_metadata_change(bool value [[maybe_unused]]) {
+    // TODO: at some point, we should kill this field completely.
+    allow_tensor_metadata_change_ = true;
+  }
+
+  /**
+   * True if a tensor allows changes to its metadata (e.g. sizes / strides /
+   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
+   * ] for details.
+   */
+  bool allow_tensor_metadata_change() const {
+    return allow_tensor_metadata_change_;
+  }
+
+  /**
+   * Set the pointer to autograd metadata.
+   */
+  void set_autograd_meta(
+      std::unique_ptr<c10::AutogradMetaInterface> autograd_meta);
+
+  /**
+   * Return the pointer to autograd metadata.  May return nullptr if the
+   * tensor does not track gradients.
+   */
+  c10::AutogradMetaInterface* autograd_meta() const;
+
+  /**
+   * Set the pointer to named tensor metadata.
+   */
+  void set_named_tensor_meta(
+      std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta) {
+    TORCH_WARN_ONCE(
+        "Named tensors and all their associated APIs are an experimental feature ",
+        "and subject to change. Please do not use them for anything important ",
+        "until they are released as stable.");
+#ifdef DEBUG
+    if (named_tensor_meta) {
+      TORCH_INTERNAL_ASSERT(named_tensor_meta->slow_dim() == dim());
+    }
+#endif
+    if (named_tensor_meta) {
+      get_extra_meta().named_tensor_meta_ = std::move(named_tensor_meta);
+      key_set_ = key_set_.add(DispatchKey::Named);
+    } else {
+      if (extra_meta_) {
+        extra_meta_->named_tensor_meta_ = nullptr;
+      }
+      key_set_ = key_set_.remove(DispatchKey::Named);
+    }
+  }
+
+  void set_python_dispatch(bool k) {
+    if (k) {
+      key_set_ = key_set_.add(c10::python_ks);
+    } else {
+      key_set_ = key_set_ - c10::python_ks;
+    }
+  }
+
+  bool is_python_dispatch() const {
+    return key_set_.has_all(c10::python_ks);
+  }
+
+  /**
+   * Return the pointer to named tensor metadata.
+   */
+  const c10::NamedTensorMetaInterface* named_tensor_meta() const {
+    if (!extra_meta_) {
+      return nullptr;
+    }
+    return extra_meta_->named_tensor_meta_.get();
+  }
+
+  c10::NamedTensorMetaInterface* named_tensor_meta() {
+    if (!extra_meta_) {
+      return nullptr;
+    }
+    return extra_meta_->named_tensor_meta_.get();
+  }
+
+  bool has_named_tensor_meta() const {
+    if (!extra_meta_) {
+      return false;
+    }
+    return extra_meta_->named_tensor_meta_ != nullptr;
+  }
+
+  // NOTE [ TensorImpl Shallow-Copying ]
+  //
+  // TensorImpl shallow-copying is used when we want to have two Variables share
+  // the same tensor metadata (e.g. sizes / strides / storage pointer /
+  // storage_offset), but each with a different autograd history. Example call
+  // sites:
+  //
+  // 1. `var_detached = var.detach()` uses `shallow_copy_and_detach()` to create
+  // `var_detached` that shares the same tensor metadata with `var`, but with a
+  // completely new autograd history.
+  // 2. `var.set_data(tensor)` uses `shallow_copy_from()` to copy tensor
+  // metadata from `tensor` into `var`, while keeping `var`'s original
+  // AutogradMeta.
+  //
+  // Functions that shallow-copy a TensorImpl (such as
+  // `shallow_copy_and_detach()` / `shallow_copy_from()` /
+  // `copy_tensor_metadata()`) copy the tensor metadata fields (e.g. sizes /
+  // strides / storage pointer / storage_offset) by value. However, the
+  // following fields are not copied:
+  //
+  // 1. the AutogradMeta pointer, because it is unique for each Variable.
+  // 2. the version counter, because the destination TensorImpl's version
+  // counter is either set to the passed-in `version_counter` (in
+  // `shallow_copy_and_detach()` and `copy_tensor_metadata()`), or it is kept
+  // intact (in `shallow_copy_from()`). See NOTE [ Version Counter Sharing ] for
+  // details.
+  //
+  // In `shallow_copy_and_detach()` and `copy_tensor_metadata()`, the passed-in
+  // `allow_tensor_metadata_change` determines whether the TensorImpl
+  // shallow-copy allows changes to its metadata (e.g. sizes / strides / storage
+  // / storage_offset). See NOTE [ Metadata Change for a Detached Tensor ] for
+  // details.
+  //
+  // In `shallow_copy_from()`, we don't check the destination TensorImpl's
+  // `allow_tensor_metadata_change_`, because `shallow_copy_from()` is used for
+  // implementing functions such as `var.set_data(tensor)`, which changes
+  // `var`'s tensor metadata and expects its `allow_tensor_metadata_change_` to
+  // be ignored.
+
+  /**
+   * One TensorImpl can be copied to another TensorImpl if they have the same
+   * DispatchKeySet. The only two special cases (for legacy reason) are:
+   * CPU is compatible with CUDA and SparseCPU is
+   * compatible with SparseCUDA.
+   */
+  inline bool has_compatible_shallow_copy_type(DispatchKeySet from) {
+    auto is_dense = [](DispatchKeySet ts) {
+      constexpr auto dense_backends = DispatchKeySet(
+          {BackendComponent::CPUBit,
+           BackendComponent::CUDABit,
+           BackendComponent::MPSBit,
+           BackendComponent::HIPBit,
+           BackendComponent::XPUBit,
+           BackendComponent::HPUBit,
+           BackendComponent::MTIABit});
+      constexpr auto dense_k = DispatchKeySet(DispatchKey::Dense);
+      return ts.has_any(dense_k) && ts.has_any(dense_backends);
+    };
+    auto is_sparse = [](DispatchKeySet ts) {
+      constexpr auto sparse_backends = DispatchKeySet(
+          {BackendComponent::CPUBit,
+           BackendComponent::CUDABit,
+           BackendComponent::HIPBit,
+           BackendComponent::XPUBit});
+      constexpr auto sparse_k = DispatchKeySet(DispatchKey::Sparse);
+      return ts.has_any(sparse_k) && ts.has_any(sparse_backends);
+    };
+    auto is_sparse_compressed = [](DispatchKeySet ts) {
+      constexpr auto sparse_compressed_k =
+          DispatchKeySet(DispatchKey::SparseCsr);
+      return ts.has_any(sparse_compressed_k);
+    };
+    return (key_set_ == from) || (is_dense(key_set_) && is_dense(from)) ||
+        (is_sparse(key_set_) && is_sparse(from)) ||
+        (is_sparse_compressed(key_set_) && is_sparse_compressed(from));
+    ;
+  }
+
+ private:
+  template <typename VariableVersion>
+  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach_core(
+      VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change) const;
+
+ public:
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change) const;
+
+  /**
+   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`,
+   * see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
+      c10::VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change) const;
+
+  /**
+   * Shallow-copies data from another TensorImpl into this TensorImpl.
+   *
+   * For why this function doesn't check this TensorImpl's
+   * `allow_tensor_metadata_change_`, see NOTE [ TensorImpl Shallow-Copying ].
+   */
+  virtual void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) {
+    copy_tensor_metadata(
+        /*src_impl=*/impl.get(),
+        /*dest_impl=*/this,
+        /*version_counter=*/version_counter(),
+        /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
+  }
+
+  // Inference tensor doesn't have version counter,
+  // set_version_counter is no-op for them.
+  void set_version_counter(const c10::VariableVersion& version_counter) {
+    TORCH_CHECK(
+        !(is_inference() && version_counter.enabled()),
+        "Cannot set version_counter for inference tensor");
+    version_counter_ = version_counter;
+  }
+
+  void set_version_counter(c10::VariableVersion&& version_counter) {
+    TORCH_CHECK(
+        !(is_inference() && version_counter.enabled()),
+        "Cannot set version_counter for inference tensor");
+    version_counter_ = std::move(version_counter);
+  }
+
+  const c10::VariableVersion& version_counter() const noexcept {
+    return version_counter_;
+  }
+
+  void bump_version() {
+    version_counter_.bump();
+  }
+
+  impl::PyObjectSlot* pyobj_slot() {
+    return &pyobj_slot_;
+  }
+
+  const impl::PyObjectSlot* pyobj_slot() const {
+    return &pyobj_slot_;
+  }
+
+ private:
+  // See NOTE [std::optional operator usage in CUDA]
+  // We probably don't want to expose this publicly until
+  // the note is addressed.
+  std::optional<c10::Device> device_opt() const {
+    return device_opt_;
+  }
+
+ public:
+  /**
+   * The device type of a Tensor, e.g., DeviceType::CPU or DeviceType::CUDA.
+   */
+  DeviceType device_type() const {
+    // TODO: A useful internal assert would be to show that device_opt_ is null
+    // only if you are an undefined tensor
+    TORCH_CHECK(
+        device_opt_.has_value(),
+        "device_type cannot be run on undefined Tensor");
+    // See NOTE [std::optional operator usage in CUDA]
+    return (*device_opt_).type();
+  }
+
+  /**
+   * @brief Extends the outer-most dimension of this tensor by num elements,
+   * preserving the existing data.
+   *
+   * The underlying data may be reallocated in order to accommodate the new
+   * elements, in which case this tensors' capacity is grown at a factor of
+   * growthPct. This ensures that Extend runs on an amortized O(1) time
+   * complexity.
+   *
+   * This op is auto-asynchronous if the underlying device (CUDA) supports it.
+   */
+  void Extend(int64_t num, float growthPct);
+
+  /**
+   * @brief Reserve space for the underlying tensor.
+   *
+   * This must be called after Resize(), since we only specify the first
+   * dimension This does not copy over the old data to the newly allocated space
+   */
+  void ReserveSpace(int64_t outer_dim);
+
+  /**
+   * @brief Resizes a tensor.
+   *
+   * Resize takes in a vector of ints specifying the dimensions of the tensor.
+   * You can pass in an empty vector to specify that it is a scalar (i.e.
+   * containing one single item).
+   *
+   * The underlying storage may be deleted after calling Resize: if the new
+   * shape leads to a different number of items in the tensor, the old memory
+   * is deleted and new memory will be allocated next time you call
+   * mutable_data(). However, if the shape is different but the total number of
+   * items is the same, the underlying storage is kept.
+   *
+   * This method respects caffe2_keep_on_shrink.  Consult the internal logic
+   * of this method to see exactly under what circumstances this flag matters.
+   */
+  template <typename... Ts>
+  void Resize(Ts... dim_source) {
+    bool size_changed = SetDims(dim_source...);
+    if (size_changed) {
+      HandleResize();
+    }
+  }
+
+  template <typename T>
+  void Resize(const std::vector<T>& dim_source) {
+    Resize(ArrayRef<T>(dim_source));
+  }
+
+  /**
+   * Resizes the tensor without touching underlying storage.
+   * This requires the total size of the tensor to remains constant.
+   */
+  void Reshape(const std::vector<int64_t>& dims);
+
+  /**
+   * Release whatever memory the tensor was holding but keep size and type
+   * information. Subsequent call to mutable_data will trigger new memory
+   * allocation.
+   */
+  void FreeMemory();
+
+  /**
+   * @brief Shares the data with another tensor.
+   *
+   * To share data between two tensors, the sizes of the two tensors must be
+   * equal already. The reason we do not implicitly do a Resize to make the two
+   * tensors have the same shape is that we want to allow tensors of different
+   * shapes but the same number of items to still be able to share data. This
+   * allows one to e.g. have a n-dimensional Tensor and a flattened version
+   * sharing the same underlying storage.
+   *
+   * The source tensor should already have its data allocated.
+   */
+  // To be deprecated
+  void ShareData(const TensorImpl& src);
+
+  void ShareExternalPointer(
+      DataPtr&& data_ptr,
+      const caffe2::TypeMeta data_type,
+      size_t size_bytes);
+
+  /**
+   * Returns a mutable raw pointer of the underlying storage. Since we will need
+   * to know the type of the data for allocation, a TypeMeta object is passed in
+   * to specify the necessary information. This is conceptually equivalent of
+   * calling mutable_data<T>() where the TypeMeta parameter meta is derived from
+   * the type T. This function differs from mutable_data<T>() in the sense that
+   * the type T can be specified during runtime via the TypeMeta object.
+   *
+   * If the existing data does not match the desired type, it will be deleted
+   * and a new storage will be created.
+   */
+  inline void* raw_mutable_data(const caffe2::TypeMeta& meta) {
+    // For 0-size tensors it's fine to return any pointer (including nullptr)
+    if (data_type_ == meta && storage_initialized()) {
+      return static_cast<void*>(
+          static_cast<char*>(storage_.mutable_data()) +
+          storage_offset_ * meta.itemsize());
+    } else {
+      bool had_special_dtor = data_type_.placementDelete() != nullptr;
+      storage_offset_ = 0;
+      data_type_ = meta;
+      // NB: device is not changed
+
+      // We can reuse the existing buffer if the current data does not have
+      // a special destructor and the new data doesn't have a special
+      // constructor.
+      if (numel_ == 0 ||
+          (meta.placementNew() == nullptr && !had_special_dtor &&
+           (storage_.nbytes() >= (numel_ * data_type_.itemsize())))) {
+        TORCH_INTERNAL_ASSERT(
+            storage_offset_ == 0); // because we just reallocated
+        return storage_.mutable_data();
+      }
+      Allocator* allocator = storage_.allocator();
+      // Storage might have nullptr allocator in rare cases, for example, if
+      // an external memory segment has been wrapped with Tensor and we don't
+      // know how to reallocate it. However, in order to preserve legacy C2
+      // behavior, we allow reallocating the memory using default allocator.
+      if (allocator == nullptr) {
+        allocator = GetAllocator(storage_.device_type());
+      }
+      if (meta.placementNew()) {
+        // For types that need placement new, we will call it, as well as
+        // making sure that when the data is freed, it calls the right
+        // destruction procedure.
+        auto size = numel_;
+        auto dtor = data_type_.placementDelete();
+        auto data_ptr = allocator->allocate(numel_ * data_type_.itemsize());
+        storage_.set_data_ptr_noswap(PlacementDeleteContext::makeDataPtr(
+            std::move(data_ptr), dtor, size, storage_.device()));
+        data_type_.placementNew()(storage_.mutable_data(), numel_);
+      } else {
+        // For fundamental type, new and delete is easier.
+        storage_.set_data_ptr_noswap(
+            allocator->allocate(numel_ * data_type_.itemsize()));
+      }
+      storage_.set_nbytes(numel_ * data_type_.itemsize());
+      TORCH_INTERNAL_ASSERT(
+          storage_offset_ == 0); // because we just reallocated
+      device_opt_ = storage_.device();
+      return storage_.mutable_data();
+    }
+  }
+
+  /**
+   * Returns a typed pointer of the underlying storage.
+   *
+   * For fundamental types, we reuse possible existing storage if there
+   * is sufficient capacity.
+   */
+  template <typename T>
+  inline T* mutable_data() {
+    if (storage_initialized() && data_type_.Match<T>()) {
+      return static_cast<T*>(storage_.mutable_data()) + storage_offset_;
+    }
+    // Check it here statically - otherwise TypeMeta would throw the runtime
+    // error in attempt to invoke TypeMeta::ctor()
+    static_assert(
+        std::is_default_constructible_v<T>,
+        "Tensor can't hold non-default-constructable types");
+    return static_cast<T*>(raw_mutable_data(caffe2::TypeMeta::Make<T>()));
+  }
+
+  /**
+   * True if a tensor is storage initialized.  A tensor may become
+   * storage UNINITIALIZED after a Resize() or FreeMemory()
+   */
+  bool storage_initialized() const {
+    TORCH_CHECK(
+        has_storage(),
+        "cannot call storage_initialized on tensor that does not have storage");
+    return storage_.data() || numel_ == 0;
+  }
+
+  /**
+   * True if a tensor is dtype initialized.  A tensor allocated with
+   * Caffe2-style constructors is dtype uninitialized until the
+   * first time mutable_data<T>() is called.
+   */
+  bool dtype_initialized() const noexcept {
+    return data_type_ != caffe2::TypeMeta();
+  }
+
+  void set_storage_keep_dtype(at::Storage storage) {
+    TORCH_CHECK(
+        allow_tensor_metadata_change(),
+        "set_storage ",
+        err_msg_tensor_metadata_change_not_allowed);
+    storage_ = std::move(storage);
+    device_opt_ = storage_.device();
+  }
+
+  void set_storage_and_dtype(
+      at::Storage storage,
+      const caffe2::TypeMeta data_type) {
+    set_storage_keep_dtype(std::move(storage));
+    data_type_ = data_type;
+  }
+
+  void empty_tensor_restride_symint(MemoryFormat memory_format);
+
+  /**
+   * Set the strides of the tensor to match memory_format
+   *
+   * WARNING: This function doesn't rearrange data and assumes tensor is a
+   * memory contiguous
+   */
+  void empty_tensor_restride(MemoryFormat memory_format) {
+    if (has_symbolic_sizes_strides_) {
+      empty_tensor_restride_symint(memory_format);
+      return;
+    }
+#ifdef DEBUG
+    TORCH_INTERNAL_ASSERT(
+        compute_numel() == numel_,
+        "If you are seeing this error, that means empty_tensor_restride was "
+        "called before setting correct numel");
+#endif
+    switch (memory_format) {
+      case MemoryFormat::Contiguous: {
+        // dim_ is a virtual call, don't repeat it
+        const auto dim_ = dim();
+        sizes_and_strides_.resize(dim_);
+        if (dim_ > 0) {
+          bool overflowed = false;
+          const auto last_idx = dim_ - 1;
+          sizes_and_strides_.stride_at_unchecked(last_idx) = 1;
+          for (auto i = last_idx - 1; i >= 0; --i) {
+            overflowed |= c10::mul_overflows(
+                sizes_and_strides_.stride_at_unchecked(i + 1),
+                std::max<int64_t>(
+                    sizes_and_strides_.size_at_unchecked(i + 1), 1),
+                std::addressof(sizes_and_strides_.stride_at_unchecked(i)));
+          }
+          TORCH_CHECK(!overflowed, "Stride calculation overflowed");
+        }
+        break;
+      }
+      case MemoryFormat::ChannelsLast: {
+        TORCH_CHECK(
+            dim() == 4, "required rank 4 tensor to use channels_last format");
+        set_sizes_and_strides(sizes(), get_channels_last_strides_2d(sizes()));
+        break;
+      }
+      case MemoryFormat::ChannelsLast3d: {
+        TORCH_CHECK(
+            dim() == 5,
+            "required rank 5 tensor to use channels_last_3d format");
+        set_sizes_and_strides(sizes(), get_channels_last_strides_3d(sizes()));
+        break;
+      }
+      case MemoryFormat::Preserve:
+        TORCH_CHECK(false, "unsupported memory format ", memory_format);
+        // Cleaning warning messages, no need to break as TORCH_CHECK(false)
+        // terminates flow.
+        // break;
+      case MemoryFormat::NumOptions:
+        TORCH_INTERNAL_ASSERT(false, "invalid memory format ", memory_format);
+    }
+    // recompute contiguous flag, as currently NHWC/NCHW flags are not mutually
+    // exclusive see #24090
+    refresh_contiguous();
+  }
+
+  bool is_strides_like(at::MemoryFormat memory_format) const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      return is_strides_like_custom(memory_format);
+    }
+    return is_strides_like_default(memory_format);
+  }
+
+  bool is_strides_like_channels_last() const {
+    return is_strides_like(at::MemoryFormat::ChannelsLast);
+  }
+
+  bool is_strides_like_channels_last_3d() const {
+    return is_strides_like(at::MemoryFormat::ChannelsLast3d);
+  }
+
+  bool is_non_overlapping_and_dense() const {
+    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
+      return is_non_overlapping_and_dense_custom();
+    }
+    return is_non_overlapping_and_dense_default();
+  }
+
+  // if this returns true, then it is guaranteed that this tensor has symbolic
+  // sizes/strides
+  bool has_symbolic_sizes_strides() const {
+    return has_symbolic_sizes_strides_;
+  }
+
+ private:
+  void HandleResize();
+
+  // The Caffe2 Resize() method supports being called both as Resize({2,2}) as
+  // well as variadic with Resize(2, 2).  These overloads provide all of the
+  // supported calling configurations, while being overloads (and not templates)
+  // so that implicit conversions still work.
+  //
+  // SetDims on ArrayRef is internally implemented as a template, so we can
+  // handle both ArrayRefs of different types (there are some uses of
+  // Resize in Caffe2 which pass in int, not int64_t.)
+
+  template <
+      typename T,
+      typename = typename std::enable_if_t<std::is_integral_v<T>>>
+  bool SetDimsTemplate(ArrayRef<T> src) {
+    TORCH_CHECK(
+        !has_symbolic_sizes_strides_,
+        "SetDims() called on tensor with symbolic shape")
+
+    auto old_numel = numel_;
+    sizes_and_strides_.resize(src.size());
+    int64_t new_numel = 1;
+    for (const auto i : c10::irange(src.size())) {
+      new_numel *= src[i];
+      sizes_and_strides_.size_at_unchecked(i) = src[i];
+    }
+    numel_ = new_numel;
+    empty_tensor_restride(MemoryFormat::Contiguous);
+    return numel_ != old_numel;
+  }
+
+  bool SetDims(ArrayRef<int64_t> s) {
+    return SetDimsTemplate(s);
+  }
+
+  bool SetDims(ArrayRef<int> s) {
+    return SetDimsTemplate(s);
+  }
+
+  bool SetDims(ArrayRef<size_t> s) {
+    return SetDimsTemplate(s);
+  }
+
+  bool SetDims() {
+    return SetDims(IntArrayRef{});
+  }
+
+  bool SetDims(const int64_t d0) {
+    return SetDims(IntArrayRef{d0});
+  }
+
+  bool SetDims(const int64_t d0, const int64_t d1) {
+    return SetDims(IntArrayRef{d0, d1});
+  }
+
+  bool SetDims(const int64_t d0, const int64_t d1, const int64_t d2) {
+    return SetDims(IntArrayRef{d0, d1, d2});
+  }
+
+  bool SetDims(
+      const int64_t d0,
+      const int64_t d1,
+      const int64_t d2,
+      const int64_t d3) {
+    return SetDims(IntArrayRef{d0, d1, d2, d3});
+  }
+
+  /**
+   * Compute the number of elements based on the sizes of a tensor.
+   */
+  // NB: This is ONLY called when sizes_and_strides_ is used directly; if
+  // we are virtualizing, then numel calls are virtualized as well, and this
+  // should never get called
+  int64_t compute_numel() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
+#if C10_HAS_BUILTIN_OVERFLOW() && !defined(C10_MOBILE)
+    // Use overflow checks if supported by the compiler
+    return safe_compute_numel();
+#else
+    return c10::multiply_integers(sizes_and_strides_.sizes_arrayref());
+#endif
+  }
+
+  /**
+   * Compute the number of elements based on the sizes of a
+   * tensor. Catches integer overflow that may occur when a tensor
+   * using a sparse layout has multiple dimensions with large sizes.
+   */
+  int64_t safe_compute_numel() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
+    uint64_t n = 1;
+    bool overflows =
+        c10::safe_multiplies_u64(sizes_and_strides_.sizes_arrayref(), &n);
+    constexpr auto numel_max = std::min(
+        static_cast<uint64_t>(std::numeric_limits<int64_t>::max()),
+        static_cast<uint64_t>(std::numeric_limits<size_t>::max()));
+
+    overflows |= (n > numel_max);
+    TORCH_CHECK(!overflows, "numel: integer multiplication overflow");
+    return static_cast<int64_t>(n);
+  }
+
+  /**
+   * Compute whether or not a tensor is contiguous based on the sizes and
+   * strides of a tensor.
+   */
+  bool compute_contiguous(identity<bool>) const;
+
+  bool compute_channels_last_contiguous_2d(identity<bool>) const;
+
+  bool compute_channels_last_contiguous_3d(identity<bool>) const;
+
+  bool compute_strides_like_channels_last_2d(identity<bool>) const;
+
+  bool compute_strides_like_channels_last_3d(identity<bool>) const;
+
+  bool compute_non_overlapping_and_dense(identity<bool>) const;
+
+ protected:
+  /**
+   * Recompute the cached numel of a tensor.  Call this if you modify
+   * sizes.
+   *
+   * For tensors with sparse layouts, use safe_refresh_numel() instead
+   * because it will catch integer overflow that may occur for tensors
+   * with sparse layouts and large dimensions.
+   *
+   * NB: We may uselessly recompute cached numel even in situations where
+   * it is completely never used (e.g., if CustomSizes for Python).  However,
+   * we still must keep it up to date in case the Python overload
+   * returns None (in which case we will consult the field here).  This also
+   * implies that sizes/strides will never be complete garbage; in the
+   * very worst case scenario, it will reflect a 1-dim zero size tensor.
+   */
+  void refresh_numel() {
+    if (has_symbolic_sizes_strides_) {
+      symbolic_shape_meta().refresh_numel();
+    } else {
+      numel_ = compute_numel();
+    }
+  }
+
+  /**
+   * Recompute the cached numel of a tensor.  Call this if you modify
+   * sizes. Use only for tensors with sparse layouts because only
+   * sparse tensor are likely to have sizes that may lead to integer
+   * overflow when computing numel.
+   */
+  void safe_refresh_numel() {
+    if (has_symbolic_sizes_strides_) {
+      // NB: sym numel is done with symbolic integers, which handle overflow
+      // checking
+      symbolic_shape_meta().refresh_numel();
+    } else {
+      numel_ = safe_compute_numel();
+    }
+  }
+
+ private:
+  // NB: the TypeId argument prevents confusion where you pass a true/false
+  // literal and pick the wrong overload
+
+  void _set_is_contiguous(identity<bool>, bool b) {
+    is_contiguous_ = b;
+  }
+
+  void _set_is_channels_last_contiguous(identity<bool>, bool b) {
+    is_channels_last_contiguous_ = b;
+  }
+
+  void _set_is_channels_last_3d_contiguous(identity<bool>, bool b) {
+    is_channels_last_3d_contiguous_ = b;
+  }
+
+  void _set_is_channels_last(identity<bool>, bool b) {
+    is_channels_last_ = b;
+  }
+
+  void _set_is_channels_last_3d(identity<bool>, bool b) {
+    is_channels_last_3d_ = b;
+  }
+
+  void _set_is_non_overlapping_and_dense(identity<bool>, bool b) {
+    is_non_overlapping_and_dense_ = b;
+  }
+
+  // These are little wrappers over the real compute_ functions that
+  // can make use of other contiguity fields to short circuit.
+
+  bool compute_is_non_overlapping_and_dense_dim4(identity<bool> type_id) {
+    return is_contiguous_ || is_channels_last_contiguous_ ||
+        compute_non_overlapping_and_dense(type_id);
+  }
+
+  bool compute_channels_last_contiguous_3d_dim5(identity<bool> type_id) {
+    return !is_channels_last_contiguous_ &&
+        compute_channels_last_contiguous_3d(type_id);
+  }
+
+  bool compute_channels_last_2d_dim5(identity<bool> type_id) {
+    return !is_channels_last_3d_contiguous_ &&
+        compute_strides_like_channels_last_2d(type_id);
+  }
+
+  bool compute_channels_last_3d_dim5(identity<bool> type_id) {
+    return !is_channels_last_ && compute_strides_like_channels_last_3d(type_id);
+  }
+
+  bool compute_is_non_overlapping_and_dense_dim5(identity<bool> type_id) {
+    return is_contiguous_ || is_channels_last_contiguous_ ||
+        is_channels_last_3d_contiguous_ ||
+        compute_non_overlapping_and_dense(type_id);
+  }
+
+  bool compute_is_non_overlapping_and_dense_anydim(identity<bool> type_id) {
+    return is_contiguous_ || compute_non_overlapping_and_dense(type_id);
+  }
+
+  template <typename T>
+  void _refresh_contiguous() {
+    auto type_id = identity<T>();
+    // Note:
+    // Dim 0, 1, 2 will never be a channels last 2d/3d format
+    // Dim 3+ is possibly be a channels last 2d format (Dim 4 only at this
+    // point) Dim 4+ is possibly be a channels last 3d format (Dim 5 only at
+    // this point)
+    switch (dim()) {
+      case 4: {
+        _set_is_contiguous(type_id, compute_contiguous(type_id));
+        _set_is_channels_last_contiguous(
+            type_id, compute_channels_last_contiguous_2d(type_id));
+        _set_is_channels_last_3d_contiguous(type_id, false);
+        _set_is_channels_last(
+            type_id, compute_strides_like_channels_last_2d(type_id));
+        _set_is_channels_last_3d(type_id, false);
+        _set_is_non_overlapping_and_dense(
+            type_id, compute_is_non_overlapping_and_dense_dim4(type_id));
+        break;
+      }
+      case 5: {
+        _set_is_contiguous(type_id, compute_contiguous(type_id));
+        _set_is_channels_last_contiguous(
+            type_id, compute_channels_last_contiguous_2d(type_id));
+        _set_is_channels_last_3d_contiguous(
+            type_id, compute_channels_last_contiguous_3d_dim5(type_id));
+        _set_is_channels_last(type_id, compute_channels_last_2d_dim5(type_id));
+        _set_is_channels_last_3d(
+            type_id, compute_channels_last_3d_dim5(type_id));
+        _set_is_non_overlapping_and_dense(
+            type_id, compute_is_non_overlapping_and_dense_dim5(type_id));
+        break;
+      }
+      default:
+        // is_channels_last_ and is_channels_last_3d_ are suggested
+        // memory_format. Being channels_last_contiguous doesn't necessarily
+        // mean the tensor is strided like channels_last: for strides on channel
+        // dimension could suggest desired memory_layout, but it doesn't affect
+        // memory storage
+        _set_is_contiguous(type_id, compute_contiguous(type_id));
+        _set_is_channels_last_contiguous(type_id, false);
+        _set_is_channels_last_3d_contiguous(type_id, false);
+        _set_is_channels_last(type_id, false);
+        _set_is_channels_last_3d(type_id, false);
+        _set_is_non_overlapping_and_dense(
+            type_id, compute_is_non_overlapping_and_dense_anydim(type_id));
+        break;
+    }
+  }
+
+ protected:
+  /**
+   * Recompute the cached contiguity of a tensor.  Call this if you modify sizes
+   * or strides.
+   */
+  void refresh_contiguous() {
+    if (has_symbolic_sizes_strides_) {
+      symbolic_shape_meta().refresh_contiguous();
+    } else {
+      _refresh_contiguous<bool>();
+    }
+  }
+
+  /**
+   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
+   * storage_offset) from one TensorImpl to another TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
+   * [ TensorImpl Shallow-Copying ].
+   */
+  static void copy_tensor_metadata(
+      const TensorImpl* src_impl,
+      TensorImpl* dest_impl,
+      const c10::VariableVersion& version_counter,
+      bool allow_tensor_metadata_change);
+
+  /**
+   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
+   * storage_offset) from one TensorImpl to another TensorImpl.
+   *
+   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
+   * [ TensorImpl Shallow-Copying ].
+   */
+  static void copy_tensor_metadata(
+      const TensorImpl* src_impl,
+      TensorImpl* dest_impl,
+      c10::VariableVersion&& version_counter,
+      bool allow_tensor_metadata_change);
+
+ private:
+  static void copy_tensor_metadata_except_version_counter(
+      const TensorImpl* src_impl,
+      TensorImpl* dest_impl,
+      bool allow_tensor_metadata_change);
+
+ protected:
+  // Error message to show when the user tries to change tensor metadata on
+  // Tensor created from .data or .detach().
+  //
+  // See NOTE [ Metadata Change for a Detached Tensor ] for details.
+  static const char* const err_msg_tensor_metadata_change_not_allowed;
+
+  static void copy_generic_tensor_metadata(
+      const TensorImpl* src_impl,
+      TensorImpl* dest_impl);
+
+ public:
+  void set_storage_access_should_throw() {
+    storage_access_should_throw_ = true;
+  }
+
+ public:
+  void set_custom_sizes_strides(SizesStridesPolicy policy) {
+    custom_sizes_strides_ = static_cast<uint8_t>(policy);
+    refresh_sizes_strides_policy();
+  }
+
+  void set_python_custom_sizes_strides(SizesStridesPolicy policy) {
+    python_custom_sizes_strides_ = static_cast<uint8_t>(policy);
+    refresh_sizes_strides_policy();
+  }
+
+  void set_custom_device(bool custom_device) {
+    custom_device_ = custom_device;
+    refresh_device_policy();
+  }
+
+  void set_custom_layout(bool custom_layout) {
+    custom_layout_ = custom_layout;
+    refresh_layout_policy();
+  }
+
+  void set_python_custom_device(bool custom_device) {
+    python_custom_device_ = custom_device;
+    refresh_device_policy();
+  }
+
+  void set_python_custom_layout(bool custom_layout) {
+    python_custom_layout_ = custom_layout;
+    refresh_layout_policy();
+  }
+
+ protected:
+  void refresh_sizes_strides_policy() {
+    if (has_symbolic_sizes_strides_) {
+      sizes_strides_policy_ =
+          static_cast<uint8_t>(SizesStridesPolicy::CustomSizes);
+    } else {
+      sizes_strides_policy_ =
+          std::max(custom_sizes_strides_, python_custom_sizes_strides_);
+    }
+  }
+
+  void refresh_device_policy() {
+    device_policy_ = custom_device_ || python_custom_device_;
+  }
+
+  void refresh_layout_policy() {
+    layout_policy_ = custom_layout_ || python_custom_layout_;
+  }
+
+ protected:
+  Storage storage_;
+
+ private:
+  // This pointer points to an AutogradMeta struct that stores autograd-specific
+  // fields (such as grad_ / grad_fn_ / grad_accumulator_). This pointer always
+  // has unique ownership (meaning only one TensorImpl can own it at a time).
+  //
+  // autograd_meta_ can be nullptr, as an optimization.  When this occurs, it is
+  // equivalent to having an autograd_meta_ pointing to a default constructed
+  // AutogradMeta; intuitively, tensors which don't require grad will have this
+  // field set to null.
+  //
+  // This means accessors on autograd_meta_ have to be careful to test if they
+  // got a nullptr, and handle default behavior appropriately in that case.
+  //
+  // Note that we don't enforce the invariant that if the AutogradMeta is
+  // default constructed, it is nullptr (to do this, we'd have to continuously
+  // check if an AutogradMeta became, by mutation, equal to the default
+  // constructed form.  (This might be useful, but it seems rare enough that
+  // a requires_grad=True variable will turn back into the requires_grad=False
+  // version.)  So there are three representable states:
+  //
+  //    1. autograd_meta_ == nullptr
+  //    2. autograd_meta_ is default constructed (semantically, same as (1))
+  //    3. autograd_meta_ has nontrivial information content
+  //
+  std::unique_ptr<c10::AutogradMetaInterface> autograd_meta_ = nullptr;
+
+ protected:
+  std::unique_ptr<c10::ExtraMeta> extra_meta_ = nullptr;
+
+  c10::VariableVersion version_counter_;
+
+  impl::PyObjectSlot pyobj_slot_;
+
+  c10::impl::SizesAndStrides sizes_and_strides_;
+
+  int64_t storage_offset_ = 0;
+  // If sizes and strides are empty, the numel is 1!!  However, most of the
+  // time, we will immediately set sizes to {0} and reset numel to 0.
+  // (Can't do that in the default initializers, because there's no way to
+  // spell "allocate a one-element array" for strides_).
+  int64_t numel_ = 1;
+
+  // INVARIANT: When storage is non-null, this type meta must
+  // agree with the type meta in storage
+  caffe2::TypeMeta data_type_;
+
+  // NOTE [std::optional operator usage in CUDA]
+  // Our optional definition doesn't compile in .cu file if `value()` or
+  // `operator->` are used.  Instead, we always use `operator*`.
+  // See https://github.com/pytorch/pytorch/issues/18496 for more info.
+  // If this is too burdensome to maintain, we can just
+  // manually implement this with an additional bool.
+
+  // INVARIANT: When storage is non-null, this Device must
+  // agree with the type meta in storage.
+  //
+  // INVARIANT: device_opt_ is only nullopt for undefined tensors
+  // (which do not have a device.)
+  std::optional<c10::Device> device_opt_;
+
+  // default member initializers for bit-fields only available with -std=c++2a
+  // or -std=gnu++2a
+  inline void init_bitfields() {
+    is_contiguous_ = true;
+    is_channels_last_ = false;
+    is_channels_last_contiguous_ = false;
+    is_channels_last_3d_ = false;
+    is_channels_last_3d_contiguous_ = false;
+    is_non_overlapping_and_dense_ = true;
+    is_wrapped_number_ = false;
+    allow_tensor_metadata_change_ = true;
+    reserved_ = false;
+    sizes_strides_policy_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
+    custom_sizes_strides_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
+    python_custom_sizes_strides_ =
+        static_cast<uint8_t>(SizesStridesPolicy::Default);
+    python_custom_device_ = false;
+    python_custom_layout_ = false;
+    custom_device_ = false;
+    custom_layout_ = false;
+    device_policy_ = false;
+    layout_policy_ = false;
+    storage_access_should_throw_ = false;
+    has_symbolic_sizes_strides_ = false;
+  }
+
+  // Tensor is contiguous
+  bool is_contiguous_ : 1;
+
+  // Tensor is a subclass that does not permit storage access.
+  bool storage_access_should_throw_ : 1;
+
+  // Tensor is stored in the channels last 2d memory format, when dimensions
+  // order is (N)CHW and C-strides < W-strides < H-strides (< N-strides)
+  // (If size of any dimension is equal to 1, this dimension strides value
+  // is not taken into account).
+  bool is_channels_last_ : 1;
+
+  // Channels last contiguous tensor is channel last tensor which occupies
+  // contiguous memory block.
+  bool is_channels_last_contiguous_ : 1;
+
+  // Tensor is stored in the channels last 3d memory format, when dimensions
+  // order is (N)CDHW and C-strides < W-strides < H-strides < D - strides (<
+  // N-strides) (If size of any dimension is equal to 1, this dimension strides
+  // value is not taken into account).
+  bool is_channels_last_3d_ : 1;
+
+  // Channels last 3d contiguous tensor is channel last 3d tensor which occupies
+  // contiguous memory block.
+  bool is_channels_last_3d_contiguous_ : 1;
+
+  // Dense tensor is the tensor that store values in a contiguous block of
+  // memory. Non-overlapping tensor is the tensor in which elements occupy
+  // individual non-repetitive memory.
+  bool is_non_overlapping_and_dense_ : 1;
+
+  bool is_wrapped_number_ : 1;
+
+  // NOTE [ Metadata Change for a Detached Tensor ]
+  //
+  // Normally, a user is allowed to change the tensor metadata
+  // (e.g. sizes / strides / storage / storage_offset) of a tensor.
+  // However, if the tensor is created by `t1_detached = t1.data` in Python
+  // or `t1_detached = t1.detach()` in Python/C++, those changes to the
+  // tensor metadata of `t1_detached` will not be propagated back to the
+  // original tensor `t1`. In order to make such changes explicitly illegal,
+  // we created the `allow_tensor_metadata_change_` flag, to prevent users
+  // from changing metadata of the detached tensor and expecting the original
+  // tensor to also be updated.
+  //
+  // NOTE: For a full list of tensor metadata fields, please see
+  // `copy_tensor_metadata()` in TensorImpl and its subclasses to find
+  // which fields are copied by value.
+  bool allow_tensor_metadata_change_ : 1;
+
+  // we decide to keep reserved_ and it will
+  // live in Tensor after the split
+  // The logic is that if Extend() or ReserveSpace() were ever called,
+  // then subsequent Resize()s will not free up Storage.
+  bool reserved_ : 1;
+
+  // Call _custom() virtual methods for
+  // strides()/is_contiguous()/sizes()/dim()/numel()
+  // This is a combination of sizes_strides_custom_dispatch_
+  // and has_symbolic_sizes_strides_
+  uint8_t sizes_strides_policy_ : 2;
+
+  // Whether or not sizes_and_strides_ contains a symbolic value.
+  bool has_symbolic_sizes_strides_ : 1;
+
+  // Call _custom() virtual method for
+  // strides()/is_contiguous()/sizes()/dim()/numel()
+  uint8_t custom_sizes_strides_ : 2;
+
+  // Combo of custom_ and python_custom_
+  bool device_policy_ : 1;
+  bool layout_policy_ : 1;
+
+  // Call _custom() virtual method for device()
+  bool custom_device_ : 1;
+
+  // Call _custom() virtual method for layout()
+  bool custom_layout_ : 1;
+
+  // Call into Python for
+  // strides()/is_contiguous()/sizes()/dim()/numel()
+  uint8_t python_custom_sizes_strides_ : 2;
+
+  // Call into Python for device()
+  bool python_custom_device_ : 1;
+
+  // Call into Python for layout()
+  bool python_custom_layout_ : 1;
+
+  // The set of DispatchKeys which describe this tensor.  NB: this
+  // does NOT include Autograd (historically, it did, but
+  // not anymore!)
+  //
+  // INVARIANT: extra_meta_->named_tensor_meta_ != nullptr  <==>
+  // key_set_.has(DispatchKey::Named)
+  DispatchKeySet key_set_;
+
+ private:
+  // C10_TensorImpl_Size_Check_Dummy_Class needs to be friends with
+  // TensorImpl so it can inspect the size of private fields
+  template <
+      size_t cplusplus,
+      size_t clang_ver_major,
+      size_t gcc_ver,
+      size_t gcc_ver_minor,
+      size_t nvcc,
+      size_t cuda_version,
+      size_t cuda_version_major,
+      size_t ptr_size>
+  friend class C10_TensorImpl_Size_Check_Dummy_Class;
+};
+
+// Note [TensorImpl size constraints]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Changed the size of TensorImpl?  If the size went down, good for
+// you!  Adjust the documentation below and the expected size.
+// Did it go up?  Read on...
+//
+// Struct size matters.  In some production systems at Facebook, we have
+// 400M live tensors during a training run.  Do the math: every 64-bit
+// word you add to Tensor is an extra 3.2 gigabytes in RAM.
+//
+// If you are a Facebook employee, you can check if the run in question
+// has tipped you over the point using the command here:
+// https://fburl.com/q5enpv98
+//
+// For reference, we OOMed at 160 bytes (20 words) per TensorImpl.
+// This is not counting overhead from strides out-of-line allocation and
+// StorageImpl space and this is from before we inlined sizes and strides
+// directly into TensorImpl as SmallVectors.
+//
+// Our memory usage on 32-bit systems is suboptimal, but we're not checking
+// for it at the moment (to help avoid rage inducing cycles when the
+// 32-bit number is wrong).
+//
+// Current breakdown:
+//
+//    vtable pointer
+//    strong refcount           TODO: pack these into one word
+//    weak refcount
+//    storage pointer
+//    autograd metadata pointer
+//    named tensor metadata pointer
+//    version counter pointer
+//    PyObjectSlot
+//    SizesAndStrides size/pointer
+//    SizesAndStrides sizes (pre-allocated 0)
+//    SizesAndStrides sizes (pre-allocated 1)
+//    SizesAndStrides sizes (pre-allocated 2)
+//    SizesAndStrides sizes (pre-allocated 3)
+//    SizesAndStrides sizes (pre-allocated 4)
+//    SizesAndStrides strides (pre-allocated 0)
+//    SizesAndStrides strides (pre-allocated 1)
+//    SizesAndStrides strides (pre-allocated 2)
+//    SizesAndStrides strides (pre-allocated 3)
+//    SizesAndStrides strides (pre-allocated 4)
+//    storage offset
+//    numel
+//    data type, device, is_contiguous, storage_access_should_throw_, bitfields
+//    DispatchKeySet
+//
+
+// Various preprocessor macros we use to check that the
+// TensorImpl size hasn't changed unexpectedly. We undef
+// these later.
+#ifndef __NVCC__
+#define C10_NVCC 0
+#else
+#define C10_NVCC __NVCC__
+#endif
+
+#ifndef __CUDA_VER_MAJOR__
+#define C10_CUDA_VERSION_MAJOR 0
+#else
+#define C10_CUDA_VERSION_MAJOR __CUDA_VER_MAJOR__
+#endif
+
+#ifndef CUDA_VERSION
+#define C10_CUDA_VERSION 0
+#else
+#define C10_CUDA_VERSION CUDA_VERSION
+#endif
+
+#ifndef __clang_major__
+#define C10_CLANG_MAJOR_VERSION 0
+#else
+#define C10_CLANG_MAJOR_VERSION __clang_major__
+#endif
+
+#ifndef __GNUC__
+#define C10_GCC_VERSION 0
+#else
+#define C10_GCC_VERSION __GNUC__
+#endif
+
+#ifndef __GNUC_MINOR__
+#define C10_GCC_VERSION_MINOR 0
+#else
+#define C10_GCC_VERSION_MINOR __GNUC_MINOR__
+#endif
+
+// We use a templatized class to both contain the logic of checking the sizes
+// as well as to provide compile-time information that might be useful in
+// figuring out why sizes may have changed.
+// All the compile time information is given by the template fields that are
+// always printed by the compiler when the static_assert fails.
+template <
+    size_t cplusplus = __cplusplus,
+    size_t clang_ver_major = C10_CLANG_MAJOR_VERSION,
+    size_t gcc_ver = C10_GCC_VERSION,
+    size_t gcc_ver_minor = C10_GCC_VERSION_MINOR,
+    size_t nvcc = C10_NVCC,
+    size_t cuda_version = C10_CUDA_VERSION,
+    size_t cuda_version_major = C10_CUDA_VERSION_MAJOR,
+    size_t ptr_size = sizeof(void*)>
+class C10_TensorImpl_Size_Check_Dummy_Class : private TensorImpl {
+  // Names of (non-bitfield) fields in TensorImpl; used to provide
+  // compile-time info about fields whose size changes unexpectedly.
+  enum class FieldNameEnum {
+    storage_,
+    autograd_meta_,
+    extra_meta_,
+    version_counter_,
+    pyobj_slot_,
+    sizes_and_strides_,
+    storage_offset_,
+    numel_,
+    data_type_,
+    device_opt_,
+    key_set_,
+    TOTAL_SIZE
+  };
+
+  // Provides compile-time equality check that reveals what numbers
+  // were used and on which quantity
+  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
+  constexpr static bool are_equal() {
+    static_assert(
+        Actual == Expected,
+        "Actual and Expected sizes of a field did not match!");
+    return true;
+  }
+
+  // Provides compile-time <= check that reveals what numbers
+  // were used and on which quantity
+  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
+  constexpr static bool is_le() {
+    static_assert(
+        Actual <= Expected,
+        "Actual and Expected sizes of a field did not match!");
+    return true;
+  }
+
+ public:
+  // Compile-time check that TensorImpl field sizes are as expected
+  //
+  // Observed total sizes and associated versions
+  // If you find a flag that predicts when unique_ptr has 16 bytes
+  // on 64-bit systems or when sizes_and_strides_ is 84 vs 88 bytes
+  // on 32-bit systems you get a cookie!
+  // Length | LLVM | GCC  |    C++ |  CUDA
+  //    192 |    ? | 11.2 | 201703 | 11040
+  //    208 |    ? | 11.2 | 201703 | 11040
+  //    208 |    ? | 11.2 | 201402 | 11040
+  //    192 |    ? | 11.2 | 201402 | 11040
+  //    160 |   12 |  4.2 | 201703 |     0
+  //
+  // To keep things clean, we split on systems here.
+
+#if UINTPTR_MAX == 0xFFFFFFFF
+  // This is a 32-bit system
+  static constexpr bool check_sizes() {
+    constexpr size_t tsize = 20 * sizeof(int64_t);
+
+    // clang-format off
+    are_equal<sizeof(storage_),            4,  FieldNameEnum::storage_>();
+    are_equal<sizeof(autograd_meta_),      4,  FieldNameEnum::autograd_meta_>();
+    are_equal<sizeof(extra_meta_),         4,  FieldNameEnum::extra_meta_>();
+    are_equal<sizeof(version_counter_),    4,  FieldNameEnum::version_counter_>();
+    are_equal<sizeof(pyobj_slot_),    8,  FieldNameEnum::pyobj_slot_>();
+    is_le<sizeof(sizes_and_strides_),     88, FieldNameEnum::sizes_and_strides_>();
+    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
+    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
+    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
+    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
+    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
+    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
+    // clang-format on
+
+    return true;
+  }
+#else
+  // This is a 64-bit system
+  static constexpr bool check_sizes() {
+    constexpr size_t tsize = 26 * sizeof(int64_t);
+
+    // clang-format off
+    are_equal<sizeof(storage_),            8,  FieldNameEnum::storage_>();
+    // On some systems involving NVCC the size of unique_ptr is 16 bytes. We haven't
+    // figured out how to detect those via macro preprocessors yet, so we use <=
+    // comparisons for the relevant fields.
+    is_le<sizeof(autograd_meta_),         16,  FieldNameEnum::autograd_meta_>();
+    is_le<sizeof(extra_meta_),            16,  FieldNameEnum::extra_meta_>();
+    are_equal<sizeof(version_counter_),    8,  FieldNameEnum::version_counter_>();
+    are_equal<sizeof(pyobj_slot_),   16,  FieldNameEnum::pyobj_slot_>();
+    are_equal<sizeof(sizes_and_strides_), 88,  FieldNameEnum::sizes_and_strides_>();
+    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
+    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
+    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
+    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
+    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
+    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
+    // clang-format on
+
+    return true;
+  }
+#endif
+};
+
+// We use a class to encapsulate size-checking logic with
+// templates to capture sizes and flags. We call this within
+// a static assert to prove there is no run-time behaviour.
+// Since the methods we call return either true or fail their
+// own static_asserts, we should never see the error messages
+// below. We have to provide it though for c++ <17.
+static_assert(
+    C10_TensorImpl_Size_Check_Dummy_Class<>::check_sizes(),
+    "You should not see this message.");
+
+// Clean up after ourselves
+#undef C10_NVCC
+#undef C10_CUDA_VERSION_MAJOR
+#undef C10_CUDA_VERSION
+#undef C10_CLANG_MAJOR_VERSION
+#undef C10_GCC_VERSION
+#undef C10_GCC_VERSION_MINOR
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/TensorOptions.h b/lib/python3.11/site-packages/torch/include/c10/core/TensorOptions.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5c2f98569b1df60c234833c4466ca544db92294
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/TensorOptions.h
@@ -0,0 +1,787 @@
+#pragma once
+
+#include <c10/core/Backend.h>
+#include <c10/core/DefaultDtype.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/Layout.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/ScalarTypeToTypeMeta.h>
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <optional>
+
+#include <cstdint>
+#include <iosfwd>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+DispatchKey computeDispatchKey(
+    std::optional<ScalarType> dtype,
+    std::optional<Layout> layout,
+    std::optional<Device> device);
+
+inline ScalarType dtype_or_default(std::optional<ScalarType> dtype) {
+  return dtype.value_or(get_default_dtype_as_scalartype());
+}
+
+inline caffe2::TypeMeta dtype_or_default(
+    std::optional<caffe2::TypeMeta> dtype) {
+  return dtype.value_or(get_default_dtype());
+}
+
+inline Layout layout_or_default(std::optional<Layout> layout) {
+  return layout.value_or(kStrided);
+}
+
+inline Device device_or_default(std::optional<Device> device) {
+  return device.value_or(Device(kCPU));
+}
+
+inline bool pinned_memory_or_default(std::optional<bool> pinned_memory) {
+  return pinned_memory.value_or(false);
+}
+
+/// A class to encapsulate construction axes of an Tensor.  TensorOptions was
+/// designed to support the Python style API for specifying construction options
+/// on factory functions, e.g.,
+///
+///     torch.zeros(2, 3, dtype=torch.int32)
+///
+/// Because C++ doesn't natively support keyword arguments, there must be
+/// another way of specifying keyword-like arguments.  TensorOptions is a
+/// builder class which can be used to construct this "dictionary" of keyword
+/// arguments: functions which support TensorOptions conventionally take this
+/// argument optionally as their last argument.
+///
+/// WARNING: In PyTorch, there are `torch::` variants of factory functions,
+/// e.g., torch::zeros for at::zeros.  These return Variables (while the
+/// stock ATen functions return plain Tensors).  If you mix these functions
+/// up, you WILL BE SAD.
+///
+/// Rather than use the constructor of this class directly, you should prefer to
+/// use the constructor functions, and then chain setter methods on top of them.
+///
+///     at::device(at::kCUDA).dtype(kInt)
+///     at::dtype(at::kInt)
+///
+/// Additionally, anywhere a TensorOptions is expected, you can directly
+/// pass at::kCUDA / at::kInt, and it will implicitly convert to a
+/// TensorOptions.
+///
+/// Here are some recommended ways to create a 2x2 tensor of zeros
+/// with certain properties.  These all *implicitly* make use of
+/// TensorOptions, even if they don't mention the class explicitly:
+///
+///     at::zeros({2,2}, at::kCUDA);
+///     at::zeros({2,2}, at::kLong);
+///     at::zeros({2,2}, at::device(at::kCUDA).dtype(at::kLong()));
+///     at::zeros({2,2}, at::device({at::kCUDA, 1})); // place on device 1
+///     at::zeros({2,2}, at::requires_grad());
+///
+
+/// NOTE [ TensorOptions Constructors ]
+///
+/// TensorOptions is like a dictionary with entries from the set:
+/// {requires_grad, device, dtype, layout}, where each entry may be
+/// unspecified (i.e., is optional). It is used to specify the properties of
+/// tensors in many places both in C++ internal and API, e.g., tensor factory
+/// methods like `at::empty({10}, options)`, tensor conversions like
+/// `tensor.to(...)`, etc.
+///
+/// To provide a simple API that is consistent with Python, where one can do
+/// `torch.empty(sizes, X)` with `X` being a `torch.device`, `torch.dtype`, or a
+/// `torch.layout`, we want TensorOptions to be implicitly convertible from
+/// `ScalarType dtype`, `Layout layout` and `Device device`. Therefore, we have
+/// three implicit constructors from each of these three types.
+///
+/// This is sufficient for `ScalarType` and `Layout` as they are simple Enum
+/// classes. However, `Device` is an ordinary class with implicit constructors
+/// `Device(DeviceType, DeviceIndex = -1)` and `Device(std::string)` to be
+/// consistent with Python API, where strings are treated as equivalent with a
+/// `torch.device` object (e.g., "cuda:1" can be passed to everywhere a
+/// `torch.device("cuda:1")` is accepted). To support the syntax
+/// `at::empty({10}, {kCUDA, 1})` and `tensor.to(kCUDA)`, we need to make sure
+/// that `TensorOptions` is implicitly constructible with any arguments that a
+/// `Device` can constructed from. So we have,
+///
+///    /* implicit */ TensorOptions(T&& device) : TensorOptions() {
+///      this->set_device(device);
+///    }
+///
+///    template <typename... Args,
+///             typename = std::enable_if_t<std::is_constructible<Device,
+///             Args&&...>::value>>
+///    /* implicit */  TensorOptions(Args&&... args)
+///     : TensorOptions(Device(std::forward<Args>(args)...)) {}
+///
+///
+/// But this will be problematic. Consider this: `TensorOptions({kCUDA, 1})`.
+/// Compiler will complain about ambiguity between the copy constructor and the
+/// `Device` constructor because `{kCUDA, 1}` can be converted to both a
+/// `TensorOption` and a `Device`.
+///
+/// To get around this, we templatize the `Device` constructor. Since overload
+/// resolution is done before template resolution, our problem is solved.
+
+DispatchKey computeDispatchKey(
+    std::optional<ScalarType> dtype,
+    std::optional<Layout> layout,
+    std::optional<Device> device);
+
+struct C10_API TensorOptions {
+  TensorOptions()
+      : requires_grad_(false),
+        pinned_memory_(false),
+        has_device_(false),
+        has_dtype_(false),
+        has_layout_(false),
+        has_requires_grad_(false),
+        has_pinned_memory_(false),
+        has_memory_format_(false) {}
+
+  /// Constructs a `TensorOptions` object with the given layout.
+  /* implicit */ TensorOptions(Layout layout) : TensorOptions() {
+    this->set_layout(layout);
+  }
+
+  /// Constructs a `TensorOptions` object with the given device.
+  /// See NOTE [ TensorOptions Constructors ] on why this is templatized.
+  template <
+      typename T,
+      typename = std::enable_if_t<std::is_same_v<std::decay_t<T>, Device>>>
+  /* implicit */ TensorOptions(T&& device) : TensorOptions() {
+    this->set_device(std::forward<T>(device));
+  }
+
+  /// Constructs a `TensorOptions` object from arguments allowed in `Device`
+  /// constructors.
+  ///
+  /// See NOTE [ TensorOptions Constructors ].
+  ///
+  /// NB: Ideally we only allow implicit constructors here. But there is no easy
+  ///     way to detect them. So we have this one that allows explicit
+  ///     constructors too.
+  template <
+      typename... Args,
+      typename = std::enable_if_t<std::is_constructible_v<Device, Args&&...>>>
+  /* implicit */ TensorOptions(Args&&... args)
+      : TensorOptions(Device(std::forward<Args>(args)...)) {}
+
+  /// Constructs a `TensorOptions` object with the given dtype.
+  /* implicit */ TensorOptions(caffe2::TypeMeta dtype) : TensorOptions() {
+    this->set_dtype(dtype);
+  }
+
+  /// legacy constructor to support ScalarType
+  /* implicit */ TensorOptions(ScalarType dtype) : TensorOptions() {
+    this->set_dtype(dtype);
+  }
+
+  /// Constructs a `TensorOptions` object with the given memory format.
+  /* implicit */ TensorOptions(MemoryFormat memory_format) : TensorOptions() {
+    set_memory_format(memory_format);
+  }
+
+  /// Return a copy of `TensorOptions` with `device` set to the given one, or
+  /// cleared if `device` is `nullopt`.
+  [[nodiscard]] TensorOptions device(
+      std::optional<Device> device) const noexcept {
+    TensorOptions r = *this;
+    r.set_device(device);
+    return r;
+  }
+
+  /// Return a copy of `TensorOptions` with `device` set to the given one.
+  /// (This overload ensures that variadic template std::optional constructor
+  /// for Device work correctly.)
+  template <typename... Args>
+  [[nodiscard]] TensorOptions device(Args&&... args) const noexcept {
+    return device(
+        std::optional<Device>(std::in_place, std::forward<Args>(args)...));
+  }
+
+  /// Return a copy of `TensorOptions`, but with device set to CUDA, and the
+  /// device index set to the given one.
+  ///
+  /// TODO: This function encourages bad behavior (assuming CUDA is
+  /// the only device that matters).  Get rid of it / rename it.
+  [[nodiscard]] TensorOptions device_index(
+      c10::DeviceIndex device_index) const noexcept {
+    return device(Device::Type::CUDA, device_index);
+  }
+
+  /// Return a copy of `TensorOptions` with `dtype` set to the given one.
+  [[nodiscard]] TensorOptions dtype(
+      std::optional<caffe2::TypeMeta> dtype) const noexcept {
+    TensorOptions r = *this;
+    r.set_dtype(dtype);
+    return r;
+  }
+
+  // legacy function to support ScalarType
+  [[nodiscard]] TensorOptions dtype(
+      std::optional<ScalarType> dtype) const noexcept {
+    TensorOptions r = *this;
+    r.set_dtype(dtype);
+    return r;
+  }
+
+  // Since dtype is taken...
+  template <typename T>
+  TensorOptions& dtype() {
+    dtype_ = caffe2::TypeMeta::Make<T>();
+    has_dtype_ = true;
+    return *this;
+  }
+
+  /// Sets the layout of the `TensorOptions`.
+  [[nodiscard]] TensorOptions layout(
+      std::optional<Layout> layout) const noexcept {
+    TensorOptions r = *this;
+    r.set_layout(layout);
+    return r;
+  }
+
+  /// Sets the `requires_grad` property of the `TensorOptions`.
+  [[nodiscard]] TensorOptions requires_grad(
+      std::optional<bool> requires_grad) const noexcept {
+    TensorOptions r = *this;
+    r.set_requires_grad(requires_grad);
+    return r;
+  }
+
+  /// Sets the `pinned_memory` property on the `TensorOptions`.
+  [[nodiscard]] TensorOptions pinned_memory(
+      std::optional<bool> pinned_memory) const noexcept {
+    TensorOptions r = *this;
+    r.set_pinned_memory(pinned_memory);
+    return r;
+  }
+
+  /// Sets the `memory_format` property on `TensorOptions`.
+  [[nodiscard]] TensorOptions memory_format(
+      std::optional<MemoryFormat> memory_format) const noexcept {
+    TensorOptions r = *this;
+    r.set_memory_format(memory_format);
+    return r;
+  }
+
+  /// Returns the device of the `TensorOptions`.
+  Device device() const noexcept {
+    return device_or_default(device_opt());
+  }
+
+  /// Returns whether the device is specified.
+  bool has_device() const noexcept {
+    return has_device_;
+  }
+
+  /// Returns the device of the `TensorOptions`, or `std::nullopt` if
+  /// device is not specified.
+  std::optional<Device> device_opt() const noexcept {
+    return has_device_ ? std::make_optional(device_) : std::nullopt;
+  }
+
+  /// Returns the device index of the `TensorOptions`.
+  c10::DeviceIndex device_index() const noexcept {
+    return device().index();
+  }
+
+  /// Returns the dtype of the `TensorOptions`.
+  caffe2::TypeMeta dtype() const noexcept {
+    return dtype_or_default(dtype_opt());
+  }
+
+  /// Returns whether the dtype is specified.
+  bool has_dtype() const noexcept {
+    return has_dtype_;
+  }
+
+  /// Returns the dtype of the `TensorOptions`, or `std::nullopt` if
+  /// device is not specified.
+  std::optional<caffe2::TypeMeta> dtype_opt() const noexcept {
+    return has_dtype_ ? std::make_optional(dtype_) : std::nullopt;
+  }
+
+  /// Returns the layout of the `TensorOptions`.
+  Layout layout() const noexcept {
+    return layout_or_default(layout_opt());
+  }
+
+  /// Returns whether the layout is specified.
+  bool has_layout() const noexcept {
+    return has_layout_;
+  }
+
+  /// Returns the layout of the `TensorOptions`, or `std::nullopt` if
+  /// layout is not specified.
+  std::optional<Layout> layout_opt() const noexcept {
+    return has_layout_ ? std::make_optional(layout_) : std::nullopt;
+  }
+
+  /// Returns the `requires_grad` property of the `TensorOptions`.
+  bool requires_grad() const noexcept {
+    return has_requires_grad_ ? requires_grad_ : false;
+  }
+
+  /// Returns whether the `requires_grad` is specified.
+  bool has_requires_grad() const noexcept {
+    return has_requires_grad_;
+  }
+
+  /// Returns the `requires_grad` property of the `TensorOptions`, or
+  /// `std::nullopt` if `requires_grad` is not specified.
+  std::optional<bool> requires_grad_opt() const noexcept {
+    return has_requires_grad_ ? std::make_optional(requires_grad_)
+                              : std::nullopt;
+  }
+
+  /// Returns the `pinned_memory` property of the `TensorOptions`.
+  bool pinned_memory() const noexcept {
+    return pinned_memory_or_default(pinned_memory_opt());
+  }
+
+  /// Returns whether the `pinned_memory` is specified.
+  bool has_pinned_memory() const noexcept {
+    return has_pinned_memory_;
+  }
+
+  /// Returns if the layout is sparse
+  bool is_sparse() const {
+    return layout_ == c10::Layout::Sparse;
+  }
+
+  /// Returns if the layout is sparse CSR, deprecated, use
+  /// is_sparse_compressed() instead
+  bool is_sparse_csr() const {
+    return layout_ == c10::Layout::SparseCsr;
+  }
+
+  bool is_sparse_compressed() const {
+    return layout_ == c10::Layout::SparseCsr ||
+        layout_ == c10::Layout::SparseCsc ||
+        layout_ == c10::Layout::SparseBsr || layout_ == c10::Layout::SparseBsc;
+  }
+
+  // For compatibility with legacy tensor.type() comparisons
+  bool type_equal(const TensorOptions& other) const {
+    return computeDispatchKey() == other.computeDispatchKey() &&
+        typeMetaToScalarType(dtype_) == typeMetaToScalarType(other.dtype());
+  }
+
+  /// Returns the `pinned_memory` property of the `TensorOptions`, or
+  /// `std::nullopt` if `pinned_memory` is not specified.
+  std::optional<bool> pinned_memory_opt() const noexcept {
+    return has_pinned_memory_ ? std::make_optional(pinned_memory_)
+                              : std::nullopt;
+  }
+
+  /// Returns whether the `memory_layout` is specified
+  bool has_memory_format() const noexcept {
+    return has_memory_format_;
+  }
+
+  // NB: memory_format() getter is PURPOSELY not defined, as the default
+  // behavior of memory_format varies from function to function.
+
+  /// Returns the `memory_layout` property of `TensorOptions, or
+  /// `std::nullopt` if `memory_format` is not specified.
+  std::optional<MemoryFormat> memory_format_opt() const noexcept {
+    return has_memory_format_ ? std::make_optional(memory_format_)
+                              : std::nullopt;
+  }
+
+  // Resolves the ATen backend specified by the current construction axes.
+  // TODO: Deprecate this
+  Backend backend() const {
+    return at::dispatchKeyToBackend(computeDispatchKey());
+  }
+
+  /// Return the right-biased merge of two TensorOptions.  This has the
+  /// effect of overwriting settings from self with specified options
+  /// of options.
+  ///
+  /// NB: This merging operation does NOT respect device merges.
+  /// For example, if you device({kCUDA, 1}).merge_in(kCUDA)
+  /// you will get kCUDA in the end!  Functions like Tensor.new_empty
+  /// ensure the right device is selected anyway by way of a
+  /// device guard.
+  ///
+  TensorOptions merge_in(TensorOptions options) const noexcept {
+    TensorOptions merged = *this;
+    if (options.has_device())
+      merged.set_device(options.device_opt());
+    if (options.has_dtype())
+      merged.set_dtype(options.dtype_opt());
+    if (options.has_layout())
+      merged.set_layout(options.layout_opt());
+    // NB: requires grad is right biased; not a logical AND/OR!
+    if (options.has_requires_grad())
+      merged.set_requires_grad(options.requires_grad_opt());
+    if (options.has_pinned_memory())
+      merged.set_pinned_memory(options.pinned_memory_opt());
+    if (options.has_memory_format())
+      merged.set_memory_format(options.memory_format_opt());
+    return merged;
+  }
+
+  // TODO remove after TensorOptions rationalization
+  TensorOptions merge_memory_format(
+      std::optional<MemoryFormat> optional_memory_format) const noexcept {
+    TensorOptions merged = *this;
+    if (optional_memory_format.has_value()) {
+      merged.set_memory_format(optional_memory_format);
+    }
+    return merged;
+  }
+
+  // INVARIANT: computeDispatchKey returns only the subset of dispatch keys for
+  // which dispatchKeyToBackend is injective, if it is defined at all  (for
+  // the most part, this just means that this function never returns an
+  // Autograd key)
+  DispatchKey computeDispatchKey() const {
+    return c10::computeDispatchKey(
+        optTypeMetaToScalarType(dtype_opt()), layout_opt(), device_opt());
+  }
+
+ private:
+  // These methods are currently private because I'm not sure if it's wise
+  // to actually publish them.  They are methods because I need them in
+  // the constructor and the functional API implementation.
+  //
+  // If you really, really need it, you can make these public, but check if you
+  // couldn't just do what you need with the functional API.  Similarly, these
+  // methods are not chainable, because if you wanted chaining, you probably
+  // want to use the functional API instead.  (It's probably OK to make
+  // these chainable, because these functions are all explicitly annotated
+  // with a ref-qualifier, the trailing &, that makes them illegal to call
+  // on temporaries.)
+
+  /// Mutably set the device of `TensorOptions`.
+  void set_device(std::optional<Device> device) & noexcept {
+    if (device) {
+      device_ = *device;
+      has_device_ = true;
+    } else {
+      has_device_ = false;
+    }
+  }
+
+  /// Mutably set the dtype of `TensorOptions`.
+  void set_dtype(std::optional<caffe2::TypeMeta> dtype) & noexcept {
+    if (dtype) {
+      dtype_ = *dtype;
+      has_dtype_ = true;
+    } else {
+      has_dtype_ = false;
+    }
+  }
+
+  // legacy function to support ScalarType
+  void set_dtype(std::optional<ScalarType> dtype) & noexcept {
+    if (dtype) {
+      dtype_ = scalarTypeToTypeMeta(*dtype);
+      has_dtype_ = true;
+    } else {
+      has_dtype_ = false;
+    }
+  }
+
+  /// Mutably set the layout of `TensorOptions`.
+  void set_layout(std::optional<Layout> layout) & noexcept {
+    if (layout) {
+      layout_ = *layout;
+      has_layout_ = true;
+    } else {
+      has_layout_ = false;
+    }
+  }
+
+  /// Mutably set the `requires_grad` property of `TensorOptions`.
+  void set_requires_grad(std::optional<bool> requires_grad) & noexcept {
+    if (requires_grad) {
+      requires_grad_ = *requires_grad;
+      has_requires_grad_ = true;
+    } else {
+      has_requires_grad_ = false;
+    }
+  }
+
+  /// Mutably set the `pinned_memory` property of `TensorOptions`.
+  void set_pinned_memory(std::optional<bool> pinned_memory) & noexcept {
+    if (pinned_memory) {
+      pinned_memory_ = *pinned_memory;
+      has_pinned_memory_ = true;
+    } else {
+      has_pinned_memory_ = false;
+    }
+  }
+
+  /// Mutably set the `memory_Format` property of `TensorOptions`.
+  void set_memory_format(std::optional<MemoryFormat> memory_format) & noexcept {
+    if (memory_format) {
+      memory_format_ = *memory_format;
+      has_memory_format_ = true;
+    } else {
+      has_memory_format_ = false;
+    }
+  }
+
+  // WARNING: If you edit TensorOptions to add more options, you
+  // may need to adjust the implementation of Tensor::options.
+  // The criteria for whether or not Tensor::options must be adjusted
+  // is whether or not the new option you added should preserved
+  // by functions such as empty_like(); if it should be preserved,
+  // you must adjust options().
+  //
+  // TODO: MemoryFormat is not implemented in this way
+
+  // NB: We didn't use std::optional here, because then we can't pack
+  // the has_***_ boolean fields.
+
+  Device device_ = at::kCPU; // 16-bit
+  caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make<float>(); // 16-bit
+  Layout layout_ = at::kStrided; // 8-bit
+  MemoryFormat memory_format_ = MemoryFormat::Contiguous; // 8-bit
+
+  // Bitmask required here to get this to fit inside 32 bits (or even 64 bits,
+  // for that matter)
+
+  bool requires_grad_ : 1;
+  bool pinned_memory_ : 1;
+
+  bool has_device_ : 1;
+  bool has_dtype_ : 1;
+  bool has_layout_ : 1;
+  bool has_requires_grad_ : 1;
+  bool has_pinned_memory_ : 1;
+  bool has_memory_format_ : 1;
+};
+
+// We should aspire to fit in one machine-size word; but a size greater than two
+// words is too much.  (We are doing terribly on 32-bit archs, where we require
+// three machine size words to store tensor options.  Eek!)
+static_assert(
+    sizeof(TensorOptions) <= sizeof(int64_t) * 2,
+    "TensorOptions must fit in 128-bits");
+
+/// Convenience function that returns a `TensorOptions` object with the `dtype`
+/// set to the given one.
+inline TensorOptions dtype(caffe2::TypeMeta dtype) {
+  return TensorOptions().dtype(dtype);
+}
+
+// legacy function to support ScalarType
+inline TensorOptions dtype(ScalarType dtype) {
+  return TensorOptions().dtype(scalarTypeToTypeMeta(dtype));
+}
+
+/// Convenience function that returns a `TensorOptions` object with the `layout`
+/// set to the given one.
+inline TensorOptions layout(Layout layout) {
+  return TensorOptions().layout(layout);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the `device`
+/// set to the given one.
+inline TensorOptions device(Device device) {
+  return TensorOptions().device(device);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `device` set to CUDA and the `device_index` set to the given one.
+inline TensorOptions device_index(c10::DeviceIndex device_index) {
+  return TensorOptions().device_index(device_index);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `requires_grad` set to the given one.
+inline TensorOptions requires_grad(bool requires_grad = true) {
+  return TensorOptions().requires_grad(requires_grad);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `memory_format` set to the given one.
+inline TensorOptions memory_format(MemoryFormat memory_format) {
+  return TensorOptions().memory_format(memory_format);
+}
+
+C10_API std::ostream& operator<<(
+    std::ostream& stream,
+    const TensorOptions& options);
+
+template <typename T>
+inline TensorOptions dtype() {
+  return dtype(caffe2::TypeMeta::Make<T>());
+}
+
+inline std::string toString(const TensorOptions& options) {
+  std::ostringstream stream;
+  stream << options;
+  return stream.str();
+}
+
+// This is intended to be a centralized location by which we can determine
+// what an appropriate DispatchKey for a tensor is.
+inline DispatchKey computeDispatchKey(
+    std::optional<ScalarType> dtype,
+    std::optional<Layout> layout,
+    std::optional<Device> device) {
+  const auto layout_ = layout_or_default(layout);
+  const auto device_ = device_or_default(device);
+  switch (layout_) {
+    case Layout::Jagged:
+    case Layout::Strided: {
+      const auto dtype_ = dtype_or_default(dtype);
+      switch (device_.type()) {
+#define DO_CASE(device, _)                   \
+  case c10::DeviceType::device: {            \
+    if (isQIntType(dtype_)) {                \
+      return DispatchKey::Quantized##device; \
+    }                                        \
+    return DispatchKey::device;              \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        case c10::DeviceType::FPGA:
+          return DispatchKey::FPGA;
+        case c10::DeviceType::MAIA:
+          return DispatchKey::MAIA;
+        case c10::DeviceType::Vulkan:
+          return DispatchKey::Vulkan;
+        case c10::DeviceType::Metal:
+          return DispatchKey::Metal;
+        case c10::DeviceType::MKLDNN:
+        case c10::DeviceType::OPENGL:
+        case c10::DeviceType::OPENCL:
+        case c10::DeviceType::IDEEP:
+          TORCH_INTERNAL_ASSERT(
+              0,
+              "This is a grandfathered Caffe2 device type ",
+              device_.type(),
+              ", it shouldn't ever convert to a DispatchKey.  File a bug describing what you were doing if you think this is in error.");
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for dense layout: ",
+              device_.type());
+      }
+    }
+    case Layout::Sparse:
+      switch (device_.type()) {
+#define DO_CASE(device, _)              \
+  case c10::DeviceType::device: {       \
+    return DispatchKey::Sparse##device; \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for sparse layout: ",
+              device_.type());
+      }
+    case Layout::Mkldnn:
+      switch (device_.type()) {
+        case c10::DeviceType::CPU:
+          return DispatchKey::MkldnnCPU;
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for mkldnn layout: ",
+              device_.type());
+      }
+    case Layout::SparseCsr:
+    case Layout::SparseCsc:
+    case Layout::SparseBsr:
+    case Layout::SparseBsc:
+      switch (device_.type()) {
+#define DO_CASE(device, _)                 \
+  case c10::DeviceType::device: {          \
+    return DispatchKey::SparseCsr##device; \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for ",
+              layout_,
+              " layout: ",
+              device_.type());
+      }
+    default:
+      TORCH_CHECK(false, "Unsupported layout: ", layout_);
+  }
+}
+
+inline Layout dispatchKeyToLayout(DispatchKey dispatch_key) {
+  switch (dispatch_key) {
+#define DO_CASE(bc, _) case DispatchKey::Sparse##bc:
+    C10_FORALL_BACKEND_COMPONENTS(DO_CASE, unused)
+#undef DO_CASE
+    return Layout::Sparse;
+#define DO_CASE(bc, _) case DispatchKey::SparseCsr##bc:
+    C10_FORALL_BACKEND_COMPONENTS(DO_CASE, unused)
+#undef DO_CASE
+    TORCH_CHECK(
+        false, "Cannot map DispatchKey ", dispatch_key, " to a unique layout.");
+    case DispatchKey::MkldnnCPU:
+      return Layout::Mkldnn;
+    default:
+      return Layout::Strided;
+  }
+}
+
+inline c10::DeviceType dispatchKeyToDeviceType(DispatchKey dispatch_key) {
+  switch (dispatch_key) {
+    // stuff that's real
+#define DO_CASE(suffix, prefix)     \
+  case DispatchKey::prefix##suffix: \
+    return c10::DeviceType::suffix;
+#define DO_CASES(_, prefix) C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, prefix)
+    C10_FORALL_FUNCTIONALITY_KEYS(DO_CASES)
+#undef DO_CASES
+#undef DO_CASE
+
+    case DispatchKey::MkldnnCPU:
+      return c10::DeviceType::CPU;
+    case DispatchKey::Vulkan:
+      return c10::DeviceType::Vulkan;
+
+    case DispatchKey::MAIA:
+      return c10::DeviceType::MAIA;
+    default:
+      TORCH_CHECK(
+          false,
+          "DispatchKey ",
+          dispatch_key,
+          " doesn't correspond to a device");
+  }
+}
+
+inline TensorOptions dispatchKeyToTensorOptions(DispatchKey dispatch_key) {
+  return TensorOptions()
+      .layout(dispatchKeyToLayout(dispatch_key))
+      .device(dispatchKeyToDeviceType(dispatch_key));
+}
+
+namespace detail {
+inline bool backend_supports_empty_operator(const TensorOptions& options) {
+  // Quantized backends don't support at::empty().
+  // They have separate operators like at::empty_quantized() that take in
+  // extra information about how to quantize the tensor.
+  return !isQIntType(typeMetaToScalarType(options.dtype()));
+}
+
+} // namespace detail
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/UndefinedTensorImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/UndefinedTensorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..33ac4e7f868a45667d21615c28b0dfba15dce8af
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/UndefinedTensorImpl.h
@@ -0,0 +1,49 @@
+#pragma once
+
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/SymIntArrayRef.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/util/ArrayRef.h>
+#include <cstdint>
+
+namespace c10 {
+
+struct C10_API UndefinedTensorImpl final : public TensorImpl {
+ public:
+  // Without this, we get:
+  //  error: identifier "at::UndefinedTensorImpl::_singleton" is undefined in
+  //  device code
+  // (ostensibly because the constexpr tricks MSVC into trying to compile this
+  // function for device as well).
+#ifdef _WIN32
+  static inline TensorImpl* singleton() {
+    return &getInstance();
+  }
+#else
+  static constexpr inline TensorImpl* singleton() {
+    return &_singleton;
+  }
+#endif
+
+#ifdef DEBUG
+  bool has_storage() const override;
+#endif
+  void set_storage_offset(int64_t offset) override;
+
+ protected:
+  bool is_contiguous_custom(MemoryFormat format) const override;
+  IntArrayRef strides_custom() const override;
+  SymIntArrayRef sym_strides_custom() const override;
+
+ private:
+  UndefinedTensorImpl();
+#ifdef _WIN32
+  static UndefinedTensorImpl& getInstance();
+#else
+  static UndefinedTensorImpl _singleton;
+#endif
+  const char* tensorimpl_type_name() const override;
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/WrapDimMinimal.h b/lib/python3.11/site-packages/torch/include/c10/core/WrapDimMinimal.h
new file mode 100644
index 0000000000000000000000000000000000000000..623da03503ae1780f02d68193b276c32383764af
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/WrapDimMinimal.h
@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <cstdint>
+#include <utility>
+
+namespace c10 {
+
+namespace detail {
+// This template can only be specialized at int64_t and c10::SymInt;
+// you'll get linker errors otherwise
+template <typename T>
+C10_API T maybe_wrap_dim_slow(T dim, T dim_post_expr, bool wrap_scalar);
+} // namespace detail
+
+template <typename T>
+T _maybe_wrap_dim(T dim, T dim_post_expr, bool wrap_scalar = true) {
+  // Inline the fast paths
+  if (C10_LIKELY(dim_post_expr * -1 <= dim && dim < dim_post_expr)) {
+    // For SymInts, we want an explicit control flow to trigger a guard, so we
+    // may as well branch too.
+    if (dim < 0) {
+      return dim + dim_post_expr;
+    }
+    return dim;
+  }
+  // Check edge-cases out-of-line (wrapping scalars and out-of-bounds errors)
+  return c10::detail::maybe_wrap_dim_slow<T>(
+      std::move(dim), std::move(dim_post_expr), wrap_scalar);
+}
+
+inline int64_t maybe_wrap_dim(
+    int64_t dim,
+    int64_t dim_post_expr,
+    bool wrap_scalar = true) {
+  return _maybe_wrap_dim(dim, dim_post_expr, wrap_scalar);
+}
+
+inline c10::SymInt maybe_wrap_dim(
+    c10::SymInt dim,
+    c10::SymInt dim_post_expr,
+    bool wrap_scalar = true) {
+  return _maybe_wrap_dim(std::move(dim), std::move(dim_post_expr), wrap_scalar);
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/alignment.h b/lib/python3.11/site-packages/torch/include/c10/core/alignment.h
new file mode 100644
index 0000000000000000000000000000000000000000..fcb960134a68aa788392e12066a205560c4f44fb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/alignment.h
@@ -0,0 +1,21 @@
+#pragma once
+
+#include <cstddef>
+
+namespace c10 {
+
+#ifdef C10_MOBILE
+// Use 16-byte alignment on mobile
+// - ARM NEON AArch32 and AArch64
+// - x86[-64] < AVX
+constexpr size_t gAlignment = 16;
+#else
+// Use 64-byte alignment should be enough for computation up to AVX512.
+constexpr size_t gAlignment = 64;
+#endif
+
+constexpr size_t gPagesize = 4096;
+// since the default thp pagesize is 2MB, enable thp only
+// for buffers of size 2MB or larger to avoid memory bloating
+constexpr size_t gAlloc_threshold_thp = static_cast<size_t>(2) * 1024 * 1024;
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/COW.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/COW.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba7b16443d108f75a72fb42d83760644bbe28a51
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/COW.h
@@ -0,0 +1,32 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/intrusive_ptr.h>
+
+namespace c10 {
+struct StorageImpl;
+class DataPtr;
+} // namespace c10
+
+namespace c10::impl::cow {
+
+// Creates a Copy-on-write (COW) clone of the given storage. This will also
+// convert the given storage into a COW storage if it is not COW already.
+//
+// Converting the storage into a COW storage will not be successful if the
+// storage's DataPtr has some context (`DataPtr::get_context()`) which is not
+// equal to the data pointer (`DataPtr::get()`). In this case, a nullptr is
+// returned.
+C10_API c10::intrusive_ptr<StorageImpl> lazy_clone_storage(
+    StorageImpl& storage);
+
+// Check if a storage has a simple DataPtr with no abnormal context
+C10_API bool has_simple_data_ptr(const c10::StorageImpl& storage);
+
+// Check if a DataPtr is COW
+C10_API bool is_cow_data_ptr(const c10::DataPtr& data_ptr);
+
+// Eagerly copies a COW storage's data, turning it into a non-COW storage.
+C10_API void materialize_cow_storage(StorageImpl& storage);
+
+} // namespace c10::impl::cow
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/COWDeleter.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/COWDeleter.h
new file mode 100644
index 0000000000000000000000000000000000000000..e26625a8c726b8e14fd519e4a5cac80514667a96
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/COWDeleter.h
@@ -0,0 +1,66 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/UniqueVoidPtr.h>
+
+#include <atomic>
+#include <cstdint>
+#include <memory>
+#include <shared_mutex>
+#include <variant>
+
+namespace c10::impl::cow {
+
+// A COWDeleterContext object is used as the `ctx` argument for DataPtr
+// to implement a Copy-on-write (COW) DataPtr.
+class C10_API COWDeleterContext {
+ public:
+  // Creates an instance, holding the pair of data and original
+  // deleter.
+  //
+  // Note that the deleter will only be called in our destructor if
+  // the last reference to this goes away without getting
+  // materialized.
+  explicit COWDeleterContext(std::unique_ptr<void, DeleterFnPtr> data);
+
+  // Increments the current refcount.
+  void increment_refcount();
+
+  // See README.md in this directory to understand the locking
+  // strategy.
+
+  // Represents a reference to the context.
+  //
+  // This is returned by decrement_refcount to allow the caller to
+  // copy the data under the shared lock.
+  using NotLastReference = std::shared_lock<std::shared_mutex>;
+
+  // Represents the last reference to the context.
+  //
+  // This will be returned by decrement_refcount when it is the last
+  // reference remaining and after any pending copies have completed.
+  using LastReference = std::unique_ptr<void, DeleterFnPtr>;
+
+  // Decrements the refcount, returning a handle indicating what to
+  // do with it.
+  std::variant<NotLastReference, LastReference> decrement_refcount();
+
+ private:
+  // The destructor is hidden, this should only ever be used within
+  // UniqueVoidPtr using cow::delete_context as the deleter.
+  ~COWDeleterContext();
+
+  std::shared_mutex mutex_;
+  std::unique_ptr<void, DeleterFnPtr> data_;
+  std::atomic<std::int64_t> refcount_ = 1;
+};
+
+// `cow_deleter` is used as the `ctx_deleter` for DataPtr to implement a COW
+// DataPtr.
+//
+// Warning: This should only be called on a pointer to a COWDeleterContext that
+// was allocated on the heap with `new`, because when the refcount reaches 0,
+// the context is deleted with `delete`.
+C10_API void cow_deleter(void* ctx);
+
+} // namespace c10::impl::cow
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/DeviceGuardImplInterface.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/DeviceGuardImplInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..29aa9bc8038562f9b2bb6c8ae6448a35224fad92
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/DeviceGuardImplInterface.h
@@ -0,0 +1,375 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Stream.h>
+#include <c10/util/Exception.h>
+
+// Just for C10_ANONYMOUS_VARIABLE
+#include <c10/util/Registry.h>
+
+#include <array>
+#include <atomic>
+
+namespace c10 {
+
+// Forward declaration
+class DataPtr;
+
+/**
+ * Note [Flags defining the behavior of events]
+ *
+ * PYTORCH_DEFAULT and BACKEND_DEFAULT are valid for all backends. The
+ * BACKEND_DEFAULT is what a particular backend would select if no
+ * flags were given. PYTORCH_DEFAULT is the PyTorch's framework default
+ * choice for events on that backend, which may not be the same.
+ *
+ * The mapping of PYTORCH_DEFAULT and BACKEND_DEFAULT is done by each
+ * backend implementation.
+ */
+enum class EventFlag {
+  // Disable timing
+  PYTORCH_DEFAULT,
+  // Enable timing
+  BACKEND_DEFAULT,
+  // FOR TESTING ONLY
+  INVALID
+};
+
+namespace impl {
+
+/**
+ * DeviceGuardImplInterface represents the virtual interface which provides
+ * functionality to provide an RAII class for device and stream switching,
+ * via DeviceGuard.  Every distinct device type, e.g., CUDA and HIP, is
+ * expected to implement and register an implementation of this interface.
+ * All classes which inherit from DeviceGuardImplInterface should be declared
+ * 'final'.
+ *
+ * This class exists because we provide a unified interface for performing
+ * device guards via DeviceGuard, but we cannot assume that we have actually
+ * compiled against the, e.g., CUDA library, which actually implements
+ * this guard functionality.  In this case, a dynamic dispatch is required
+ * to cross the library boundary.
+ *
+ * If possible, you should directly use implementations of this interface;
+ * those uses will be devirtualized.
+ */
+struct C10_API DeviceGuardImplInterface {
+  DeviceGuardImplInterface() = default;
+  DeviceGuardImplInterface(const DeviceGuardImplInterface&) = default;
+  DeviceGuardImplInterface& operator=(const DeviceGuardImplInterface&) =
+      default;
+  DeviceGuardImplInterface(DeviceGuardImplInterface&&) noexcept = default;
+  DeviceGuardImplInterface& operator=(DeviceGuardImplInterface&&) noexcept =
+      default;
+
+  /**
+   * Return the type of device managed by this guard implementation.
+   */
+  virtual DeviceType type() const = 0;
+
+  /**
+   * Set the current device to Device, and return the previous Device.
+   */
+  virtual Device exchangeDevice(Device) const = 0;
+  // NB: Implementations of exchangeDevice can be a bit boilerplatey.  You might
+  // consider replacing exchangeDevice with a non-virtual function with a baked
+  // in implementation; however, note that this will triple the number of
+  // virtual calls (when you implement exchangeDevice in a final subclass,
+  // the compiler gets to devirtualize everything; it won't do that if you don't
+  // define it in the subclass!)  A common way to solve this problem is to use
+  // some sort of CRTP; however, we can template DeviceGuardImplInterface since
+  // we really *do* need it to be virtual.  A little boilerplate seems easiest
+  // to explain.  (Another way around this problem is to provide inline
+  // functions that provide the default implementations, but this seems a little
+  // hard to explain.  In any case, we're only going to have on order of ten
+  // implementations of this anyway.)
+
+  /**
+   * Get the current device.
+   */
+  virtual Device getDevice() const = 0;
+
+  /**
+   * Set the current device to Device.
+   */
+  virtual void setDevice(Device) const = 0;
+
+  /**
+   * Set the current device to Device, without checking for errors
+   * (so, e.g., this can be called from a destructor).
+   */
+  virtual void uncheckedSetDevice(Device) const noexcept = 0;
+
+  /**
+   * Get the current stream for a given device.
+   */
+  virtual Stream getStream(Device) const noexcept = 0;
+
+  /**
+   * Get the default stream for a given device.
+   */
+  virtual Stream getDefaultStream(Device) const {
+    TORCH_CHECK(false, "Backend doesn't support acquiring a default stream.")
+  }
+
+  /**
+   * Get a stream from the global pool for a given device.
+   */
+  virtual Stream getStreamFromGlobalPool(Device, bool isHighPriority = false)
+      const {
+    (void)isHighPriority; // Suppress unused variable warning
+    TORCH_CHECK(false, "Backend doesn't support acquiring a stream from pool.")
+  }
+
+  /**
+   * Return a new stream for a given device and priority. The stream will be
+   * copied and shared around, device backend should be able to correctly handle
+   * the lifetime of the stream.
+   */
+  virtual Stream getNewStream(Device, int priority = 0) const {
+    (void)priority;
+    TORCH_CHECK(false, "Backend doesn't support create a new Stream.")
+  }
+
+  /**
+   * Set a stream to be the thread local current stream for its device.
+   * Return the previous stream for that device. You are NOT required
+   * to set the current device to match the device of this stream.
+   */
+  virtual Stream exchangeStream(Stream) const noexcept = 0;
+
+  /**
+   * Destroys the given event.
+   */
+  virtual void destroyEvent(void* /*event*/, const DeviceIndex /*device_index*/)
+      const noexcept {}
+
+  /**
+   * Increments the event's version and enqueues a job with this version
+   * in the stream's work queue. When the stream process that job
+   * it notifies all streams waiting on / blocked by that version of the
+   * event to continue and marks that version as recorded.
+   * */
+  virtual void record(
+      void** /*event*/,
+      const Stream& /*stream*/,
+      const DeviceIndex /*device_index*/,
+      const c10::EventFlag /*flag*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Does nothing if the event has not been scheduled to be recorded.
+   * If the event was previously enqueued to be recorded, a command
+   * to wait for the version of the event that exists at the time of this call
+   * is inserted in the stream's work queue.
+   * When the stream reaches this command it will stop processing
+   * additional commands until that version of the event is marked as recorded.
+   */
+  virtual void block(void* /*event*/, const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Returns true if (and only if)
+   *  (1) the event has never been scheduled to be recorded
+   *  (2) the current version is marked as recorded.
+   * Returns false otherwise.
+   */
+  virtual bool queryEvent(void* /*event*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Get the number of devices.  WARNING: This is REQUIRED to not raise
+   * an exception.  If there is some sort of problem, e.g., driver error,
+   * you should report that there are zero available devices.
+   */
+  virtual DeviceIndex deviceCount() const noexcept = 0;
+
+  /**
+   * Return true if all the work previously enqueued on the stream for
+   * asynchronous execution has completed running on the device.
+   */
+  virtual bool queryStream(const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support querying streams.");
+  }
+
+  /**
+   * Wait (by blocking the calling thread) until all the work previously
+   * enqueued on the stream has completed running on the device.
+   */
+  virtual void synchronizeStream(const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support synchronizing streams.");
+  }
+
+  /**
+   * Wait (by blocking the calling thread) until all the work previously
+   * recorded on the event has completed running on the device.
+   */
+  virtual void synchronizeEvent(void* /*event*/) const {
+    TORCH_CHECK(false, "Backend doesn't support synchronizing events.");
+  }
+
+  /**
+   * Wait (by blocking the calling thread) until all the work previously
+   * enqueued on the device has been completed.
+   */
+  virtual void synchronizeDevice(const DeviceIndex /*device_index*/) const {
+    TORCH_CHECK(
+        false, "Backend doesn't support synchronizing all streams on device.");
+  }
+
+  /**
+   * Ensure the caching allocator (if any) is aware that the given DataPtr is
+   * being used on the given stream, and that it should thus avoid recycling the
+   * DataPtr until all work on that stream is done.
+   */
+  virtual void recordDataPtrOnStream(const c10::DataPtr&, const Stream&) const {
+  }
+
+  /**
+   * Fetch the elapsed time between two recorded events.
+   */
+  virtual double elapsedTime(
+      void* /*event1*/,
+      void* /*event2*/,
+      const DeviceIndex /*device_index*/) const {
+    TORCH_CHECK(false, "Backend doesn't support elapsedTime.");
+  }
+
+  /**
+   * Intended use of this class is to leak the DeviceGuardImpl at program end.
+   * So you better not call the destructor, buster!
+   */
+  virtual ~DeviceGuardImplInterface() = default;
+};
+
+// A no-op device guard impl that doesn't do anything interesting.  Useful
+// for devices that don't actually have a concept of device index.  Prominent
+// examples are CPU and Meta.
+template <DeviceType D>
+struct NoOpDeviceGuardImpl final : public DeviceGuardImplInterface {
+  NoOpDeviceGuardImpl() = default;
+  DeviceType type() const override {
+    return D;
+  }
+  Device exchangeDevice(Device) const override {
+    return Device(D, -1); // no-op
+  }
+  Device getDevice() const override {
+    return Device(D, -1);
+  }
+  void setDevice(Device) const override {
+    // no-op
+  }
+  void uncheckedSetDevice(Device) const noexcept override {
+    // no-op
+  }
+  Stream getStream(Device) const noexcept override {
+    // no-op
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+
+  Stream getNewStream(Device, int priority = 0) const override {
+    // no-op
+    (void)priority;
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+
+  // NB: These do NOT set the current device
+  Stream exchangeStream(Stream) const noexcept override {
+    // no-op
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return 1;
+  }
+
+  // Event-related functions
+  void record(
+      void** /*event*/,
+      const Stream& /*stream*/,
+      const DeviceIndex /*device_index*/,
+      const EventFlag /*flag*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.");
+  }
+  void block(void* /*event*/, const Stream& /*stream*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.")
+  }
+  bool queryEvent(void* /*event*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.")
+  }
+  void destroyEvent(void* /*event*/, const DeviceIndex /*device_index*/)
+      const noexcept override {}
+
+  // Stream-related functions
+  bool queryStream(const Stream& /*stream*/) const override {
+    return true;
+  }
+  void synchronizeStream(const Stream& /*stream*/) const override {
+    // Don't wait for anything.
+  }
+};
+
+// The registry is NON-owning.  Each stored pointer is std::atomic so
+// that under all interleavings of registry calls the structure is
+// race-free.  This doesn't cost us anything on reads in X86.  (An
+// unsynchronized implementation probably is OK too, but I didn't want
+// to prove that we never read from device_guard_impl_registry at the
+// same time some registration is occurring.  Shiver.)
+//
+// I'd like this registry to be valid even at program destruction time
+// (in case someone uses a DeviceGuard in a destructor to do some cleanup
+// in the CUDA API.)  Since there are no direct accesses of the underlying
+// owning objects which I can use to enforce initialization order (unlike
+// in a Meyer singleton), it implies that you must *leak* objects when
+// putting them in the registry.  This is done by deleting the destructor
+// on DeviceGuardImplInterface.
+extern C10_API std::array<
+    std::atomic<const DeviceGuardImplInterface*>,
+    static_cast<size_t>(DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES)>
+    device_guard_impl_registry;
+
+// I can't conveniently use c10/util/Registry.h for the following reason:
+// c10/util/Registry.h gives me a slow way of Create'ing a object of some
+// interface from the registry, but no way of quickly accessing an already
+// created object.  I'll be banging on getDeviceGuardImpl every time we do a
+// DeviceGuard, so I really don't want to be doing an unordered_map lookup.
+// Better if the registration mechanism directly drops its implementation
+// into device_guard_impl_registry.
+
+class C10_API DeviceGuardImplRegistrar {
+ public:
+  DeviceGuardImplRegistrar(DeviceType, const DeviceGuardImplInterface*);
+};
+
+#define C10_REGISTER_GUARD_IMPL(DevType, DeviceGuardImpl)              \
+  static ::c10::impl::DeviceGuardImplRegistrar C10_ANONYMOUS_VARIABLE( \
+      g_##DeviceType)(::c10::DeviceType::DevType, new DeviceGuardImpl());
+
+inline const DeviceGuardImplInterface* getDeviceGuardImpl(DeviceType type) {
+  // Two adjacent int16_t fields DeviceType and DeviceIndex has field access
+  // miscompiled on NVCC. To workaround this issue, we apply a mask to the
+  // DeviceType. First check if the DeviceType is 16-bit.
+  // FB employees can see
+  //   https://fb.workplace.com/groups/llvm.gcc/permalink/4053565044692080/
+  // for more details
+  static_assert(sizeof(DeviceType) == 1, "DeviceType is not 8-bit");
+  auto p = device_guard_impl_registry[static_cast<size_t>(type) & 0xFF].load();
+
+  // This seems to be the first place where you make use of a device
+  // when you pass devices to factory functions.  Give a nicer error
+  // message in this case.
+  TORCH_CHECK(p, "PyTorch is not linked with support for ", type, " devices");
+  return p;
+}
+
+inline bool hasDeviceGuardImpl(DeviceType type) {
+  return device_guard_impl_registry[static_cast<size_t>(type)].load();
+}
+
+} // namespace impl
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/FakeGuardImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/FakeGuardImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8bfe91619edca3e362ba79bd1075325738d3cc4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/FakeGuardImpl.h
@@ -0,0 +1,102 @@
+#pragma once
+
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+
+#include <array>
+
+namespace c10::impl {
+
+// FakeGuardImpl is hardcoded to have eight devices.  Not for
+// any good reason, just to simplify code.
+constexpr DeviceIndex kFakeGuardImplMaxDevices = 8;
+
+/**
+ * A fake implementation of DeviceGuardImplInterface suitable for testing.
+ * The current device is modeled as a mutable field in the guard implementation
+ * class.  See DeviceGuard_test.cpp for an example use.
+ */
+template <DeviceType T>
+struct FakeGuardImpl final : public DeviceGuardImplInterface {
+  static constexpr DeviceType static_type = T;
+  // Runtime device type is not used
+  FakeGuardImpl(DeviceType) {}
+  FakeGuardImpl() = default;
+  DeviceType type() const override {
+    return T;
+  }
+  Device exchangeDevice(Device d) const override {
+    AT_ASSERT(d.type() == type());
+    AT_ASSERT(d.index() < kFakeGuardImplMaxDevices);
+    Device old_device = getDevice();
+    if (old_device.index() != d.index()) {
+      current_device_ = d.index();
+    }
+    return old_device;
+  }
+  Device getDevice() const override {
+    return Device(type(), current_device_);
+  }
+  void setDevice(Device d) const override {
+    AT_ASSERT(d.type() == type());
+    AT_ASSERT(d.index() >= 0);
+    AT_ASSERT(d.index() < kFakeGuardImplMaxDevices);
+    current_device_ = d.index();
+  }
+  void uncheckedSetDevice(Device d) const noexcept override {
+    current_device_ = d.index();
+  }
+  Stream getStream(Device d) const noexcept override {
+    return Stream(Stream::UNSAFE, d, current_streams_[d.index()]);
+  }
+  Stream exchangeStream(Stream s) const noexcept override {
+    auto old_id = current_streams_[s.device_index()];
+    current_streams_[s.device_index()] = s.id();
+    return Stream(Stream::UNSAFE, s.device(), old_id);
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return kFakeGuardImplMaxDevices;
+  }
+
+  // Event-related functions
+  void record(
+      void** event,
+      const Stream& stream,
+      const DeviceIndex device_index,
+      const EventFlag flag) const override {}
+  void block(void* event, const Stream& stream) const override {}
+  bool queryEvent(void* event) const override {
+    return true;
+  }
+  void destroyEvent(void* event, const DeviceIndex device_index)
+      const noexcept override {}
+
+  // Convenience methods for testing
+  static DeviceIndex getDeviceIndex() {
+    return current_device_;
+  }
+  static void setDeviceIndex(DeviceIndex i) {
+    AT_ASSERT(i >= 0);
+    AT_ASSERT(i < kFakeGuardImplMaxDevices);
+    current_device_ = i;
+  }
+  static StreamId getCurrentStreamIdFor(DeviceIndex i) {
+    return current_streams_.at(i);
+  }
+  static void resetStreams() {
+    current_streams_.fill(0);
+  }
+
+ private:
+  thread_local static DeviceIndex current_device_;
+  thread_local static std::array<StreamId, kFakeGuardImplMaxDevices>
+      current_streams_;
+};
+
+template <DeviceType T>
+thread_local DeviceIndex FakeGuardImpl<T>::current_device_ = 0;
+
+template <DeviceType T>
+thread_local std::array<StreamId, kFakeGuardImplMaxDevices>
+    FakeGuardImpl<T>::current_streams_ = {0, 0, 0, 0, 0, 0, 0, 0};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/GPUTrace.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/GPUTrace.h
new file mode 100644
index 0000000000000000000000000000000000000000..3acb875b54a32c7032d5945afac7e1137fc489e8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/GPUTrace.h
@@ -0,0 +1,28 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+
+namespace c10::impl {
+
+struct C10_API GPUTrace {
+  // On the x86 architecture the atomic operations are lock-less.
+  static std::atomic<const PyInterpreter*> gpuTraceState;
+
+  // When PyTorch migrates to C++20, this should be changed to an atomic flag.
+  // Currently, the access to this variable is not synchronized, on the basis
+  // that it will only be flipped once and by the first interpreter that
+  // accesses it.
+  static bool haveState;
+
+  // This function will only register the first interpreter that tries to invoke
+  // it. For all of the next ones it will be a no-op.
+  static void set_trace(const PyInterpreter*);
+
+  static const PyInterpreter* get_trace() {
+    if (!haveState)
+      return nullptr;
+    return gpuTraceState.load(std::memory_order_acquire);
+  }
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/HermeticPyObjectTLS.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/HermeticPyObjectTLS.h
new file mode 100644
index 0000000000000000000000000000000000000000..741132b9f967c19a29e2d130890e0f5ef99289a2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/HermeticPyObjectTLS.h
@@ -0,0 +1,59 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <atomic>
+
+namespace c10::impl {
+
+// This TLS controls whether or not we permanently associate PyObject
+// with Tensor the first time it is allocated.  When hermetic PyObject
+// TLS is enabled (state is true), we DO NOT save PyObjects to Tensor,
+// meaning you get a distinct PyObject whenever you execute the code in
+// question.
+struct C10_API HermeticPyObjectTLS {
+  static void set_state(bool state);
+  static bool get_state() {
+    // Hypothetical fastpath if torchdeploy/multipy isn't used.  Per
+    // https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
+    // this qualifies relaxed access because it is a single-location data
+    // structure (only the boolean here).
+    //
+    // Forgetting about data races for a moment, is there a logical race?
+    //
+    //  - Boolean only ever transitions from false to true.  So the
+    //    critical situation is when one interpreter is already running
+    //    when a second interpreter switches haveState from false to true.
+    //
+    //  - The first interpreter is indifferent whether or not it sees
+    //    hasState true/false; obviously false works (this is what the
+    //    interpreter was previously using; more directly, the interpreter
+    //    calls into itself as the handler, so being hermetic is not
+    //    required), and true simply means serviced python operator calls will
+    //    be hermetic; in these cases it is expected to be functionally
+    //    equivalent.
+    //
+    //  - The second interpreter MUST see hasState true (as its requests will
+    //    be forwarded to the first interpreter), but it is assumed that there
+    //    is a synchronization between the interpreter initialization, and
+    //    when we actually perform operations, so it is guaranteed to see
+    //    hasState true.
+    //
+    // QED.
+    //
+    // This fastpath is currently disabled so that we can more easily test that
+    // hermetic mode works correctly even on stock build of PyTorch.
+    if (false && !haveState_.load(std::memory_order_relaxed))
+      return false;
+    return get_tls_state();
+  }
+  // Call this from the multipy/torchdeploy top level
+  static void init_state();
+
+ private:
+  // This only flipped once from false to true during torchdeploy/multipy
+  // initialization, and never again.
+  static std::atomic<bool> haveState_;
+  static bool get_tls_state();
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineDeviceGuard.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineDeviceGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..a80ac550906aa488600df0e1abaf265d2fefd0aa
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineDeviceGuard.h
@@ -0,0 +1,433 @@
+#pragma once
+
+// This file provides implementations of InlineDeviceGuard and
+// InlineOptionalDeviceGuard.
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <type_traits>
+#include <utility>
+
+namespace c10::impl {
+
+/**
+ * A DeviceGuard is an RAII class that sets a device to some value
+ * on construction, and resets the device to its original value on
+ * destruction.
+ *
+ * InlineDeviceGuard is a helper class for implementing DeviceGuards.
+ * It is templated over a DeviceGuardImpl (anything that implements
+ * DeviceGuardImplInterface).  There are two primary ways to instantiate
+ * InlineDeviceGuard:
+ *
+ *  - With a concrete implementation of DeviceGuardImpl, e.g., CUDAGuardImpl.
+ *    This is the best way to use InlineDeviceGuard, as all calls are
+ *    devirtualized, giving you code as efficient as straight line
+ *    calls to cudaGetDevice/cudaSetDevice.
+ *
+ *  - With VirtualGuardImpl, which does a virtual dispatch to a DeviceGuardImpl
+ *    retrieved from a DeviceType registry.  We have explicitly instantiated
+ *    InlineDeviceGuard this way as c10::DeviceGuard.
+ *
+ * If you are in a hurry, you can use InlineDeviceGuard directly:
+ *
+ *    using CUDAGuard = impl::InlineDeviceGuard<CUDAGuardImpl>;
+ *
+ * However, you can provide a better user experience if you explicitly write a
+ * wrapper class that itself contains the template instantiation:
+ *
+ *    class CUDAGuard {
+ *    public:
+ *      // ... the API ...
+ *    private:
+ *      impl::InlineDeviceGuard<CUDAGuardImpl> guard_;
+ *    }
+ *
+ * The wrapper class provides a good place to write documentation, and helps
+ * avoid weird template instantiation errors when a user incorrectly uses the
+ * class.
+ *
+ * If you need to test this class, consider instantiating it with FakeGuardImpl.
+ */
+template <typename T>
+class InlineDeviceGuard {
+ public:
+  // Note [Omitted default constructor from RAII]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // In principle, we could add a default constructor to
+  // DeviceGuard which reads the current device and promises to
+  // restore to that device on exit.  However, most cases where you
+  // would have written this, you probably meant to actually just
+  // use DeviceGuard (since you don't actually need the
+  // restore to happen if you don't ever actually set the device).
+  // We remove the constructor here to encourage you to think about
+  // what you actually want to happen.
+  explicit InlineDeviceGuard() = delete;
+
+  /// Set the current device to the passed Device.
+  explicit InlineDeviceGuard(Device device)
+      : impl_(device.type()),
+        original_device_(
+            device.index() == -1 ? impl_.getDevice()
+                                 : impl_.exchangeDevice(device)),
+        current_device_(device.index() == -1 ? original_device_ : device) {}
+
+  /// Set the current device index to the passed DeviceIndex.  (The
+  /// device type is inferred from the template parameter T).
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineDeviceGuard(DeviceIndex device_index)
+      : InlineDeviceGuard(Device(U::static_type, device_index)) {}
+
+  /// Construct an InlineDeviceGuard using VirtualGuardImpl with an explicit
+  /// DeviceGuardImplInterface pointer.
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineDeviceGuard(
+      Device device,
+      const DeviceGuardImplInterface* impl)
+      : impl_(
+            VirtualGuardImpl(impl ? impl : getDeviceGuardImpl(device.type()))),
+        original_device_(
+            device.index() == -1 ? impl_.getDevice()
+                                 : impl_.exchangeDevice(device)),
+        current_device_(device.index() == -1 ? original_device_ : device) {}
+
+  /// Copy is disallowed
+  InlineDeviceGuard(const InlineDeviceGuard<T>&) = delete;
+  InlineDeviceGuard<T>& operator=(const InlineDeviceGuard<T>&) = delete;
+
+  /// Move is disallowed, as DeviceGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineDeviceGuard(InlineDeviceGuard<T>&& other) = delete;
+  InlineDeviceGuard& operator=(InlineDeviceGuard<T>&& other) = delete;
+
+  ~InlineDeviceGuard() {
+    impl_.uncheckedSetDevice(original_device_);
+  }
+
+  /// Sets the device to the given one.
+  template <
+      typename U = T,
+      typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>, int> = 0>
+  void set_device(at::Device device) {
+    AT_ASSERT(
+        (U::static_type == DeviceType::HIP && device.is_cuda()) ||
+        device.type() == U::static_type);
+    auto index = device.index();
+    if (index == -1)
+      return;
+    impl_.setDevice(device);
+    current_device_ = device;
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device.  This is effectively equivalent to
+  /// set_device when a guard supports only a single device type.
+  template <typename U = T>
+  typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>> reset_device(
+      at::Device device) {
+    set_device(device);
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device (for a possibly different device
+  /// type).
+  ///
+  /// This method is named reset_device to highlight the fact that previous
+  /// device settings from this guard are NOT preserved, even if the device
+  /// has a different device type.  For example:
+  ///
+  ///   // CUDA device is 0
+  ///   DeviceGuard g(Device(kCUDA, 1));
+  ///   g.reset_device(Device(kHIP, 2));
+  ///   // CUDA device is 0 (!!)
+  ///
+  /// NOTE: this implementation may skip some device setting if it can prove
+  /// that it is unnecessary.
+  ///
+  /// Optional argument is for testing only.
+  template <typename U = T>
+  typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>> reset_device(
+      at::Device device,
+      const impl::DeviceGuardImplInterface* impl = nullptr) {
+    auto index = device.index();
+    if (index == -1)
+      return;
+    if (device.type() == original_device_.type()) {
+      AT_ASSERT(impl == nullptr || impl->type() == device.type());
+      impl_.setDevice(device);
+      current_device_ = device;
+    } else {
+      // Destruct and reconstruct the DeviceGuard in place
+      impl_.setDevice(original_device_);
+      impl_ = !impl ? VirtualGuardImpl(device.type()) : VirtualGuardImpl(impl);
+      original_device_ = impl_.exchangeDevice(device);
+      current_device_ = device;
+    }
+  }
+
+  /// Sets the device index to the given one.  The device type is inferred
+  /// from the original device type.
+  void set_index(DeviceIndex index) {
+    reset_device(Device(original_device_.type(), index));
+  }
+
+  /// Returns the device that was set at the time the most recent
+  /// reset_device(), or otherwise the device at construction time.
+  Device original_device() const {
+    return original_device_;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return current_device_;
+  }
+
+ protected:
+  T impl_;
+
+ private:
+  Device original_device_;
+  Device current_device_;
+};
+
+/**
+ * A OptionalDeviceGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ *
+ * InlineOptionalDeviceGuard is a helper class for implementing
+ * OptionalDeviceGuards.  See guidance in InlineDeviceGuard on how to
+ * use this.  See OptionalDeviceGuard for user-oriented usage notes.
+ */
+template <typename T>
+class InlineOptionalDeviceGuard {
+ public:
+  // Note [Explicit initialization of optional fields]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // Explicit initialization of optional fields
+  // required to workaround an nvcc bug; see
+  // https://github.com/pytorch/pytorch/issues/12117
+
+  /// Creates an uninitialized OptionalDeviceGuard.
+  explicit InlineOptionalDeviceGuard()
+      : guard_() // See Note [Explicit initialization of optional fields]
+  {}
+  ~InlineOptionalDeviceGuard() = default;
+
+  /// Set the current device to the passed Device, if it is not nullopt.
+  explicit InlineOptionalDeviceGuard(std::optional<Device> device_opt)
+      : guard_() { // See Note [Explicit initialization of optional fields]
+    if (device_opt.has_value()) {
+      guard_.emplace(device_opt.value());
+    }
+  }
+
+  /// Set the current device to the passed DeviceIndex, if it is not nullopt.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineOptionalDeviceGuard(
+      std::optional<DeviceIndex> device_index_opt)
+      : guard_() { // See Note [Explicit initialization of optional fields]
+    if (device_index_opt.has_value()) {
+      guard_.emplace(device_index_opt.value());
+    }
+  }
+
+  /// All constructors of DeviceGuard are valid for OptionalDeviceGuard
+  /// and result in initialized OptionalDeviceGuard.
+  template <typename... Args>
+  explicit InlineOptionalDeviceGuard(Args&&... args)
+      : guard_(std::in_place, std::forward<Args>(args)...) {}
+
+  // TODO: Consider reading Tensor and TensorList constructors here, when
+  // Tensor moves to c10.  (These are only valid on OptionalDeviceGuard,
+  // because a Tensor may be undefined, in which case we need an uninitialized
+  // tensor guard.)
+
+  // Note [Move construction for RAII guards is tricky]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // In principle, move construction is useful for terminating
+  // the lifetime of a `OptionalDeviceGuard` early; for example:
+  //
+  //     // current device is d0
+  //     OptionalDeviceGuard g1(d1);
+  //     // current device is d1
+  //     {
+  //       OptionalDeviceGuard g2(std::move(g1));
+  //     }
+  //     // current device is d0!!
+  //
+  // However, it's difficult to implement the move constructor
+  // in a way that works in all situations.  For example, consider
+  // the following example:
+  //
+  //     OptionalDeviceGuard g1(d1);
+  //     {
+  //       OptionalDeviceGuard g2(d2);
+  //       {
+  //         OptionalDeviceGuard g3(std::move(g1)); // !!!
+  //       }
+  //     }
+  //
+  // What should the current device be while g3 in scope... and what
+  // should it be after it goes out of scope?  What about g2?
+  // There don't seem to be satisfactory answers for these questions.
+  //
+  // It's in principle possible to raise an error when this occurs
+  // by doing some extra thread-local bookkeeping.  But why bother?
+  // Just don't provide the constructor.
+  InlineOptionalDeviceGuard(const InlineOptionalDeviceGuard<T>& other) = delete;
+  InlineOptionalDeviceGuard(InlineOptionalDeviceGuard<T>&& other) = delete;
+
+  // Note [Move assignment for RAII guards is tricky]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // Move assignment is deleted, because you need to know which guard was
+  // defined "first", as that guard's original_device_ wins--with the current
+  // representation, we have no way of telling which is the case.  (Move
+  // construction does not have this problem, as one guard is always
+  // uninitialized.)
+  //
+  // We can make this clear by way of a pair of examples:
+  //
+  // Example 1:
+  //
+  //  // initial device is n0
+  //  {
+  //    CUDAGuard g1(n1);
+  //    {
+  //      CUDAGuard g2(n2);
+  //      // current device should be n2
+  //      g1 = std::move(g2);
+  //      // current device should still be n2
+  //    }
+  //    // current device should still be n2
+  //  }
+  //  // current device should be n0
+  //
+  //  Example 2 (flip the order of the two guards):
+  //
+  //  // initial device is n0
+  //  {
+  //    CUDAGuard g2(n2);
+  //    {
+  //      CUDAGuard g1(n1);
+  //      // current device should be n1
+  //      g1 = std::move(g2);
+  //      // current device should be n2
+  //    }
+  //    // current device should be n0 (since g2 has been vacated)
+  //  }
+  //
+  // In both examples, we need g1 to restore to n0 after move assignment.
+  // However, in example 1, this is determined by the restore value of g1
+  // (prior to the move). In example 2, however, it is determined by the the
+  // restore value of g2(!!). We don't know which one should win, without having
+  // a way of telling which guard was allocated first.
+  //
+  // We could solve this with an extra thread-local variable.  But no one is
+  // actually using move-assignment.  So just get rid of it.
+  InlineOptionalDeviceGuard& operator=(const InlineOptionalDeviceGuard& other) =
+      delete;
+  InlineOptionalDeviceGuard& operator=(InlineOptionalDeviceGuard&& other) =
+      delete;
+
+  /// Sets the device to the given one.  Initializes OptionalDeviceGuard if it
+  /// is not already initialized.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void set_device(at::Device device) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device);
+    } else {
+      guard_->set_device(device);
+    }
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device (for a possibly different device
+  /// type).  Initializes OptionalDeviceGuard if it is not already initialized.
+  ///
+  /// See notes on why this is called reset_device on InlineDeviceGuard.
+  ///
+  /// Optional argument is for testing only.
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  void reset_device(
+      at::Device device,
+      const DeviceGuardImplInterface* impl = nullptr) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device, impl);
+    } else {
+      guard_->reset_device(device, impl);
+    }
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device.  Initializes the guard if it is
+  /// not already initialized.  This is effectively equivalent to set_device
+  /// when a guard supports only a single device type.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void reset_device(at::Device device) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device);
+    } else {
+      guard_->reset_device(device);
+    }
+  }
+
+  /// Sets the device index to the given one.  The device type is statically
+  /// known.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void set_index(DeviceIndex index) {
+    if (!guard_.has_value()) {
+      guard_.emplace(index);
+    } else {
+      guard_->set_index(index);
+    }
+  }
+
+  /// Returns the device that was set immediately prior to initialization of
+  /// the, guard, or nullopt if the guard is uninitialized.
+  std::optional<Device> original_device() const {
+    return guard_.has_value() ? std::make_optional(guard_->original_device())
+                              : std::nullopt;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device, if the guard is initialized,
+  /// or nullopt if the guard is uninitialized.
+  std::optional<Device> current_device() const {
+    return guard_.has_value() ? std::make_optional(guard_->current_device())
+                              : std::nullopt;
+  }
+
+  /// Restore the original device, resetting this guard to uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  std::optional<InlineDeviceGuard<T>> guard_;
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineEvent.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineEvent.h
new file mode 100644
index 0000000000000000000000000000000000000000..82fa3384907ef03907f1edccea5ea17f34ad77cb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineEvent.h
@@ -0,0 +1,139 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/core/Stream.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/util/Exception.h>
+
+namespace c10::impl {
+
+template <typename T>
+struct InlineEvent final {
+  InlineEvent() = delete;
+  InlineEvent(
+      const DeviceType _device_type,
+      const EventFlag _flag = EventFlag::PYTORCH_DEFAULT)
+      : backend_{_device_type}, device_type_{_device_type}, flag_{_flag} {}
+
+  // Copy constructor and copy assignment operator (deleted)
+  InlineEvent(const InlineEvent&) = delete;
+  InlineEvent& operator=(const InlineEvent&) = delete;
+
+  // Move constructor and move assignment operator
+  InlineEvent(InlineEvent&& other) noexcept
+      : event_(other.event_),
+        backend_(std::move(other.backend_)),
+        device_type_(other.device_type_),
+        device_index_(other.device_index_),
+        flag_(other.flag_),
+        was_marked_for_recording_(other.was_marked_for_recording_) {
+    other.event_ = nullptr;
+  }
+  InlineEvent& operator=(InlineEvent&& other) noexcept {
+    swap(other);
+    return *this;
+  }
+
+  void swap(InlineEvent& other) noexcept {
+    std::swap(event_, other.event_);
+    std::swap(backend_, other.backend_);
+    std::swap(device_type_, other.device_type_);
+    std::swap(device_index_, other.device_index_);
+    std::swap(flag_, other.flag_);
+    std::swap(was_marked_for_recording_, other.was_marked_for_recording_);
+  }
+
+  ~InlineEvent() noexcept {
+    if (event_)
+      backend_.destroyEvent(event_, device_index_);
+  }
+
+  DeviceType device_type() const noexcept {
+    return device_type_;
+  }
+  DeviceIndex device_index() const noexcept {
+    return device_index_;
+  }
+  EventFlag flag() const noexcept {
+    return flag_;
+  }
+  bool was_marked_for_recording() const noexcept {
+    return was_marked_for_recording_;
+  }
+
+  void recordOnce(const Stream& stream) {
+    if (!was_marked_for_recording_)
+      record(stream);
+  }
+
+  void record(const Stream& stream) {
+    TORCH_CHECK(
+        stream.device_type() == device_type_,
+        "Event device type ",
+        DeviceTypeName(device_type_),
+        " does not match recording stream's device type ",
+        DeviceTypeName(stream.device_type()),
+        ".");
+
+    backend_.record(&event_, stream, device_index_, flag_);
+    was_marked_for_recording_ = true;
+    device_index_ = stream.device_index();
+  }
+
+  void block(const Stream& stream) const {
+    if (!was_marked_for_recording_)
+      return;
+
+    TORCH_CHECK(
+        stream.device_type() == device_type_,
+        "Event device type ",
+        DeviceTypeName(device_type_),
+        " does not match blocking stream's device type ",
+        DeviceTypeName(stream.device_type()),
+        ".");
+
+    backend_.block(event_, stream);
+  }
+
+  bool query() const {
+    if (!was_marked_for_recording_)
+      return true;
+    return backend_.queryEvent(event_);
+  }
+
+  void* eventId() const {
+    return event_;
+  }
+
+  double elapsedTime(const InlineEvent& other) const {
+    TORCH_CHECK(
+        other.was_marked_for_recording(),
+        "other was not marked for recording.");
+    TORCH_CHECK(
+        was_marked_for_recording(), "self was not marked for recording.");
+    TORCH_CHECK(
+        other.device_type() == device_type_,
+        "Event device type ",
+        DeviceTypeName(device_type_),
+        " does not match other's device type ",
+        DeviceTypeName(other.device_type()),
+        ".");
+    return backend_.elapsedTime(event_, other.event_, device_index_);
+  }
+
+  void synchronize() const {
+    if (!was_marked_for_recording_)
+      return;
+    backend_.synchronizeEvent(event_);
+  }
+
+ private:
+  void* event_ = nullptr;
+  T backend_;
+  DeviceType device_type_;
+  DeviceIndex device_index_ = -1;
+  EventFlag flag_ = EventFlag::PYTORCH_DEFAULT;
+  bool was_marked_for_recording_ = false;
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineStreamGuard.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineStreamGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..51c25e25ffa6bc217b3a231568f248becf202a71
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/InlineStreamGuard.h
@@ -0,0 +1,260 @@
+#pragma once
+
+#include <c10/core/impl/InlineDeviceGuard.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/irange.h>
+
+namespace c10::impl {
+
+/**
+ * A StreamGuard is an RAII class that changes the current device
+ * to the device corresponding to some stream, and changes the
+ * default stream on that device to be this stream.
+ *
+ * InlineStreamGuard is a helper class for implementing StreamGuards.
+ * See InlineDeviceGuard for guidance on how to use this class.
+ */
+template <typename T>
+class InlineStreamGuard : private InlineDeviceGuard<T> {
+ public:
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit InlineStreamGuard() = delete;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  explicit InlineStreamGuard(Stream stream)
+      : InlineDeviceGuard<T>(stream.device()),
+        original_stream_of_original_device_(
+            this->impl_.getStream(original_device())),
+        original_stream_of_current_device_(this->impl_.exchangeStream(stream)),
+        current_stream_(stream) {}
+
+  /// This constructor exists purely for testing
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineStreamGuard(
+      Stream stream,
+      const DeviceGuardImplInterface* impl)
+      : InlineDeviceGuard<T>(
+            stream.device(),
+            impl ? impl : getDeviceGuardImpl(stream.device_type())),
+        original_stream_of_original_device_(
+            this->impl_.getStream(original_device())),
+        original_stream_of_current_device_(this->impl_.exchangeStream(stream)),
+        current_stream_(stream) {}
+
+  /// Copy is disallowed
+  InlineStreamGuard(const InlineStreamGuard<T>&) = delete;
+  InlineStreamGuard<T>& operator=(const InlineStreamGuard<T>&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineStreamGuard(InlineStreamGuard<T>&& other) = delete;
+  InlineStreamGuard& operator=(InlineStreamGuard<T>&& other) = delete;
+
+  ~InlineStreamGuard() {
+    this->impl_.exchangeStream(original_stream_of_current_device_);
+  }
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// use MultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    // TODO: make a version that takes an impl argument.  Unfortunately,
+    // that will require SFINAE because impl is only valid for the
+    // VirtualGuardImpl specialization.
+    if (stream.device() == this->current_device()) {
+      this->impl_.exchangeStream(stream);
+      current_stream_ = stream;
+    } else {
+      // Destruct and reconstruct the StreamGuard in-place
+      this->impl_.exchangeStream(original_stream_of_current_device_);
+      this->reset_device(stream.device());
+      original_stream_of_current_device_ = this->impl_.exchangeStream(stream);
+      current_stream_ = stream;
+    }
+  }
+
+  // It's not clear if set_device should also reset the current stream
+  // if the device is unchanged; therefore, we don't provide it.
+  // The situation is somewhat clearer with reset_device, but it's still
+  // a pretty weird thing to do, so haven't added this either.
+
+  /// Returns the stream of the original device prior to this guard.  Subtly,
+  /// the stream returned here is the original stream of the *original*
+  /// device; i.e., it's the stream that your computation *would* have
+  /// been put on, if it hadn't been for this meddling stream guard.
+  /// This is usually what you want.
+  Stream original_stream() const {
+    return original_stream_of_original_device_;
+  }
+
+  /// Returns the most recent stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  Stream current_stream() const {
+    return current_stream_;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return InlineDeviceGuard<T>::current_device();
+  }
+
+  /// Returns the device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return InlineDeviceGuard<T>::original_device();
+  }
+
+ private:
+  Stream
+      original_stream_of_original_device_; // what the user probably cares about
+  Stream original_stream_of_current_device_; // what we need to restore
+  Stream current_stream_;
+};
+
+/**
+ * An OptionalStreamGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ * See InlineOptionalDeviceGuard for more guidance on how to use this class.
+ */
+template <typename T>
+class InlineOptionalStreamGuard {
+ public:
+  /// Creates an uninitialized stream guard.
+  explicit InlineOptionalStreamGuard()
+      : guard_() // See Note [Explicit initialization of optional fields]
+  {}
+  ~InlineOptionalStreamGuard() = default;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit InlineOptionalStreamGuard(std::optional<Stream> stream_opt)
+      : guard_() {
+    if (stream_opt.has_value()) {
+      guard_.emplace(stream_opt.value());
+    }
+  }
+
+  /// All constructors of StreamGuard are valid for OptionalStreamGuard
+  template <typename... Args>
+  explicit InlineOptionalStreamGuard(Args&&... args)
+      : guard_(std::in_place, std::forward<Args>(args)...) {}
+
+  InlineOptionalStreamGuard(const InlineOptionalStreamGuard<T>& other) = delete;
+  InlineOptionalStreamGuard& operator=(const InlineOptionalStreamGuard& other) =
+      delete;
+  // See Note [Move construction for RAII guards is tricky]
+  InlineOptionalStreamGuard(InlineOptionalStreamGuard<T>&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  InlineOptionalStreamGuard& operator=(InlineOptionalStreamGuard&& other) =
+      delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the OptionalStreamGuard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    if (guard_.has_value()) {
+      guard_->reset_stream(stream);
+    } else {
+      guard_.emplace(stream);
+    }
+  }
+
+  /// Returns the stream that was set at the time the guard was most recently
+  /// initialized, or nullopt if the guard is uninitialized.
+  std::optional<Stream> original_stream() const {
+    return guard_.has_value() ? std::make_optional(guard_->original_stream())
+                              : std::nullopt;
+  }
+
+  /// Returns the most recent stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  std::optional<Stream> current_stream() const {
+    return guard_.has_value() ? std::make_optional(guard_->current_stream())
+                              : std::nullopt;
+  }
+
+  /// Restore the original device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  std::optional<InlineStreamGuard<T>> guard_;
+};
+
+template <typename T>
+class InlineMultiStreamGuard {
+ public:
+  /// Calls `set_stream` on each of the streams in the list.
+  /// This may be useful if you need to set different streams
+  /// for different devices.
+  explicit InlineMultiStreamGuard(ArrayRef<Stream> streams) {
+    if (!streams.empty()) {
+      impl_.emplace(getDeviceTypeOfStreams(streams));
+      original_streams_.reserve(streams.size());
+      for (const Stream& s : streams) {
+        original_streams_.emplace_back(this->impl_->exchangeStream(s));
+      }
+    }
+  }
+
+  /// Copy is disallowed
+  InlineMultiStreamGuard(const InlineMultiStreamGuard&) = delete;
+  InlineMultiStreamGuard<T>& operator=(const InlineMultiStreamGuard&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineMultiStreamGuard(InlineMultiStreamGuard&& other) = delete;
+  InlineMultiStreamGuard& operator=(InlineMultiStreamGuard&& other) = delete;
+
+  ~InlineMultiStreamGuard() noexcept {
+    if (this->impl_.has_value()) {
+      for (const Stream& s : original_streams_) {
+        this->impl_->exchangeStream(s);
+      }
+    }
+  }
+
+ protected:
+  std::optional<T> impl_;
+
+ private:
+  /// The original streams that were active on all devices.
+  std::vector<Stream> original_streams_;
+
+  static DeviceType getDeviceTypeOfStreams(ArrayRef<Stream> streams) {
+    TORCH_INTERNAL_ASSERT(!streams.empty());
+    DeviceType type = streams[0].device_type();
+    for (const auto idx : c10::irange(1, streams.size())) {
+      TORCH_CHECK_VALUE(
+          streams[idx].device_type() == type,
+          "Streams have a mix of device types: stream 0 is on ",
+          streams[0].device(),
+          " while stream ",
+          idx,
+          " is on device ",
+          streams[idx].device());
+    }
+    return type;
+  }
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/LocalDispatchKeySet.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/LocalDispatchKeySet.h
new file mode 100644
index 0000000000000000000000000000000000000000..1232bd25eb3bd41b01655751e3ddf5f8a0fd5b5e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/LocalDispatchKeySet.h
@@ -0,0 +1,169 @@
+#pragma once
+
+#include <c10/core/DispatchKeySet.h>
+#include <c10/macros/Export.h>
+
+// TLS management for DispatchKeySet (the "local" DispatchKeySet(s))
+//
+// This manages two thread-local DispatchKeySets:
+//
+//  - The included type set, which adds a tensor type for consideration
+//    in dispatch.  (For example, you might add Profiling to
+//    the included type set to turn on profiling on all tensor operations.)
+//
+//  - The excluded type set, which disqualifies a tensor type from dispatch.
+//    (For example, after redispatching on variable, we disqualify
+//    Autograd so we don't attempt to handle variable again.)
+//    (Exclusion wins over inclusion.)
+//
+// NB: Originally, I implemented the excluded type set as storing the inverted
+// set, but TLS is defined to be zero-initialized, so this doesn't actually work
+// (if it's inverted, you want the set to be -1 initialized).
+
+namespace c10::impl {
+
+// POD version of LocalDispatchKeySet.  Declared here just so that
+// we can put it in the guards.
+// This struct encapsulates special handling for TLS initialization
+// in set_included()/included() API so that they reflect the truth.
+// If you want to create PODLocalDispatchKeySet with non-zero state,
+// use set_included() instead of default constructor.
+struct C10_API PODLocalDispatchKeySet {
+  uint64_t included_;
+  uint64_t excluded_;
+
+  // See Note [TLS Initialization]
+  DispatchKeySet included() const {
+    return DispatchKeySet(DispatchKeySet::RAW, included_) ^
+        c10::default_included_set;
+  }
+  DispatchKeySet excluded() const {
+    return DispatchKeySet(DispatchKeySet::RAW, excluded_) ^
+        c10::default_excluded_set;
+  }
+
+  void set_included(DispatchKeySet x) {
+    included_ = (x ^ c10::default_included_set).raw_repr();
+  }
+  void set_excluded(DispatchKeySet x) {
+    excluded_ = (x ^ c10::default_excluded_set).raw_repr();
+  }
+};
+static_assert(
+    std::is_trivial_v<PODLocalDispatchKeySet>,
+    "PODLocalDispatchKeySet must be a POD type.");
+
+struct C10_API LocalDispatchKeySet {
+  /* implicit */ LocalDispatchKeySet(PODLocalDispatchKeySet x)
+      : included_(x.included()), excluded_(x.excluded()) {}
+  DispatchKeySet included_;
+  DispatchKeySet excluded_;
+};
+
+// thread_local variables cannot be C10_API on Windows.
+// Inlining this seems to break AutoDispatchBelowAutograd on Android.
+#if defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+C10_API LocalDispatchKeySet tls_local_dispatch_key_set();
+#else // defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+extern C10_API thread_local PODLocalDispatchKeySet raw_local_dispatch_key_set;
+
+inline C10_API LocalDispatchKeySet tls_local_dispatch_key_set() {
+  // Don't let people fiddle with the thread_local directly just
+  // because they include this header.
+  return raw_local_dispatch_key_set;
+}
+#endif // defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+
+// Internal, use ThreadLocalStateGuard
+C10_API void _force_tls_local_dispatch_key_set(LocalDispatchKeySet key_set);
+
+// RAII API for manipulating the thread-local dispatch state.
+
+class C10_API IncludeDispatchKeyGuard {
+ public:
+  IncludeDispatchKeyGuard(DispatchKeySet);
+  IncludeDispatchKeyGuard(DispatchKey k)
+      : IncludeDispatchKeyGuard(DispatchKeySet(k)) {}
+  IncludeDispatchKeyGuard(const IncludeDispatchKeyGuard&) = delete;
+  IncludeDispatchKeyGuard operator=(const IncludeDispatchKeyGuard&) = delete;
+  IncludeDispatchKeyGuard(IncludeDispatchKeyGuard&&) = delete;
+  IncludeDispatchKeyGuard operator=(IncludeDispatchKeyGuard&&) = delete;
+  ~IncludeDispatchKeyGuard();
+
+ private:
+  // A little micro-optimization to save us from tls_get_addr call
+  // on destruction
+  PODLocalDispatchKeySet* tls_;
+  DispatchKeySet include_;
+};
+
+class C10_API ExcludeDispatchKeyGuard {
+ public:
+  ExcludeDispatchKeyGuard(DispatchKeySet);
+  ExcludeDispatchKeyGuard(DispatchKey k)
+      : ExcludeDispatchKeyGuard(DispatchKeySet(k)) {}
+  ExcludeDispatchKeyGuard(const ExcludeDispatchKeyGuard&) = delete;
+  ExcludeDispatchKeyGuard operator=(const ExcludeDispatchKeyGuard&) = delete;
+  ExcludeDispatchKeyGuard(ExcludeDispatchKeyGuard&&) = delete;
+  ExcludeDispatchKeyGuard operator=(ExcludeDispatchKeyGuard&&) = delete;
+  ~ExcludeDispatchKeyGuard();
+
+ private:
+  // A little micro-optimization to save us from tls_get_addr call
+  // on destruction
+  PODLocalDispatchKeySet* tls_;
+  DispatchKeySet exclude_;
+};
+
+struct C10_API ForceDispatchKeyGuard {
+ public:
+  ForceDispatchKeyGuard()
+      : saved_keyset_(c10::impl::tls_local_dispatch_key_set()) {}
+  ForceDispatchKeyGuard(c10::impl::LocalDispatchKeySet key_set)
+      : ForceDispatchKeyGuard() {
+    c10::impl::_force_tls_local_dispatch_key_set(key_set);
+  }
+  ForceDispatchKeyGuard(
+      c10::DispatchKeySet include,
+      c10::DispatchKeySet exclude)
+      : ForceDispatchKeyGuard() {
+    auto updated_set = saved_keyset_;
+    updated_set.included_ = include;
+    updated_set.excluded_ = exclude;
+    c10::impl::_force_tls_local_dispatch_key_set(updated_set);
+  }
+
+  ForceDispatchKeyGuard(ForceDispatchKeyGuard&&) noexcept = delete;
+  ForceDispatchKeyGuard(const ForceDispatchKeyGuard&) = delete;
+  ForceDispatchKeyGuard& operator=(const ForceDispatchKeyGuard&) = delete;
+  ForceDispatchKeyGuard& operator=(ForceDispatchKeyGuard&&) = delete;
+  ~ForceDispatchKeyGuard() {
+    c10::impl::_force_tls_local_dispatch_key_set(saved_keyset_);
+  }
+
+ private:
+  c10::impl::LocalDispatchKeySet saved_keyset_;
+};
+
+// Non-RAII API for manipulating the thread-local dispatch state.
+// Please prefer the RAII API.  The non-RAII API may be useful when
+// the included/excluded state of a given DispatchKey must span
+// many calls from the Python to the C++, so you cannot conveniently
+// use an RAII guard.
+//
+// Example use case:  a Python context manager that includes a certain
+// DispatchKey, to ensure ops running under the context manager dispatch
+// through that DispatchKey's registered overrides.
+//
+// The non-RAII API is less efficient than the RAII guards because both the
+// getter and setter will do a tls_getaddr lookup (the RAII struct only needs
+// one!)
+
+C10_API bool tls_is_dispatch_key_excluded(DispatchKey x);
+C10_API void tls_set_dispatch_key_excluded(DispatchKey x, bool desired_state);
+C10_API bool tls_is_dispatch_key_included(DispatchKey x);
+C10_API void tls_set_dispatch_key_included(DispatchKey x, bool desired_state);
+C10_API bool tls_is_dispatch_keyset_excluded(DispatchKeySet ks);
+C10_API bool tls_is_dispatch_keyset_included(DispatchKeySet ks);
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/PyInterpreter.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/PyInterpreter.h
new file mode 100644
index 0000000000000000000000000000000000000000..568de4491cfbb1a0063fdd08c2d612fa780bf816
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/PyInterpreter.h
@@ -0,0 +1,263 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/core/Layout.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/SymIntArrayRef.h>
+#include <c10/macros/Export.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/intrusive_ptr.h>
+#include <c10/util/python_stub.h>
+#include <string>
+#include <vector>
+
+// Forward declarations
+
+namespace c10 {
+struct IValue;
+class OperatorHandle;
+struct TensorImpl;
+} // namespace c10
+
+namespace torch::jit {
+using Stack = std::vector<c10::IValue>;
+}
+
+// Actual implementation
+
+namespace c10::impl {
+
+struct C10_API PyInterpreter;
+
+// Note [Python interpreter tag]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Traditionally, PyTorch is layered such that our Python library
+// (libtorch_python) references our pure C++ library (libtorch) as the
+// natural order of things.  However, sometimes this natural order is
+// subverted: C++ objects refer to Python objects (for example, we
+// store a PyObject* pointer on TensorImpl so that converting from a
+// C++ Tensor to a Python Tensor is just a memory dereference).
+//
+// These unusual orderings must be treated with care.  To start, you need to
+// virtualize the destructor so that the PyObject can be decref'ed on
+// destruction (because the C++ object itself doesn't know anything about
+// Python--remember, layering!).  This process itself is fraught, since
+// acquiring the GIL could lead to deadlocks if someone is blocking on you
+// while holding the GIL.  Furthermore, if the C++ objects outlive the
+// interpreter (which can happen if you stash them in a static global
+// variable defined in libtorch), you may attempt to decref the object when
+// the Python interpreter has already been shutdown.
+//
+// BUT WAIT, IT GETS WORSE.  With torchdeploy, there may be multiple Python
+// interpreters in a single process. If a C++ object is accessible from
+// multiple interpreters, we must take care not to accidentally pass a
+// PyObject from one interpreter with another interpreter.
+//
+// To prevent these mixups, we introduce a PyInterpreter "tag" (object with
+// a vtable), which specifies a specific Python interpreter.
+//
+//  - Any given object can be associated with AT MOST one Python interpreter.
+//    We represent the interpreter tag as a memory address to an instance of
+//    a virtual class that is allocated once per interpreter (this is so that
+//    we can request the interpreter to perform operations for us, if
+//    necessary).
+//
+//  - It can be recorded with a PyObject (PyInterpreterObject) so that
+//    we know what interpreter the object is associated with, and we can
+//    raise an error if you try to use the PyObject from the wrong
+//    interpreter context.
+//
+//  - It contains a vtable that can be used to perform various Python
+//    operations from ordinary C++ code that ordinarily wouldn't be accessible
+//    from libtorch.
+//
+// A simple use case is when a C++ object must be associated with a PyObject.
+// However, for TensorImpl, we lazily allocate a PyObject the first time the
+// object passes into Python.  The invariants for this situation are more
+// subtle:
+//
+//  - A given TensorImpl's interpreter tag can only go from uninitialized to
+//    tagged; once tagged, this is a quiescent state (once tagged to an
+//    interpreter, ALWAYS tagged to that interpreter)
+//
+//  - A thread may mutate the PyObject field of a TensorImpl if and only if it
+//    holds the GIL for the interpreter tagged on the TensorImpl.  (If the
+//    TensorImpl is not tagged, it must first atomically claim its tag before it
+//    can validly write)
+//
+// WARNING: This class has to be written very carefully, because it may be
+// possible for a Tensor to have a reference an interpreter corresponding to
+// a shared library that has ALREADY BEEN UNLOADED.  This makes blindly calling
+// virtual methods very dangerous, because the vtable may be garbage at that
+// point (on a good day, you might get "pure virtual method called").
+//
+// The idea to solve this problem is we always leak PyInterpreters (so they
+// always stay live even after dlclose), and make sure we can disarm their
+// virtual methods by indirecting through a separate PyInterpreterVTable
+// object.  This can be replaced with a no-op vtable from libc10.so, which
+// is guaranteed to stick around until the bitter end.
+//
+// NB: The downside with representing PyInterpreter tags as full objects is that
+// it takes an extra word on TensorImpl.  If tags were instead just integer
+// indices, on 64-bit architectures we could pack the tag and PyObject together
+// into a single atomic word.  On 32-bit architectures we could simply say that
+// only one Python interpreter is supported (erroring if a nontrivial
+// interpreter tag is attempted to be set).
+//
+// The difficulty with this scheme is we need to maintain an out-of-line table
+// to get at the PyInterpreters so that we can do virtual method calls on them,
+// and registration/deregistration to this table must be done in a thread safe
+// manner.  This can be easily done if the number of possible PyInterpreters is
+// small enough (e.g., 8-bit integer) by simply preallocating an array of
+// sufficient size to hold all possible interpreters.  Surely 128 threads is
+// more than enough for anyone!
+//
+// I didn't decide to do this technique at the moment, because the extra word
+// added by the PyInterpreter tag takes us to 24 words, which means that we
+// still fit inside three eight word cache lines.  If you need to penny pinch
+// another word consider doing this!
+
+struct C10_API PyInterpreterVTable {
+  virtual ~PyInterpreterVTable() = default;
+
+  // Report the name of this interpreter
+  virtual std::string name() const = 0;
+
+  // Run Py_INCREF on a PyObject.
+  virtual void incref(PyObject* pyobj) const = 0;
+  // Run Py_DECREF on a PyObject.  We DO NOT assume the GIL is held on call
+  // See NOTE [PyInterpreter::decref takes a `has_pyobj_slot` arg]
+  virtual void decref(PyObject* pyobj, bool has_pyobj_slot) const = 0;
+
+  // Perform a detach by deferring to the __torch_dispatch__ implementation of
+  // detach, which will also arrange for the PyObject to get copied in this
+  // situation
+  virtual c10::intrusive_ptr<TensorImpl> detach(
+      const TensorImpl* self) const = 0;
+
+  // Invoke the Python boxed fallback dispatch to go back into Python
+  virtual void dispatch(const c10::OperatorHandle& op, torch::jit::Stack* stack)
+      const = 0;
+
+  virtual void reportErrorCallback(PyObject* callback, DispatchKey key)
+      const = 0;
+
+  // This is only invoked in the multipy/torchdeploy situation from
+  // pythonOpRegistrationTrampoline; this lets us get to the Python
+  // interpreter to actually find the appropriate Python op registration
+  // entry to call.
+  virtual void python_op_registration_trampoline(
+      const c10::OperatorHandle& op,
+      c10::DispatchKey,
+      c10::DispatchKeySet keyset,
+      torch::jit::Stack* stack,
+      bool with_keyset,
+      bool with_op) const = 0;
+
+  virtual void throw_abstract_impl_not_imported_error(
+      std::string opname,
+      const char* pymodule,
+      const char* context) const = 0;
+
+  // Invoke the Python dispatcher to handle this call
+  virtual void python_dispatcher(
+      const c10::OperatorHandle& op,
+      c10::DispatchKeySet,
+      torch::jit::Stack* stack) const = 0;
+
+  virtual bool is_contiguous(const TensorImpl* self, at::MemoryFormat)
+      const = 0;
+  virtual bool is_strides_like(const TensorImpl* self, at::MemoryFormat)
+      const = 0;
+  virtual bool is_non_overlapping_and_dense(const TensorImpl* self) const = 0;
+  virtual c10::Device device(const TensorImpl* self) const = 0;
+  virtual int64_t dim(const TensorImpl* self) const = 0;
+  virtual c10::IntArrayRef strides(const TensorImpl* self) const = 0;
+  virtual c10::IntArrayRef sizes(const TensorImpl* self) const = 0;
+  virtual c10::SymIntArrayRef sym_sizes(const TensorImpl* self) const = 0;
+  virtual c10::Layout layout(const TensorImpl* self) const = 0;
+  virtual int64_t numel(const TensorImpl* self) const = 0;
+  virtual c10::SymInt sym_numel(const TensorImpl* self) const = 0;
+  virtual c10::SymIntArrayRef sym_strides(const TensorImpl* self) const = 0;
+  virtual c10::SymInt sym_storage_offset(const TensorImpl* self) const = 0;
+
+  virtual void trace_gpu_event_creation(
+      c10::DeviceType device_type,
+      uintptr_t event) const = 0;
+  virtual void trace_gpu_event_deletion(
+      c10::DeviceType device_type,
+      uintptr_t event) const = 0;
+  virtual void trace_gpu_event_record(
+      c10::DeviceType device_type,
+      uintptr_t event,
+      uintptr_t stream) const = 0;
+  virtual void trace_gpu_event_wait(
+      c10::DeviceType device_type,
+      uintptr_t event,
+      uintptr_t stream) const = 0;
+  virtual void trace_gpu_memory_allocation(
+      c10::DeviceType device_type,
+      uintptr_t ptr) const = 0;
+  virtual void trace_gpu_memory_deallocation(
+      c10::DeviceType device_type,
+      uintptr_t ptr) const = 0;
+  virtual void trace_gpu_stream_creation(
+      c10::DeviceType device_type,
+      uintptr_t stream) const = 0;
+  virtual void trace_gpu_device_synchronization(
+      c10::DeviceType device_type) const = 0;
+  virtual void trace_gpu_stream_synchronization(
+      c10::DeviceType device_type,
+      uintptr_t stream) const = 0;
+  virtual void trace_gpu_event_synchronization(
+      c10::DeviceType device_type,
+      uintptr_t event) const = 0;
+
+  virtual void reset_backward_hooks(const TensorImpl* self) const = 0;
+};
+
+struct C10_API PyInterpreter {
+  const PyInterpreterVTable* vtable_;
+
+  PyInterpreter(const PyInterpreterVTable* vtable) : vtable_(vtable) {}
+
+  const PyInterpreterVTable& operator*() const noexcept {
+    return *vtable_;
+  }
+  const PyInterpreterVTable* operator->() const noexcept {
+    return vtable_;
+  }
+
+  // Disarm this PyInterpreter, making all of its methods noops.
+  // The vtable pointer is not an atomic at the moment, which means
+  // a disarm() invocation that is concurrent with active destructors
+  // is not thread safe and will trigger TSAN.  My hope is that this
+  // situations doesn't ever actually happen; tensor destruction should
+  // quiesce when a dlclose happens, and any long lived tensors whose
+  // destructors would be disarmed here only begin the destruction process
+  // on process shutdown (long after the dlclose has occurred).
+  void disarm() noexcept;
+};
+
+// PyInterpreterStatus describes what the state of its interpreter tag
+// is, relative to the thread currently holding the GIL.
+enum class PyInterpreterStatus {
+  // We just allocated the Tensor, it hasn't escaped to other threads,
+  // we know that it definitely hasn't been tagged to be associated
+  // with an interpreter.
+  DEFINITELY_UNINITIALIZED,
+  // We queried the interpreter field and it looked uninitialized.  But
+  // another thread may have raced with us to tag it with some other
+  // interpreter id.  So we will have to do a CEX to make sure we can
+  // actually nab it.
+  MAYBE_UNINITIALIZED,
+  // We queried the interpreter field and it was tagged to belong to us.
+  // This means we have sole write access (as we hold the GIL for this
+  // interpreter)
+  TAGGED_BY_US,
+  // Someone else tagged this.  We can't use this TensorImpl from Python.
+  TAGGED_BY_OTHER,
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/PyObjectSlot.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/PyObjectSlot.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b9bcf1e4a1c353c84d9e8273b12ee2b8db5c076
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/PyObjectSlot.h
@@ -0,0 +1,190 @@
+#pragma once
+
+#include <c10/core/impl/HermeticPyObjectTLS.h>
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/util/python_stub.h>
+#include <optional>
+
+#include <atomic>
+
+namespace c10::impl {
+
+struct C10_API PyObjectSlot {
+ public:
+  PyObjectSlot();
+
+  ~PyObjectSlot();
+
+  void maybe_destroy_pyobj();
+
+  // Associate the TensorImpl with the specified PyObject, and, if necessary,
+  // also tag the interpreter.
+  //
+  // NB: This lives in a header so that we can inline away the switch on status
+  //
+  // NB: THIS FUNCTION CAN RAISE AN EXCEPTION.  Make sure to clean up after
+  // PyObject if necessary!
+  void init_pyobj(
+      PyInterpreter* self_interpreter,
+      PyObject* pyobj,
+      PyInterpreterStatus status) {
+    impl::PyInterpreter* expected = nullptr;
+    switch (status) {
+      case impl::PyInterpreterStatus::DEFINITELY_UNINITIALIZED:
+        // caller guarantees there is no multithreaded access; if there is
+        // no data race OK to do a relaxed store
+        pyobj_interpreter_.store(self_interpreter, std::memory_order_relaxed);
+        break;
+      case impl::PyInterpreterStatus::TAGGED_BY_US:
+        // no tagging is necessary, the tag is already correct
+        break;
+      case impl::PyInterpreterStatus::MAYBE_UNINITIALIZED:
+        // attempt to claim this TensorImpl with the specified interpreter
+        // tag
+        if (pyobj_interpreter_.compare_exchange_strong(
+                expected, self_interpreter, std::memory_order_acq_rel)) {
+          break;
+        }
+        // test if, actually, it was already tagged by us!  this situation can't
+        // be caused by a race, but it could be caused by a situation
+        // where someone conservatively tagged the tensor as MAYBE_UNINITIALIZED
+        // (because they didn't pre-check the tag) when actually it was
+        // owned by the interpreter
+        if (expected == self_interpreter) {
+          break;
+        }
+        // fallthrough, we lost the race.  We are guaranteed not to lose the
+        // race with ourself, as calls to init_pyobj with the same interpreter
+        // ID must be sequentialized by the GIL
+        [[fallthrough]];
+      case impl::PyInterpreterStatus::TAGGED_BY_OTHER:
+        TORCH_CHECK(
+            false,
+            "cannot allocate PyObject for Tensor on interpreter ",
+            self_interpreter,
+            " that has already been used by another torch deploy interpreter ",
+            pyobj_interpreter_.load());
+    }
+
+    // we are the ONLY thread that can have gotten to this point.  It is not
+    // possible to conflict with another zero interpreter as access is protected
+    // by GIL
+    // NB: owns_pyobj tag is initially false
+    pyobj_ = pyobj;
+  }
+
+  // Query the PyObject interpreter.  This may return null if there is no
+  // interpreter.  This is racy!
+  PyInterpreter* pyobj_interpreter();
+
+  PyObject* _unchecked_untagged_pyobj() const;
+
+  // Test the interpreter tag.  If tagged for the current interpreter, return
+  // a non-nullopt (but possibly null) PyObject.  If (possibly) untagged,
+  // returns a nullopt.  If it is definitely invalid, raises an error.
+  //
+  // If `ignore_hermetic_tls` is false and this function is called from a
+  // hermetic context (ie, `HermeticPyObjectTLS::get_state()` is true), then
+  // nullopt is returned. If `ignore_hermetic_tls` is true, then the hermetic
+  // context is ignored, allowing you to check the interpreter tag of a
+  // nonhermetic PyObject from within a hermetic context. This is necessary
+  // because there are some cases where the deallocator function of a
+  // nonhermetic PyObject is called from within a hermetic context, so it must
+  // be properly treated as a nonhermetic PyObject.
+  //
+  // NB: this lives in header so that we can avoid actually creating the
+  // std::optional
+  std::optional<PyObject*> check_pyobj(
+      PyInterpreter* self_interpreter,
+      bool ignore_hermetic_tls = false) const {
+    // Note [Memory ordering on Python interpreter tag]
+    impl::PyInterpreter* interpreter =
+        pyobj_interpreter_.load(std::memory_order_acquire);
+    if (interpreter == nullptr) {
+      // NB: This never returns DEFINITELY_UNINITIALIZED because there is
+      // always the possibility that another thread races to initialize
+      // after we query here.  The only time when we can conclude a tensor
+      // is definitely uninitialized is when we have just allocated it and
+      // it cannot have escaped to other threads yet
+      return std::nullopt;
+    } else if (interpreter == self_interpreter) {
+      // NB: pyobj_ could still be null!
+      if (!ignore_hermetic_tls && c10::impl::HermeticPyObjectTLS::get_state()) {
+        return std::nullopt;
+      } else {
+        return _unchecked_untagged_pyobj();
+      }
+    } else {
+      TORCH_CHECK(
+          false,
+          "cannot access PyObject for Tensor on interpreter ",
+          (*self_interpreter)->name(),
+          " that has already been used by another torch deploy interpreter ",
+          (*pyobj_interpreter_.load())->name());
+    }
+  }
+
+  // Clear the PyObject field for an interpreter, in situations where we
+  // statically know the tensor is tagged with our interpreter.
+  void unchecked_clear_pyobj(PyInterpreter* interpreter);
+
+  PyInterpreter& load_pyobj_interpreter() const;
+
+  // Check if the PyObjectSlot's interpreter is the same as the specified
+  // interpreter
+  bool check_interpreter(PyInterpreter* interpreter);
+
+  // Check if the PyObjectSlot is holding a PyObject, owned or non-owned
+  bool has_pyobj_nonhermetic();
+
+  bool owns_pyobj();
+
+  void set_owns_pyobj(bool b);
+
+ private:
+  // This field contains the interpreter tag for this object.  See
+  // Note [Python interpreter tag] for general context
+  //
+  // Note [Memory ordering on Python interpreter tag]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // What memory_order do we need when accessing this atomic?  We don't
+  // need a single total modification order (as provided by
+  // memory_order_seq_cst) as pyobj_interpreter_ is monotonic: it can only
+  // transition from -1 to some positive integer and never changes afterwards.
+  // Because there is only one modification, it trivially already has a total
+  // modification order (e.g., we don't need fences or locked instructions on
+  // x86)
+  //
+  // In fact, one could make a reasonable argument that relaxed reads are OK,
+  // due to the presence of external locking (GIL) to ensure that interactions
+  // with other data structures are still correctly synchronized, so that
+  // we fall in the "Single-Location Data Structures" case as described in
+  // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
+  // However, on x86, it doesn't matter if I use acquire or relaxed on the load
+  // as I get the same assembly in both cases.  So I just use the more
+  // conservative acquire (which will impede compiler optimizations but I don't
+  // care)
+  std::atomic<PyInterpreter*> pyobj_interpreter_;
+
+  // This field contains a reference to a PyObject representing this Tensor.
+  // If pyobj is nullptr, when we transfer Tensor to Python, we allocate a new
+  // PyObject for it and set this field.  This field does not have to be
+  // protected by an atomic as it is only allowed to be accessed when you hold
+  // the GIL, or during destruction of the tensor.
+  //
+  // When a PyObject dies, you are obligated to clear this field
+  // (otherwise, you will try to use-after-free the pyobj); this currently
+  // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp
+  //
+  // NB: Ordinarily, this should not be a strong reference, as if the
+  // PyObject owns the Tensor, this would create a reference cycle.
+  // However, sometimes this ownership flips.  To track who owns
+  // who, this has a single pointer tag indicating whether or not the
+  // C++ object owns the PyObject (the common case, zero, means PyObject
+  // owns the C++ object); see _unchecked_untagged_pyobj for raw access
+  // or check_pyobj for checked access.  See references to PyObject
+  // resurrection in torch/csrc/autograd/python_variable.cpp
+  PyObject* pyobj_;
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/PythonDispatcherTLS.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/PythonDispatcherTLS.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b91aab686eca123175f83877cfe8e9d8bd98487
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/PythonDispatcherTLS.h
@@ -0,0 +1,29 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Export.h>
+
+namespace c10::impl {
+
+struct C10_API PythonDispatcherTLS {
+  static void set_state(PyInterpreter* state);
+  static PyInterpreter* get_state();
+  static void reset_state();
+};
+
+struct C10_API DisablePythonDispatcher {
+  DisablePythonDispatcher() : old_(PythonDispatcherTLS::get_state()) {
+    PythonDispatcherTLS::set_state({});
+  }
+
+  DisablePythonDispatcher(DisablePythonDispatcher&& other) = delete;
+  DisablePythonDispatcher(const DisablePythonDispatcher&) = delete;
+  DisablePythonDispatcher& operator=(const DisablePythonDispatcher&) = delete;
+  DisablePythonDispatcher& operator=(DisablePythonDispatcher&&) = delete;
+  ~DisablePythonDispatcher() {
+    PythonDispatcherTLS::set_state(old_);
+  }
+  PyInterpreter* old_;
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/SizesAndStrides.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/SizesAndStrides.h
new file mode 100644
index 0000000000000000000000000000000000000000..827da598d8e5a77384c5f24af6e6dd3d08cbb2fd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/SizesAndStrides.h
@@ -0,0 +1,315 @@
+#pragma once
+
+#include <algorithm>
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/SmallVector.h>
+
+#define C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE 5
+
+namespace c10::impl {
+
+// Packed container for TensorImpl sizes and strides.
+// This design improves on the previous approach of using a pair of
+// c10::SmallVector<int64_t, 5> by specializing for the operations we
+// actually use and enforcing that the number of sizes is the same as
+// the number of strides. The memory layout is as follows:
+//
+// 1 size_t for the size
+// 5 eightbytes of inline sizes and 5 eightbytes of inline strides, OR pointer
+// to out-of-line array
+class C10_API SizesAndStrides {
+ public:
+  // TODO: different iterator types for sizes & strides to prevent
+  // mixing the two accidentally.
+  using sizes_iterator = int64_t*;
+  using sizes_const_iterator = const int64_t*;
+  using strides_iterator = int64_t*;
+  using strides_const_iterator = const int64_t*;
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  SizesAndStrides() {
+    size_at_unchecked(0) = 0;
+    stride_at_unchecked(0) = 1;
+  }
+
+  ~SizesAndStrides() {
+    if (C10_UNLIKELY(!isInline())) {
+      // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+      free(outOfLineStorage_);
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  SizesAndStrides(const SizesAndStrides& rhs) : size_(rhs.size_) {
+    if (C10_LIKELY(rhs.isInline())) {
+      copyDataInline(rhs);
+    } else {
+      allocateOutOfLineStorage(size_);
+      copyDataOutline(rhs);
+    }
+  }
+
+  SizesAndStrides& operator=(const SizesAndStrides& rhs) {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_LIKELY(rhs.isInline())) {
+      if (C10_UNLIKELY(!isInline())) {
+        // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+        free(outOfLineStorage_);
+      }
+      copyDataInline(rhs);
+    } else {
+      if (isInline()) {
+        allocateOutOfLineStorage(rhs.size_);
+      } else {
+        resizeOutOfLineStorage(rhs.size_);
+      }
+      copyDataOutline(rhs);
+    }
+    size_ = rhs.size_;
+    return *this;
+  }
+
+  // Move from rhs. rhs.size() == 0 afterwards.
+  SizesAndStrides(SizesAndStrides&& rhs) noexcept : size_(rhs.size_) {
+    if (C10_LIKELY(isInline())) {
+      memcpy(inlineStorage_, rhs.inlineStorage_, sizeof(inlineStorage_));
+    } else {
+      outOfLineStorage_ = rhs.outOfLineStorage_;
+      rhs.outOfLineStorage_ = nullptr;
+    }
+
+    rhs.size_ = 0;
+  }
+
+  // Move from rhs. rhs.size() == 0 afterwards.
+  SizesAndStrides& operator=(SizesAndStrides&& rhs) noexcept {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_LIKELY(rhs.isInline())) {
+      if (C10_UNLIKELY(!isInline())) {
+        // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+        free(outOfLineStorage_);
+      }
+      copyDataInline(rhs);
+    } else {
+      // They're outline. We're going to steal their vector.
+      if (!isInline()) {
+        // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+        free(outOfLineStorage_);
+      }
+      outOfLineStorage_ = rhs.outOfLineStorage_;
+      rhs.outOfLineStorage_ = nullptr;
+    }
+    size_ = rhs.size_;
+    rhs.size_ = 0;
+
+    return *this;
+  }
+
+  size_t size() const noexcept {
+    return size_;
+  }
+
+  const int64_t* sizes_data() const noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[0];
+    } else {
+      return &outOfLineStorage_[0];
+    }
+  }
+
+  int64_t* sizes_data() noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[0];
+    } else {
+      return &outOfLineStorage_[0];
+    }
+  }
+
+  sizes_const_iterator sizes_begin() const noexcept {
+    return sizes_data();
+  }
+
+  sizes_iterator sizes_begin() noexcept {
+    return sizes_data();
+  }
+
+  sizes_const_iterator sizes_end() const noexcept {
+    return sizes_begin() + size();
+  }
+
+  sizes_iterator sizes_end() noexcept {
+    return sizes_begin() + size();
+  }
+
+  IntArrayRef sizes_arrayref() const noexcept {
+    return IntArrayRef{sizes_data(), size()};
+  }
+
+  void set_sizes(IntArrayRef newSizes) {
+    resize(newSizes.size());
+    std::copy(newSizes.begin(), newSizes.end(), sizes_begin());
+  }
+
+  void set_strides(IntArrayRef strides) {
+    TORCH_INTERNAL_ASSERT(strides.size() == size());
+    std::copy(strides.begin(), strides.end(), strides_begin());
+  }
+
+  const int64_t* strides_data() const noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE];
+    } else {
+      return &outOfLineStorage_[size()];
+    }
+  }
+
+  int64_t* strides_data() noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE];
+    } else {
+      return &outOfLineStorage_[size()];
+    }
+  }
+
+  strides_const_iterator strides_begin() const noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE];
+    } else {
+      return &outOfLineStorage_[size()];
+    }
+  }
+
+  strides_iterator strides_begin() noexcept {
+    if (C10_LIKELY(isInline())) {
+      return &inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE];
+    } else {
+      return &outOfLineStorage_[size()];
+    }
+  }
+
+  strides_const_iterator strides_end() const noexcept {
+    return strides_begin() + size();
+  }
+
+  strides_iterator strides_end() noexcept {
+    return strides_begin() + size();
+  }
+
+  IntArrayRef strides_arrayref() const noexcept {
+    return IntArrayRef{strides_data(), size()};
+  }
+
+  // Size accessors.
+  int64_t size_at(size_t idx) const noexcept {
+    assert(idx < size());
+    return sizes_data()[idx];
+  }
+
+  int64_t& size_at(size_t idx) noexcept {
+    assert(idx < size());
+    return sizes_data()[idx];
+  }
+
+  int64_t size_at_unchecked(size_t idx) const noexcept {
+    return sizes_data()[idx];
+  }
+
+  int64_t& size_at_unchecked(size_t idx) noexcept {
+    return sizes_data()[idx];
+  }
+
+  // Size accessors.
+  int64_t stride_at(size_t idx) const noexcept {
+    assert(idx < size());
+    return strides_data()[idx];
+  }
+
+  int64_t& stride_at(size_t idx) noexcept {
+    assert(idx < size());
+    return strides_data()[idx];
+  }
+
+  int64_t stride_at_unchecked(size_t idx) const noexcept {
+    return strides_data()[idx];
+  }
+
+  int64_t& stride_at_unchecked(size_t idx) noexcept {
+    return strides_data()[idx];
+  }
+
+  void resize(size_t newSize) {
+    const auto oldSize = size();
+    if (newSize == oldSize) {
+      return;
+    }
+    if (C10_LIKELY(
+            newSize <= C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE && isInline())) {
+      if (oldSize < newSize) {
+        const auto bytesToZero =
+            (newSize - oldSize) * sizeof(inlineStorage_[0]);
+        memset(&inlineStorage_[oldSize], 0, bytesToZero);
+        memset(
+            &inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE + oldSize],
+            0,
+            bytesToZero);
+      }
+      size_ = newSize;
+    } else {
+      resizeSlowPath(newSize, oldSize);
+    }
+  }
+
+  void resizeSlowPath(size_t newSize, size_t oldSize);
+
+ private:
+  bool isInline() const noexcept {
+    return size_ <= C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE;
+  }
+
+  void copyDataInline(const SizesAndStrides& rhs) {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(rhs.isInline());
+    memcpy(inlineStorage_, rhs.inlineStorage_, sizeof(inlineStorage_));
+  }
+
+  static size_t storageBytes(size_t size) noexcept {
+    return size * 2 * sizeof(int64_t);
+  }
+
+  void allocateOutOfLineStorage(size_t size) {
+    // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+    outOfLineStorage_ = static_cast<int64_t*>(malloc(storageBytes(size)));
+    TORCH_CHECK(
+        outOfLineStorage_,
+        "Could not allocate memory for Tensor SizesAndStrides!");
+  }
+
+  void resizeOutOfLineStorage(size_t newSize) {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!isInline());
+    outOfLineStorage_ = static_cast<int64_t*>(
+        // NOLINTNEXTLINE(cppcoreguidelines-no-malloc)
+        realloc(outOfLineStorage_, storageBytes(newSize)));
+    TORCH_CHECK(
+        outOfLineStorage_,
+        "Could not allocate memory for Tensor SizesAndStrides!");
+  }
+
+  void copyDataOutline(const SizesAndStrides& rhs) noexcept {
+    memcpy(outOfLineStorage_, rhs.outOfLineStorage_, storageBytes(rhs.size_));
+  }
+
+  size_t size_{1};
+  union {
+    int64_t* outOfLineStorage_;
+    // NOLINTNEXTLINE(*c-array*)
+    int64_t inlineStorage_[C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE * 2]{};
+  };
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/TorchDispatchModeTLS.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/TorchDispatchModeTLS.h
new file mode 100644
index 0000000000000000000000000000000000000000..7179d52c351621083706f4d4ce53e35649c52706
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/TorchDispatchModeTLS.h
@@ -0,0 +1,67 @@
+#pragma once
+
+#include <c10/core/SafePyObject.h>
+#include <c10/macros/Export.h>
+
+namespace c10::impl {
+
+enum class TorchDispatchModeKey : int8_t {
+  FAKE,
+  PROXY,
+  FUNCTIONAL,
+  NUM_MODE_KEYS
+};
+
+using PyObject_TorchDispatchMode = SafePyObjectT<TorchDispatchModeKey>;
+
+struct C10_API TorchDispatchModeTLS {
+  // This API is NOT invariant safe.
+  // It must not take in an infra mode that uses TorchDispatchModeKey
+  // If you're pushing an infra mode onto the stack, we expect
+  // you to use set_mode
+  static void push_non_infra_mode_onto_stack(
+      std::shared_ptr<PyObject_TorchDispatchMode> mode);
+  // Pops the top mode of the stack,
+  // giving precedence to user modes before attempting to pop
+  // any infra modes
+  static const std::shared_ptr<PyObject_TorchDispatchMode> pop_stack();
+  // Returns the highest-priority infra mode on the stack,
+  // along with its mode key.
+  static const std::
+      tuple<std::shared_ptr<PyObject_TorchDispatchMode>, TorchDispatchModeKey>
+      pop_highest_infra_mode();
+
+  static const std::shared_ptr<PyObject_TorchDispatchMode>& get_stack_at(
+      int64_t idx);
+  static int64_t stack_len();
+
+  static const std::optional<std::shared_ptr<PyObject_TorchDispatchMode>>
+  get_mode(TorchDispatchModeKey mode_key);
+  static const std::optional<std::shared_ptr<PyObject_TorchDispatchMode>>
+  unset_mode(TorchDispatchModeKey mode_key);
+  static void set_mode(
+      const std::shared_ptr<PyObject_TorchDispatchMode>& mode,
+      TorchDispatchModeKey mode_key);
+
+  static const TorchDispatchModeTLS& get_state();
+  static void set_state(TorchDispatchModeTLS state);
+
+  static bool any_modes_set(bool skip_infra_modes = false);
+
+ private:
+  std::vector<std::shared_ptr<PyObject_TorchDispatchMode>> stack_;
+  // Users are allowed to push multiple ProxyTorchDispatchMode objects onto the
+  // stack
+  // However, we only allow a single FakeTensorMode onto the stack at a time
+  // (Pushing additional FakeTensorModes onto the stack is a no-op)
+  std::array<
+      std::optional<std::shared_ptr<PyObject_TorchDispatchMode>>,
+      static_cast<size_t>(TorchDispatchModeKey::NUM_MODE_KEYS)>
+      infra_modes_;
+};
+
+C10_API bool dispatch_mode_enabled();
+
+C10_API std::string to_string(TorchDispatchModeKey mode_key);
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/VirtualGuardImpl.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/VirtualGuardImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5e4ab3e01bd620e27db0deed41adbd4fe4a13f1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/VirtualGuardImpl.h
@@ -0,0 +1,108 @@
+#pragma once
+
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+
+namespace c10::impl {
+
+/**
+ * An implementation of DeviceGuardImplInterface which delegates
+ * to virtual dispatch on the DeviceGuardImpl registry.
+ */
+class VirtualGuardImpl final : public DeviceGuardImplInterface {
+ public:
+  VirtualGuardImpl(DeviceType device_type)
+      : impl_(getDeviceGuardImpl(device_type)) {}
+  // This constructor exists purely for testing
+  VirtualGuardImpl(const DeviceGuardImplInterface* impl) : impl_(impl) {}
+
+  // Copying and moving is OK!
+  VirtualGuardImpl(const VirtualGuardImpl&) = default;
+  VirtualGuardImpl& operator=(const VirtualGuardImpl&) = default;
+  VirtualGuardImpl(VirtualGuardImpl&&) noexcept = default;
+  VirtualGuardImpl& operator=(VirtualGuardImpl&&) noexcept = default;
+  ~VirtualGuardImpl() override = default;
+
+  DeviceType type() const override {
+    return impl_->type();
+  }
+  Device exchangeDevice(Device d) const override {
+    return impl_->exchangeDevice(d);
+  }
+  Device getDevice() const override {
+    return impl_->getDevice();
+  }
+  void setDevice(Device d) const override {
+    impl_->setDevice(d);
+  }
+  void uncheckedSetDevice(Device d) const noexcept override {
+    impl_->uncheckedSetDevice(d);
+  }
+  Stream getStream(Device d) const noexcept override {
+    return impl_->getStream(d);
+  }
+  Stream getNewStream(Device d, int priority = 0) const override {
+    return impl_->getNewStream(d, priority);
+  }
+  Stream getDefaultStream(Device d) const override {
+    return impl_->getDefaultStream(d);
+  }
+  Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false)
+      const override {
+    return impl_->getStreamFromGlobalPool(d, isHighPriority);
+  }
+  Stream exchangeStream(Stream s) const noexcept override {
+    return impl_->exchangeStream(s);
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return impl_->deviceCount();
+  }
+
+  // Event functions
+  void record(
+      void** event,
+      const Stream& stream,
+      const DeviceIndex device_index,
+      const EventFlag flag) const override {
+    impl_->record(event, stream, device_index, flag);
+  }
+  void block(void* event, const Stream& stream) const override {
+    impl_->block(event, stream);
+  }
+  bool queryEvent(void* event) const override {
+    return impl_->queryEvent(event);
+  }
+  void destroyEvent(void* event, const DeviceIndex device_index)
+      const noexcept override {
+    impl_->destroyEvent(event, device_index);
+  }
+
+  bool queryStream(const Stream& stream) const override {
+    return impl_->queryStream(stream);
+  }
+  void synchronizeStream(const Stream& stream) const override {
+    impl_->synchronizeStream(stream);
+  }
+
+  void recordDataPtrOnStream(const c10::DataPtr& data_ptr, const Stream& stream)
+      const override {
+    impl_->recordDataPtrOnStream(data_ptr, stream);
+  }
+
+  double elapsedTime(void* event1, void* event2, const DeviceIndex device_index)
+      const override {
+    return impl_->elapsedTime(event1, event2, device_index);
+  }
+
+  void synchronizeEvent(void* event) const override {
+    return impl_->synchronizeEvent(event);
+  }
+
+  void synchronizeDevice(const DeviceIndex device_index) const override {
+    return impl_->synchronizeDevice(device_index);
+  }
+
+ private:
+  const DeviceGuardImplInterface* impl_ = nullptr;
+};
+
+} // namespace c10::impl
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/impl/alloc_cpu.h b/lib/python3.11/site-packages/torch/include/c10/core/impl/alloc_cpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d506acf392f4497583682c2e7ef1aed01ebce25
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/impl/alloc_cpu.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+
+#include <cstddef>
+
+namespace c10 {
+
+C10_API void* alloc_cpu(size_t nbytes);
+C10_API void free_cpu(void* data);
+
+#ifdef USE_MIMALLOC_ON_MKL
+namespace mi_malloc_wrapper {
+C10_API void* c10_mi_malloc(size_t size);
+C10_API void* c10_mi_calloc(size_t count, size_t size);
+C10_API void* c10_mi_realloc(void* p, size_t newsize);
+C10_API void* c10_mi_malloc_aligned(size_t size, size_t alignment);
+C10_API void c10_mi_free(void* p);
+} // namespace mi_malloc_wrapper
+#endif
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/core/thread_pool.h b/lib/python3.11/site-packages/torch/include/c10/core/thread_pool.h
new file mode 100644
index 0000000000000000000000000000000000000000..805e41fdb812e14d2ad28660498ee662a59050ea
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/core/thread_pool.h
@@ -0,0 +1,120 @@
+#pragma once
+
+#include <atomic>
+#include <condition_variable>
+#include <cstddef>
+#include <functional>
+#include <mutex>
+#include <queue>
+#include <thread>
+#include <utility>
+#include <vector>
+
+#include <c10/macros/Export.h>
+#include <c10/util/Registry.h>
+#include <c10/util/numa.h>
+#include <c10/util/thread_name.h>
+
+namespace c10 {
+
+class C10_API TaskThreadPoolBase {
+ public:
+  virtual void run(std::function<void()> func) = 0;
+
+  virtual size_t size() const = 0;
+
+  /**
+   * The number of available (i.e. idle) threads in this thread pool.
+   */
+  virtual size_t numAvailable() const = 0;
+
+  /**
+   * Check if the current thread is from the thread pool.
+   */
+  virtual bool inThreadPool() const = 0;
+
+  virtual ~TaskThreadPoolBase() noexcept = default;
+
+  static size_t defaultNumThreads();
+};
+
+class C10_API ThreadPool : public c10::TaskThreadPoolBase {
+ protected:
+  struct task_element_t {
+    bool run_with_id;
+    // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
+    const std::function<void()> no_id;
+    // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
+    const std::function<void(std::size_t)> with_id;
+
+    explicit task_element_t(std::function<void()> f)
+        : run_with_id(false), no_id(std::move(f)), with_id(nullptr) {}
+    explicit task_element_t(std::function<void(std::size_t)> f)
+        : run_with_id(true), no_id(nullptr), with_id(std::move(f)) {}
+  };
+
+  std::queue<task_element_t> tasks_;
+  std::vector<std::thread> threads_;
+  mutable std::mutex mutex_;
+  std::condition_variable condition_;
+  std::condition_variable completed_;
+  std::atomic_bool running_;
+  bool complete_;
+  std::size_t available_;
+  std::size_t total_;
+  int numa_node_id_;
+
+ public:
+  ThreadPool() = delete;
+
+  explicit ThreadPool(
+      int pool_size,
+      int numa_node_id = -1,
+      const std::function<void()>& init_thread = nullptr);
+
+  ~ThreadPool() override;
+
+  size_t size() const override;
+
+  size_t numAvailable() const override;
+
+  bool inThreadPool() const override;
+
+  void run(std::function<void()> func) override;
+
+  template <typename Task>
+  void runTaskWithID(Task task) {
+    std::unique_lock<std::mutex> lock(mutex_);
+
+    // Set task and signal condition variable so that a worker thread will
+    // wake up and use the task.
+    tasks_.emplace(static_cast<std::function<void(std::size_t)>>(task));
+    complete_ = false;
+    condition_.notify_one();
+  }
+
+  /// @brief Wait for queue to be empty
+  void waitWorkComplete();
+
+ private:
+  // @brief Entry point for pool threads.
+  void main_loop(std::size_t index);
+};
+
+class C10_API TaskThreadPool : public c10::ThreadPool {
+ public:
+  explicit TaskThreadPool(int pool_size, int numa_node_id = -1)
+      : ThreadPool(pool_size, numa_node_id, [numa_node_id]() {
+          setThreadName("CaffeTaskThread");
+          NUMABind(numa_node_id);
+        }) {}
+};
+
+C10_DECLARE_SHARED_REGISTRY(
+    ThreadPoolRegistry,
+    TaskThreadPoolBase,
+    int,
+    int,
+    bool);
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAlgorithm.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAlgorithm.h
new file mode 100644
index 0000000000000000000000000000000000000000..cdb4d7adf94b9eaee6a497a28e6f48702a1983bb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAlgorithm.h
@@ -0,0 +1,31 @@
+#ifdef THRUST_DEVICE_LOWER_BOUND_WORKS
+#include <thrust/binary_search.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#endif
+namespace c10::cuda {
+#ifdef THRUST_DEVICE_LOWER_BOUND_WORKS
+template <typename Iter, typename Scalar>
+__forceinline__ __device__ Iter
+lower_bound(Iter start, Iter end, Scalar value) {
+  return thrust::lower_bound(thrust::device, start, end, value);
+}
+#else
+// thrust::lower_bound is broken on device, see
+// https://github.com/NVIDIA/thrust/issues/1734 Implementation inspired by
+// https://github.com/pytorch/pytorch/blob/805120ab572efef66425c9f595d9c6c464383336/aten/src/ATen/native/cuda/Bucketization.cu#L28
+template <typename Iter, typename Scalar>
+__device__ Iter lower_bound(Iter start, Iter end, Scalar value) {
+  while (start < end) {
+    auto mid = start + ((end - start) >> 1);
+    if (*mid < value) {
+      start = mid + 1;
+    } else {
+      end = mid;
+    }
+  }
+  return end;
+}
+#endif // THRUST_DEVICE_LOWER_BOUND_WORKS
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAllocatorConfig.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAllocatorConfig.h
new file mode 100644
index 0000000000000000000000000000000000000000..876bffcf98527a055d76cf54430344b9cb67ba68
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAAllocatorConfig.h
@@ -0,0 +1,140 @@
+#pragma once
+
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/util/Exception.h>
+
+#include <atomic>
+#include <cstddef>
+#include <cstdlib>
+#include <mutex>
+#include <string>
+#include <vector>
+
+namespace c10::cuda::CUDACachingAllocator {
+
+// Environment config parser
+class C10_CUDA_API CUDAAllocatorConfig {
+ public:
+  static size_t max_split_size() {
+    return instance().m_max_split_size;
+  }
+  static double garbage_collection_threshold() {
+    return instance().m_garbage_collection_threshold;
+  }
+
+  static bool expandable_segments() {
+#ifndef PYTORCH_C10_DRIVER_API_SUPPORTED
+    if (instance().m_expandable_segments) {
+      TORCH_WARN_ONCE("expandable_segments not supported on this platform")
+    }
+    return false;
+#else
+    return instance().m_expandable_segments;
+#endif
+  }
+
+  static bool release_lock_on_cudamalloc() {
+    return instance().m_release_lock_on_cudamalloc;
+  }
+
+  /** Pinned memory allocator settings */
+  static bool pinned_use_cuda_host_register() {
+    return instance().m_pinned_use_cuda_host_register;
+  }
+
+  static size_t pinned_num_register_threads() {
+    return instance().m_pinned_num_register_threads;
+  }
+
+  static bool pinned_use_background_threads() {
+    return instance().m_pinned_use_background_threads;
+  }
+
+  static size_t pinned_max_register_threads() {
+    // Based on the benchmark results, we see better allocation performance
+    // with 8 threads. However on future systems, we may need more threads
+    // and limiting this to 128 threads.
+    return 128;
+  }
+
+  // This is used to round-up allocation size to nearest power of 2 divisions.
+  // More description below in function roundup_power2_next_division
+  // As ane example, if we want 4 divisions between 2's power, this can be done
+  // using env variable: PYTORCH_CUDA_ALLOC_CONF=roundup_power2_divisions:4
+  static size_t roundup_power2_divisions(size_t size);
+
+  static std::vector<size_t> roundup_power2_divisions() {
+    return instance().m_roundup_power2_divisions;
+  }
+
+  static size_t max_non_split_rounding_size() {
+    return instance().m_max_non_split_rounding_size;
+  }
+
+  static std::string last_allocator_settings() {
+    std::lock_guard<std::mutex> lock(
+        instance().m_last_allocator_settings_mutex);
+    return instance().m_last_allocator_settings;
+  }
+
+  static CUDAAllocatorConfig& instance() {
+    static CUDAAllocatorConfig* s_instance = ([]() {
+      auto inst = new CUDAAllocatorConfig();
+      const char* env = getenv("PYTORCH_CUDA_ALLOC_CONF");
+      inst->parseArgs(env);
+      return inst;
+    })();
+    return *s_instance;
+  }
+
+  void parseArgs(const char* env);
+
+ private:
+  CUDAAllocatorConfig();
+
+  static void lexArgs(const char* env, std::vector<std::string>& config);
+  static void consumeToken(
+      const std::vector<std::string>& config,
+      size_t i,
+      const char c);
+  size_t parseMaxSplitSize(const std::vector<std::string>& config, size_t i);
+  size_t parseMaxNonSplitRoundingSize(
+      const std::vector<std::string>& config,
+      size_t i);
+  size_t parseGarbageCollectionThreshold(
+      const std::vector<std::string>& config,
+      size_t i);
+  size_t parseRoundUpPower2Divisions(
+      const std::vector<std::string>& config,
+      size_t i);
+  size_t parseAllocatorConfig(
+      const std::vector<std::string>& config,
+      size_t i,
+      bool& used_cudaMallocAsync);
+  size_t parsePinnedUseCudaHostRegister(
+      const std::vector<std::string>& config,
+      size_t i);
+  size_t parsePinnedNumRegisterThreads(
+      const std::vector<std::string>& config,
+      size_t i);
+  size_t parsePinnedUseBackgroundThreads(
+      const std::vector<std::string>& config,
+      size_t i);
+
+  std::atomic<size_t> m_max_split_size;
+  std::atomic<size_t> m_max_non_split_rounding_size;
+  std::vector<size_t> m_roundup_power2_divisions;
+  std::atomic<double> m_garbage_collection_threshold;
+  std::atomic<size_t> m_pinned_num_register_threads;
+  std::atomic<bool> m_expandable_segments;
+  std::atomic<bool> m_release_lock_on_cudamalloc;
+  std::atomic<bool> m_pinned_use_cuda_host_register;
+  std::atomic<bool> m_pinned_use_background_threads;
+  std::string m_last_allocator_settings;
+  std::mutex m_last_allocator_settings_mutex;
+};
+
+// General caching allocator utilities
+C10_CUDA_API void setAllocatorSettings(const std::string& env);
+
+} // namespace c10::cuda::CUDACachingAllocator
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDACachingAllocator.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDACachingAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..df31a11da785d57d8f177f1768c4e9fdf4215f1c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDACachingAllocator.h
@@ -0,0 +1,548 @@
+#pragma once
+
+#include <c10/core/CachingDeviceAllocator.h>
+#include <c10/cuda/CUDAGraphsC10Utils.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/cuda/CUDAStream.h>
+#include <c10/util/ApproximateClock.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Registry.h>
+
+#include <atomic>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <memory>
+#include <string>
+#include <unordered_set>
+#include <utility>
+
+namespace c10 {
+
+// Caching allocator will execute every registered callback if it unable to find
+// block inside of already allocated area.
+class C10_CUDA_API FreeMemoryCallback {
+ public:
+  virtual ~FreeMemoryCallback() = default;
+  virtual bool Execute() = 0;
+};
+
+C10_DECLARE_REGISTRY(FreeCudaMemoryCallbacksRegistry, FreeMemoryCallback);
+#define REGISTER_FREE_MEMORY_CALLBACK(name, ...) \
+  C10_REGISTER_CLASS(FreeCudaMemoryCallbacksRegistry, name, __VA_ARGS__)
+} // namespace c10
+  //
+// TODO: Turn this into an honest to goodness class. I briefly attempted to do
+// this, but it was a bit irritating to figure out how to also correctly
+// apply pimpl pattern so I didn't have to leak any internal implementation
+// details in the header (CUDACachingAllocator could be made a pimpl, but
+// you also need to appropriately define a class which is a subclass
+// of Allocator. Not impossible, but required a bit more surgery than
+// I wanted to do at the time.)
+//
+// Why is this using a namespace rather than old-style THCCachingAllocator_
+// prefix?  Mostly because it made the HIPify rules easier to write; _ is
+// not counted as a word boundary, so you would otherwise have to list each
+// of these functions.
+
+namespace c10::cuda::CUDACachingAllocator {
+
+// Preserved only for BC reasons
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using c10::CachingDeviceAllocator::DeviceStats;
+
+extern const size_t kLargeBuffer;
+
+typedef std::shared_ptr<GatheredContext> (*CreateContextFn)();
+
+// Struct containing info of an allocation block (i.e. a fractional part of a
+// cudaMalloc)..
+struct BlockInfo {
+  size_t size = 0;
+  size_t requested_size = 0;
+  int32_t gc_counter = 0;
+  bool allocated = false;
+  bool active = false;
+  std::shared_ptr<GatheredContext>
+      context_when_allocated; // per-watcher context
+};
+
+// Struct containing info of a memory segment (i.e. one contiguous cudaMalloc).
+struct SegmentInfo {
+  c10::DeviceIndex device = 0;
+  size_t address = 0;
+  size_t total_size = 0;
+  size_t requested_size = 0; // unrounded, actually requested size
+  size_t allocated_size = 0;
+  size_t active_size = 0;
+  cudaStream_t stream = nullptr;
+  bool is_large = false;
+  bool is_expandable = false;
+  MempoolId_t owner_private_pool_id = {0, 0};
+  std::vector<BlockInfo> blocks;
+  std::shared_ptr<GatheredContext> context_when_allocated;
+};
+
+struct AllocatorState {
+  virtual ~AllocatorState() = default;
+};
+
+union trace_time_ {
+  time_t t_;
+  approx_time_t approx_t_;
+};
+
+struct TraceEntry {
+  enum Action {
+    ALLOC, // API made to the caching allocator for new memory
+    FREE_REQUESTED, // API call made to the caching allocator to free memory
+    FREE_COMPLETED, // The allocator might have to delay a free because
+                    // it is still in use on another stream via record_stream
+                    // This event is generated when a free actually completes.
+    SEGMENT_ALLOC, // a call to cudaMalloc to get more memory from the OS
+    SEGMENT_FREE, // a call to cudaFree to return memory to the OS (e.g. to
+                  // defragment or empty_caches)
+    SEGMENT_MAP, // a call to cuMemMap (used with expandable_segments)
+    SEGMENT_UNMAP, // unmap part of a segment (used with expandable segments)
+    SNAPSHOT, // a call to snapshot, used to correlate memory snapshots to trace
+              // events
+    OOM // the allocator threw an OutOfMemoryError (addr_ is the amount of free
+        // bytes reported by cuda)
+  };
+  TraceEntry(
+      Action action,
+      c10::DeviceIndex device,
+      size_t addr,
+      size_t size,
+      cudaStream_t stream,
+      approx_time_t time,
+      std::shared_ptr<GatheredContext> context = nullptr)
+      : action_(action),
+        device_(device),
+        addr_(addr),
+        context_(std::move(context)),
+        stream_(stream),
+        size_(size) {
+    time_.approx_t_ = time;
+  }
+  Action action_;
+  c10::DeviceIndex device_;
+  size_t addr_; // for OOM, this is the amount of free bytes reported by cuda
+  std::shared_ptr<GatheredContext> context_;
+  cudaStream_t stream_{};
+  size_t size_;
+  trace_time_ time_{};
+};
+
+// Calls made by record_function will save annotations
+struct AnnotationEntry {
+  AnnotationEntry(c10::DeviceIndex device, approx_time_t time)
+      : device_(device) {
+    time_.approx_t_ = time;
+  }
+
+  void recordUserMetadata(const std::string& name, std::string value) {
+    metadata_[name] = std::move(value);
+  }
+
+  c10::DeviceIndex device_;
+  trace_time_ time_{};
+  std::unordered_map<std::string, std::string> metadata_;
+};
+
+struct AllocatorConfigInfo {
+  double garbage_collection_threshold;
+  size_t max_split_size;
+  size_t pinned_num_register_threads;
+  bool expandable_segments;
+  bool release_lock_on_malloc;
+  bool pinned_use_host_register;
+  std::string last_allocator_settings;
+  std::vector<size_t> roundup_power2_divisions;
+};
+
+struct SnapshotInfo {
+  std::vector<SegmentInfo> segments;
+  std::vector<std::vector<TraceEntry>> device_traces;
+  std::vector<AnnotationEntry> external_annotations;
+  AllocatorConfigInfo config_metadata;
+};
+
+// returns the pointers freed in the pool
+// and the pointers allocated. Note: a pointer
+// may appear in both freed and allocated
+struct CheckpointDelta {
+  std::vector<void*> ptrs_freed;
+  std::vector<at::DataPtr> dataptrs_allocd;
+};
+
+enum struct RecordContext {
+  NEVER = 0,
+  STATE = 1, // only keep stacks for active allocations
+  ALLOC = 2, // additionally keep stacks for allocations in the trace history
+  ALL = 3, // additionally record stacks for when something is freed
+};
+
+using OutOfMemoryObserver = std::function<void(
+    int64_t device,
+    size_t allocated,
+    size_t device_total,
+    size_t device_free)>;
+
+using AllocatorTraceTracker = std::function<void(const TraceEntry&)>;
+
+struct ShareableHandle {
+  ptrdiff_t offset;
+  std::string handle;
+};
+
+class CUDAAllocator : public Allocator {
+ public:
+  virtual void* raw_alloc(size_t nbytes) = 0;
+  virtual void* raw_alloc_with_stream(size_t nbytes, cudaStream_t stream) = 0;
+  virtual void raw_delete(void* ptr) = 0;
+  virtual void init(int device_count) = 0;
+  virtual bool initialized() = 0;
+  virtual double getMemoryFraction(c10::DeviceIndex device) = 0;
+  virtual void setMemoryFraction(double fraction, c10::DeviceIndex device) = 0;
+  virtual void emptyCache() = 0;
+  virtual void enable(bool value) = 0;
+  virtual bool isEnabled() const = 0;
+  virtual void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) = 0;
+  virtual void* getBaseAllocation(void* ptr, size_t* size) = 0;
+  virtual void recordStream(const DataPtr&, CUDAStream stream) = 0;
+  virtual c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+      c10::DeviceIndex device) = 0;
+  virtual void resetAccumulatedStats(c10::DeviceIndex device) = 0;
+  virtual void resetPeakStats(c10::DeviceIndex device) = 0;
+  virtual SnapshotInfo snapshot() = 0;
+  virtual void beginAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      std::function<bool(cudaStream_t)> filter) = 0;
+  virtual void endAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id) = 0;
+  virtual void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) = 0;
+  virtual int getPoolUseCount(c10::DeviceIndex device, MempoolId_t mempool_id) {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support getPoolUseCount. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  virtual void ensureExistsAndIncrefPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id) {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support ensureExistsAndIncrefPool. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  // returns true if the allocated blocks are equal to expected live allocations
+  virtual bool checkPoolLiveAllocations(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      const std::unordered_set<void*>& expected_live_allocations) {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support checkPoolLiveAllocations. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  virtual ShareableHandle shareIpcHandle(void* ptr) = 0;
+  virtual std::shared_ptr<void> getIpcDevPtr(std::string handle) = 0;
+  virtual bool isHistoryEnabled() {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support recordHistory. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  virtual void recordHistory(
+      bool enabled,
+      CreateContextFn context_recorder,
+      size_t alloc_trace_max_entries,
+      RecordContext when) = 0;
+  virtual void recordAnnotation(
+      const std::vector<std::pair<std::string, std::string>>& md) {}
+  virtual void attachOutOfMemoryObserver(OutOfMemoryObserver observer) = 0;
+
+  // Attached AllocatorTraceTracker callbacks will be called while the
+  // per-device allocator lock is held. Any additional locks taken from within
+  // the callback must be proven to always have the lock order that never
+  // triggers a deadlock. In particular, Python's GIL may be held when
+  // calling the allocator so it is unsafe to try to acquire the GIL in this
+  // callback.
+  virtual void attachAllocatorTraceTracker(AllocatorTraceTracker tracker) = 0;
+
+  virtual void enablePeerAccess(
+      c10::DeviceIndex dev,
+      c10::DeviceIndex dev_to_access) = 0;
+
+  // memory not allocated from cudaMalloc cannot be copied
+  // across devices using cudaMemcpyAsync if peer to peer access is disabled.
+  // instead it requires cudaMemcpyAsyncPeer
+  //  with P2P Enabled, all combinations work
+  //  with P2P Disabled:
+  //                       cudaMalloc cudaMallocAsync/cuMemMap
+  // cudaMemcpyAsyncPeer   works      works
+  // cudaMemcpyAsync       works      error
+
+  // This function performs chooses to use the Peer version of
+  // memcpy if required based on where the allocated put dst/src.
+  virtual cudaError_t memcpyAsync(
+      void* dst,
+      int dstDevice,
+      const void* src,
+      int srcDevice,
+      size_t count,
+      cudaStream_t stream,
+      bool p2p_enabled) = 0;
+  virtual std::shared_ptr<AllocatorState> getCheckpointState(
+      c10::DeviceIndex device,
+      MempoolId_t id) = 0;
+  virtual CheckpointDelta setCheckpointPoolState(
+      c10::DeviceIndex device,
+      std::shared_ptr<AllocatorState> pps) = 0;
+  virtual std::string name() = 0;
+};
+
+// Allocator object, statically initialized
+// See BackendInitializer in CUDACachingAllocator.cpp.
+// Atomic loads on x86 are just normal loads,
+// (atomic stores are different), so reading this value
+// is no different than loading a pointer.
+C10_CUDA_API extern std::atomic<CUDAAllocator*> allocator;
+
+inline CUDAAllocator* get() {
+  return allocator.load();
+}
+
+// Called directly by clients.
+inline void* raw_alloc(size_t nbytes) {
+  return get()->raw_alloc(nbytes);
+}
+
+inline void* raw_alloc_with_stream(size_t nbytes, cudaStream_t stream) {
+  return get()->raw_alloc_with_stream(nbytes, stream);
+}
+
+inline void raw_delete(void* ptr) {
+  return get()->raw_delete(ptr);
+}
+
+inline void init(int device_count) {
+  return get()->init(device_count);
+}
+
+inline double getMemoryFraction(c10::DeviceIndex device) {
+  return get()->getMemoryFraction(device);
+}
+
+inline void setMemoryFraction(double fraction, c10::DeviceIndex device) {
+  return get()->setMemoryFraction(fraction, device);
+}
+
+inline void emptyCache() {
+  return get()->emptyCache();
+}
+
+inline void enable(bool value) {
+  return get()->enable(value);
+}
+
+inline bool isEnabled() {
+  return get()->isEnabled();
+}
+
+inline void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) {
+  return get()->cacheInfo(device, largestBlock);
+}
+
+inline void* getBaseAllocation(void* ptr, size_t* size) {
+  return get()->getBaseAllocation(ptr, size);
+}
+
+inline void recordStream(const DataPtr& dataPtr, CUDAStream stream) {
+  return get()->recordStream(dataPtr, stream);
+}
+
+inline c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+    c10::DeviceIndex device) {
+  return get()->getDeviceStats(device);
+}
+
+inline void resetAccumulatedStats(c10::DeviceIndex device) {
+  return get()->resetAccumulatedStats(device);
+}
+
+inline void resetPeakStats(c10::DeviceIndex device) {
+  return get()->resetPeakStats(device);
+}
+
+inline SnapshotInfo snapshot() {
+  return get()->snapshot();
+}
+
+inline std::shared_ptr<AllocatorState> getCheckpointState(
+    c10::DeviceIndex device,
+    MempoolId_t id) {
+  return get()->getCheckpointState(device, id);
+}
+
+inline CheckpointDelta setCheckpointPoolState(
+    c10::DeviceIndex device,
+    std::shared_ptr<AllocatorState> pps) {
+  return get()->setCheckpointPoolState(device, std::move(pps));
+}
+
+// CUDAGraph interactions
+inline void beginAllocateToPool(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    std::function<bool(cudaStream_t)> filter) {
+  get()->beginAllocateToPool(device, mempool_id, std::move(filter));
+}
+
+inline void endAllocateToPool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  get()->endAllocateToPool(device, mempool_id);
+}
+
+inline void recordHistory(
+    bool enabled,
+    CreateContextFn context_recorder,
+    size_t alloc_trace_max_entries,
+    RecordContext when) {
+  return get()->recordHistory(
+      enabled, context_recorder, alloc_trace_max_entries, when);
+}
+
+inline void recordAnnotation(
+    const std::vector<std::pair<std::string, std::string>>& md) {
+  return get()->recordAnnotation(md);
+}
+
+inline bool isHistoryEnabled() {
+  return get()->isHistoryEnabled();
+}
+
+inline bool checkPoolLiveAllocations(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    const std::unordered_set<void*>& expected_live_allocations) {
+  return get()->checkPoolLiveAllocations(
+      device, mempool_id, expected_live_allocations);
+}
+
+inline void attachOutOfMemoryObserver(OutOfMemoryObserver observer) {
+  return get()->attachOutOfMemoryObserver(std::move(observer));
+}
+
+inline void attachAllocatorTraceTracker(AllocatorTraceTracker tracker) {
+  return get()->attachAllocatorTraceTracker(std::move(tracker));
+}
+
+inline void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  return get()->releasePool(device, mempool_id);
+}
+inline void ensureExistsAndIncrefPool(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id) {
+  get()->ensureExistsAndIncrefPool(device, mempool_id);
+}
+
+inline int getPoolUseCount(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  return get()->getPoolUseCount(device, mempool_id);
+}
+
+// Not part of CUDA_ALLOCATOR_BACKEND_INTERFACE
+inline std::shared_ptr<void> getIpcDevPtr(std::string handle) {
+  return get()->getIpcDevPtr(std::move(handle));
+}
+
+inline ShareableHandle shareIpcHandle(void* ptr) {
+  return get()->shareIpcHandle(ptr);
+}
+
+inline std::string name() {
+  return get()->name();
+}
+
+inline cudaError_t memcpyAsync(
+    void* dst,
+    int dstDevice,
+    const void* src,
+    int srcDevice,
+    size_t count,
+    cudaStream_t stream,
+    bool p2p_enabled) {
+  return get()->memcpyAsync(
+      dst, dstDevice, src, srcDevice, count, stream, p2p_enabled);
+}
+
+inline void enablePeerAccess(
+    c10::DeviceIndex dev,
+    c10::DeviceIndex dev_to_access) {
+  return get()->enablePeerAccess(dev, dev_to_access);
+}
+
+} // namespace c10::cuda::CUDACachingAllocator
+
+namespace c10::cuda {
+
+// MemPool represents a pool of memory in a caching allocator. Currently,
+// it's just the ID of the pool object maintained in the CUDACachingAllocator.
+//
+// An allocator pointer can be passed to the MemPool to define how the
+// allocations should be done in the pool. For example: using a different
+// system allocator such as ncclMemAlloc.
+struct C10_CUDA_API MemPool {
+  MemPool(
+      CUDACachingAllocator::CUDAAllocator* allocator = nullptr,
+      bool is_user_created = true);
+  MemPool(const MemPool&) = delete;
+  MemPool(MemPool&&) = default;
+  MemPool& operator=(const MemPool&) = delete;
+  MemPool& operator=(MemPool&&) = default;
+  ~MemPool();
+
+  MempoolId_t id();
+  CUDACachingAllocator::CUDAAllocator* allocator();
+  int use_count();
+  c10::DeviceIndex device();
+  static MempoolId_t graph_pool_handle(bool is_user_created = true);
+
+ private:
+  static std::atomic<CaptureId_t> uid_;
+  static std::atomic<CaptureId_t> uuid_;
+  CUDACachingAllocator::CUDAAllocator* allocator_;
+  bool is_user_created_;
+  MempoolId_t id_;
+  c10::DeviceIndex device_;
+};
+
+// MemPoolContext holds the currently active pool and stashes the previous
+// pool. On deletion it makes the previous pool active.
+struct C10_CUDA_API MemPoolContext {
+  MemPoolContext(MemPool* mempool);
+
+  ~MemPoolContext();
+
+  // getActiveMemPool() can be used to get the currently active pool.
+  // For instance: in CUDACachingAllocator, we can route allocations
+  // to a user provided allocator, by doing:
+  //
+  //  auto active_pool = MemPoolContext::getActiveMemPool();
+  //  if (active_pool && active_pool->allocator()) {
+  //    ptr = active_pool->allocator()->raw_alloc(size);
+  //  }
+  //
+  static MemPool* getActiveMemPool();
+
+ private:
+  MemPool* prev_mempool_;
+};
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertion.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertion.h
new file mode 100644
index 0000000000000000000000000000000000000000..063c7836932af00125d6650261da84b7fb90b4ce
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertion.h
@@ -0,0 +1,96 @@
+#pragma once
+
+#include <c10/cuda/CUDAException.h>
+#include <c10/macros/Macros.h>
+
+namespace c10::cuda {
+
+#ifdef TORCH_USE_CUDA_DSA
+// Copy string from `src` to `dst`
+static __device__ void dstrcpy(char* dst, const char* src) {
+  int i = 0;
+  // Copy string from source to destination, ensuring that it
+  // isn't longer than `C10_CUDA_DSA_MAX_STR_LEN-1`
+  while (*src != '\0' && i++ < C10_CUDA_DSA_MAX_STR_LEN - 1) {
+    *dst++ = *src++;
+  }
+  *dst = '\0';
+}
+
+static __device__ void dsa_add_new_assertion_failure(
+    DeviceAssertionsData* assertions_data,
+    const char* assertion_msg,
+    const char* filename,
+    const char* function_name,
+    const int line_number,
+    const uint32_t caller,
+    const dim3 block_id,
+    const dim3 thread_id) {
+  // `assertions_data` may be nullptr if device-side assertion checking
+  // is disabled at run-time. If it is disabled at compile time this
+  // function will never be called
+  if (!assertions_data) {
+    return;
+  }
+
+  // Atomically increment so other threads can fail at the same time
+  // Note that incrementing this means that the CPU can observe that
+  // a failure has happened and can begin to respond before we've
+  // written information about that failure out to the buffer.
+  const auto nid = atomicAdd(&(assertions_data->assertion_count), 1);
+
+  if (nid >= C10_CUDA_DSA_ASSERTION_COUNT) {
+    // At this point we're ran out of assertion buffer space.
+    // We could print a message about this, but that'd get
+    // spammy if a lot of threads did it, so we just silently
+    // ignore any other assertion failures. In most cases the
+    // failures will all probably be analogous anyway.
+    return;
+  }
+
+  // Write information about the assertion failure to memory.
+  // Note that this occurs only after the `assertion_count`
+  // increment broadcasts that there's been a problem.
+  auto& self = assertions_data->assertions[nid];
+  dstrcpy(self.assertion_msg, assertion_msg);
+  dstrcpy(self.filename, filename);
+  dstrcpy(self.function_name, function_name);
+  self.line_number = line_number;
+  self.caller = caller;
+  self.block_id[0] = block_id.x;
+  self.block_id[1] = block_id.y;
+  self.block_id[2] = block_id.z;
+  self.thread_id[0] = thread_id.x;
+  self.thread_id[1] = thread_id.y;
+  self.thread_id[2] = thread_id.z;
+}
+
+// Emulates a kernel assertion. The assertion won't stop the kernel's progress,
+// so you should assume everything the kernel produces is garbage if there's an
+// assertion failure.
+// NOTE: This assumes that `assertions_data` and  `assertion_caller_id` are
+//       arguments of the kernel and therefore accessible.
+#define CUDA_KERNEL_ASSERT2(condition)                                   \
+  do {                                                                   \
+    if (C10_UNLIKELY(!(condition))) {                                    \
+      /* Has an atomic element so threads can fail at the same time */   \
+      c10::cuda::dsa_add_new_assertion_failure(                          \
+          assertions_data,                                               \
+          C10_STRINGIZE(condition),                                      \
+          __FILE__,                                                      \
+          __FUNCTION__,                                                  \
+          __LINE__,                                                      \
+          assertion_caller_id,                                           \
+          blockIdx,                                                      \
+          threadIdx);                                                    \
+      /* Now that the kernel has failed we early exit the kernel, but */ \
+      /* otherwise keep going and rely on the host to check UVM and */   \
+      /* determine we've had a problem */                                \
+      return;                                                            \
+    }                                                                    \
+  } while (false)
+#else
+#define CUDA_KERNEL_ASSERT2(condition) assert(condition)
+#endif
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertionHost.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertionHost.h
new file mode 100644
index 0000000000000000000000000000000000000000..a945915c2878bec1ea7f0ea6f89b04e20f90dc4e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDADeviceAssertionHost.h
@@ -0,0 +1,164 @@
+#pragma once
+
+#include <c10/cuda/CUDAMacros.h>
+
+#include <cstdint>
+#include <memory>
+#include <mutex>
+#include <string>
+#include <utility>
+#include <vector>
+
+#ifdef USE_CUDA
+#define TORCH_USE_CUDA_DSA
+#endif
+
+/// Number of assertion failure messages we can store. If this is too small
+/// threads will fail silently.
+constexpr int C10_CUDA_DSA_ASSERTION_COUNT = 10;
+constexpr int C10_CUDA_DSA_MAX_STR_LEN = 512;
+
+namespace c10::cuda {
+
+/// Holds information about any device-side assertions that fail.
+/// Held in managed memory and access by both the CPU and the GPU.
+struct DeviceAssertionData {
+  /// Stringification of the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  char assertion_msg[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// File the assertion was in
+  // NOLINTNEXTLINE(*-c-arrays)
+  char filename[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// Name of the function the assertion was in
+  // NOLINTNEXTLINE(*-c-arrays)
+  char function_name[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// Line number the assertion was at
+  int line_number{};
+  /// Number uniquely identifying the kernel launch that triggered the assertion
+  uint32_t caller{};
+  /// block_id of the thread that failed the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  int32_t block_id[3]{};
+  /// third_id of the thread that failed the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  int32_t thread_id[3]{};
+};
+
+/// Used to hold assertions generated by the device
+/// Held in managed memory and access by both the CPU and the GPU.
+struct DeviceAssertionsData {
+  /// Total number of assertions found; a subset of thse will be recorded
+  /// in `assertions`
+  int32_t assertion_count{};
+  /// An array of assertions that will be written to in a race-free manner
+  // NOLINTNEXTLINE(*-c-arrays)
+  DeviceAssertionData assertions[C10_CUDA_DSA_ASSERTION_COUNT]{};
+};
+
+/// Use to hold info about kernel launches so that we can run kernels
+/// asynchronously and still associate launches with device-side
+/// assertion failures
+struct CUDAKernelLaunchInfo {
+  /// Filename of the code where the kernel was launched from
+  const char* launch_filename;
+  /// Function from which the kernel was launched
+  const char* launch_function;
+  /// Line number of where the code was launched from
+  uint32_t launch_linenum;
+  /// Backtrace of where the kernel was launched from, only populated if
+  /// CUDAKernelLaunchRegistry::gather_launch_stacktrace is True
+  std::string launch_stacktrace;
+  /// Kernel that was launched
+  const char* kernel_name;
+  /// Device the kernel was launched on
+  int device;
+  /// Stream the kernel was launched on
+  int32_t stream;
+  /// A number that uniquely identifies the kernel launch
+  uint64_t generation_number;
+};
+
+/// Circular buffer used to hold information about kernel launches
+/// this is later used to reconstruct how a device-side kernel assertion failure
+/// occurred CUDAKernelLaunchRegistry is used as a singleton
+class C10_CUDA_API CUDAKernelLaunchRegistry {
+ private:
+  /// Assume that this is the max number of kernel launches that might ever be
+  /// enqueued across all streams on a single device
+  static constexpr int max_kernel_launches = 1024;
+  /// How many kernel launch infos we've inserted. Used to ensure that circular
+  /// queue doesn't provide false information by always increasing, but also to
+  /// mark where we are inserting into the queue
+#ifdef TORCH_USE_CUDA_DSA
+  uint64_t generation_number = 0;
+#endif
+  /// Shared mutex between writer and accessor to ensure multi-threaded safety.
+  mutable std::mutex read_write_mutex;
+  /// Used to ensure prevent race conditions in GPU memory allocation
+  mutable std::mutex gpu_alloc_mutex;
+  /// Pointer to managed memory keeping track of device-side assertions. There
+  /// is one entry for each possible device the process might work with. Unused
+  /// entries are nullptrs. We could also use an unordered_set here, but this
+  /// vector design will be faster and the wasted memory is small since we
+  /// expect the number of GPUs per node will always be small
+  std::vector<
+      std::unique_ptr<DeviceAssertionsData, void (*)(DeviceAssertionsData*)>>
+      uvm_assertions;
+  /// A single circular buffer holds information about every kernel launch the
+  /// process makes across all devices.
+  std::vector<CUDAKernelLaunchInfo> kernel_launches;
+  bool check_env_for_enable_launch_stacktracing() const;
+  bool check_env_for_dsa_enabled() const;
+
+ public:
+  CUDAKernelLaunchRegistry();
+  /// Register a new kernel launch and obtain a generation number back to be
+  /// passed to the kernel
+  uint32_t insert(
+      const char* launch_filename,
+      const char* launch_function,
+      const uint32_t launch_linenum,
+      const char* kernel_name,
+      const int32_t stream_id);
+  /// Get copies of the kernel launch registry and each device's assertion
+  /// failure buffer so they can be inspected without raising race conditions
+  std::
+      pair<std::vector<DeviceAssertionsData>, std::vector<CUDAKernelLaunchInfo>>
+      snapshot() const;
+  /// Get a pointer to the current device's assertion failure buffer. If no such
+  /// buffer exists then one is created. This means that the first kernel launch
+  /// made on each device will be slightly slower because memory allocations are
+  /// required
+  DeviceAssertionsData* get_uvm_assertions_ptr_for_current_device();
+  /// Gets the global singleton of the registry
+  static CUDAKernelLaunchRegistry& get_singleton_ref();
+  /// If not all devices support DSA, we disable it
+  const bool do_all_devices_support_managed_memory = false;
+  /// Whether or not to gather stack traces when launching kernels
+  bool gather_launch_stacktrace = false;
+  /// Whether or not host-side DSA is enabled or disabled at run-time
+  /// Note: Device-side code cannot be enabled/disabled at run-time
+  bool enabled_at_runtime = false;
+  /// Whether or not a device has indicated a failure
+  bool has_failed() const;
+#ifdef TORCH_USE_CUDA_DSA
+  const bool enabled_at_compile_time = true;
+#else
+  const bool enabled_at_compile_time = false;
+#endif
+};
+
+std::string c10_retrieve_device_side_assertion_info();
+
+} // namespace c10::cuda
+
+// Each kernel launched with TORCH_DSA_KERNEL_LAUNCH
+// requires the same input arguments. We introduce the following macro to
+// standardize these.
+#define TORCH_DSA_KERNEL_ARGS                                              \
+  [[maybe_unused]] c10::cuda::DeviceAssertionsData *const assertions_data, \
+      [[maybe_unused]] uint32_t assertion_caller_id
+
+// This macro can be used to pass the DSA arguments onward to another
+// function
+#define TORCH_DSA_KERNEL_ARGS_PASS assertions_data, assertion_caller_id
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAException.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAException.h
new file mode 100644
index 0000000000000000000000000000000000000000..899d85e8a73f6b1f2e0f3f8e069ae97de5302937
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAException.h
@@ -0,0 +1,97 @@
+#pragma once
+
+#include <c10/cuda/CUDADeviceAssertionHost.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/cuda/CUDAMiscFunctions.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/irange.h>
+#include <cuda.h>
+
+// Note [CHECK macro]
+// ~~~~~~~~~~~~~~~~~~
+// This is a macro so that AT_ERROR can get accurate __LINE__
+// and __FILE__ information.  We could split this into a short
+// macro and a function implementation if we pass along __LINE__
+// and __FILE__, but no one has found this worth doing.
+
+// Used to denote errors from CUDA framework.
+// This needs to be declared here instead util/Exception.h for proper conversion
+// during hipify.
+namespace c10 {
+class C10_CUDA_API CUDAError : public c10::Error {
+  using Error::Error;
+};
+} // namespace c10
+
+#define C10_CUDA_CHECK(EXPR)                                        \
+  do {                                                              \
+    const cudaError_t __err = EXPR;                                 \
+    c10::cuda::c10_cuda_check_implementation(                       \
+        static_cast<int32_t>(__err),                                \
+        __FILE__,                                                   \
+        __func__, /* Line number data type not well-defined between \
+                      compilers, so we perform an explicit cast */  \
+        static_cast<uint32_t>(__LINE__),                            \
+        true);                                                      \
+  } while (0)
+
+#define C10_CUDA_CHECK_WARN(EXPR)                              \
+  do {                                                         \
+    const cudaError_t __err = EXPR;                            \
+    if (C10_UNLIKELY(__err != cudaSuccess)) {                  \
+      [[maybe_unused]] auto error_unused = cudaGetLastError(); \
+      TORCH_WARN("CUDA warning: ", cudaGetErrorString(__err)); \
+    }                                                          \
+  } while (0)
+
+// Indicates that a CUDA error is handled in a non-standard way
+#define C10_CUDA_ERROR_HANDLED(EXPR) EXPR
+
+// Intentionally ignore a CUDA error
+#define C10_CUDA_IGNORE_ERROR(EXPR)                                   \
+  do {                                                                \
+    const cudaError_t __err = EXPR;                                   \
+    if (C10_UNLIKELY(__err != cudaSuccess)) {                         \
+      [[maybe_unused]] cudaError_t error_unused = cudaGetLastError(); \
+    }                                                                 \
+  } while (0)
+
+// Clear the last CUDA error
+#define C10_CUDA_CLEAR_ERROR()                                      \
+  do {                                                              \
+    [[maybe_unused]] cudaError_t error_unused = cudaGetLastError(); \
+  } while (0)
+
+// This should be used directly after every kernel launch to ensure
+// the launch happened correctly and provide an early, close-to-source
+// diagnostic if it didn't.
+#define C10_CUDA_KERNEL_LAUNCH_CHECK() C10_CUDA_CHECK(cudaGetLastError())
+
+/// Launches a CUDA kernel appending to it all the information need to handle
+/// device-side assertion failures. Checks that the launch was successful.
+#define TORCH_DSA_KERNEL_LAUNCH(                                      \
+    kernel, blocks, threads, shared_mem, stream, ...)                 \
+  do {                                                                \
+    auto& launch_registry =                                           \
+        c10::cuda::CUDAKernelLaunchRegistry::get_singleton_ref();     \
+    kernel<<<blocks, threads, shared_mem, stream>>>(                  \
+        __VA_ARGS__,                                                  \
+        launch_registry.get_uvm_assertions_ptr_for_current_device(),  \
+        launch_registry.insert(                                       \
+            __FILE__, __FUNCTION__, __LINE__, #kernel, stream.id())); \
+    C10_CUDA_KERNEL_LAUNCH_CHECK();                                   \
+  } while (0)
+
+namespace c10::cuda {
+
+/// In the event of a CUDA failure, formats a nice error message about that
+/// failure and also checks for device-side assertion failures
+C10_CUDA_API void c10_cuda_check_implementation(
+    const int32_t err,
+    const char* filename,
+    const char* function_name,
+    const int line_number,
+    const bool include_device_assertions);
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAFunctions.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..192fafbad10f42525604fe4b9769090f05ed9df1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAFunctions.h
@@ -0,0 +1,116 @@
+#pragma once
+
+// This header provides C++ wrappers around commonly used CUDA API functions.
+// The benefit of using C++ here is that we can raise an exception in the
+// event of an error, rather than explicitly pass around error codes.  This
+// leads to more natural APIs.
+//
+// The naming convention used here matches the naming convention of torch.cuda
+
+#include <c10/core/Device.h>
+#include <c10/core/impl/GPUTrace.h>
+#include <c10/cuda/CUDAException.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <cuda_runtime_api.h>
+namespace c10::cuda {
+
+// NB: In the past, we were inconsistent about whether or not this reported
+// an error if there were driver problems are not.  Based on experience
+// interacting with users, it seems that people basically ~never want this
+// function to fail; it should just return zero if things are not working.
+// Oblige them.
+// It still might log a warning for user first time it's invoked
+C10_CUDA_API DeviceIndex device_count() noexcept;
+
+// Version of device_count that throws is no devices are detected
+C10_CUDA_API DeviceIndex device_count_ensure_non_zero();
+
+C10_CUDA_API DeviceIndex current_device();
+
+C10_CUDA_API void set_device(DeviceIndex device);
+
+C10_CUDA_API void device_synchronize();
+
+C10_CUDA_API void warn_or_error_on_sync();
+
+// Raw CUDA device management functions
+C10_CUDA_API cudaError_t GetDeviceCount(int* dev_count);
+
+C10_CUDA_API cudaError_t GetDevice(DeviceIndex* device);
+
+C10_CUDA_API cudaError_t SetDevice(DeviceIndex device);
+
+C10_CUDA_API cudaError_t MaybeSetDevice(DeviceIndex device);
+
+C10_CUDA_API DeviceIndex ExchangeDevice(DeviceIndex device);
+
+C10_CUDA_API DeviceIndex MaybeExchangeDevice(DeviceIndex device);
+
+C10_CUDA_API void SetTargetDevice();
+
+enum class SyncDebugMode { L_DISABLED = 0, L_WARN, L_ERROR };
+
+// this is a holder for c10 global state (similar to at GlobalContext)
+// currently it's used to store cuda synchronization warning state,
+// but can be expanded to hold other related global state, e.g. to
+// record stream usage
+class WarningState {
+ public:
+  void set_sync_debug_mode(SyncDebugMode l) {
+    sync_debug_mode = l;
+  }
+
+  SyncDebugMode get_sync_debug_mode() {
+    return sync_debug_mode;
+  }
+
+ private:
+  SyncDebugMode sync_debug_mode = SyncDebugMode::L_DISABLED;
+};
+
+C10_CUDA_API __inline__ WarningState& warning_state() {
+  static WarningState warning_state_;
+  return warning_state_;
+}
+// the subsequent functions are defined in the header because for performance
+// reasons we want them to be inline
+C10_CUDA_API void __inline__ memcpy_and_sync(
+    void* dst,
+    const void* src,
+    int64_t nbytes,
+    cudaMemcpyKind kind,
+    cudaStream_t stream) {
+  if (C10_UNLIKELY(
+          warning_state().get_sync_debug_mode() != SyncDebugMode::L_DISABLED)) {
+    warn_or_error_on_sync();
+  }
+  const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+  if (C10_UNLIKELY(interp)) {
+    (*interp)->trace_gpu_stream_synchronization(
+        c10::kCUDA, reinterpret_cast<uintptr_t>(stream));
+  }
+#if defined(TORCH_HIP_VERSION) && (TORCH_HIP_VERSION >= 301)
+  C10_CUDA_CHECK(hipMemcpyWithStream(dst, src, nbytes, kind, stream));
+#else
+  C10_CUDA_CHECK(cudaMemcpyAsync(dst, src, nbytes, kind, stream));
+  C10_CUDA_CHECK(cudaStreamSynchronize(stream));
+#endif
+}
+
+C10_CUDA_API void __inline__ stream_synchronize(cudaStream_t stream) {
+  if (C10_UNLIKELY(
+          warning_state().get_sync_debug_mode() != SyncDebugMode::L_DISABLED)) {
+    warn_or_error_on_sync();
+  }
+  const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+  if (C10_UNLIKELY(interp)) {
+    (*interp)->trace_gpu_stream_synchronization(
+        c10::kCUDA, reinterpret_cast<uintptr_t>(stream));
+  }
+  C10_CUDA_CHECK(cudaStreamSynchronize(stream));
+}
+
+C10_CUDA_API bool hasPrimaryContext(DeviceIndex device_index);
+C10_CUDA_API std::optional<DeviceIndex> getDeviceIndexWithPrimaryContext();
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGraphsC10Utils.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGraphsC10Utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..eb29ca8bc9f0203481fa6ebd76a2c2fba41a7ecf
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGraphsC10Utils.h
@@ -0,0 +1,82 @@
+#pragma once
+
+#include <c10/cuda/CUDAStream.h>
+#include <iostream>
+#include <utility>
+
+// CUDA Graphs utils used by c10 and aten.
+// aten/cuda/CUDAGraphsUtils.cuh adds utils used by aten only.
+
+namespace c10::cuda {
+
+using CaptureId_t = unsigned long long;
+
+// first is set if the instance is created by CUDAGraph::capture_begin.
+// second is set if the instance is created by at::cuda::graph_pool_handle.
+using MempoolId_t = std::pair<CaptureId_t, CaptureId_t>;
+
+// RAII guard for "cudaStreamCaptureMode", a thread-local value
+// that controls the error-checking strictness of a capture.
+struct C10_CUDA_API CUDAStreamCaptureModeGuard {
+  CUDAStreamCaptureModeGuard(cudaStreamCaptureMode desired)
+      : strictness_(desired) {
+    C10_CUDA_CHECK(cudaThreadExchangeStreamCaptureMode(&strictness_));
+  }
+  CUDAStreamCaptureModeGuard(const CUDAStreamCaptureModeGuard&) = delete;
+  CUDAStreamCaptureModeGuard(CUDAStreamCaptureModeGuard&&) = delete;
+  CUDAStreamCaptureModeGuard& operator=(const CUDAStreamCaptureModeGuard&) =
+      delete;
+  CUDAStreamCaptureModeGuard& operator=(CUDAStreamCaptureModeGuard&&) = delete;
+  ~CUDAStreamCaptureModeGuard() {
+    C10_CUDA_CHECK_WARN(cudaThreadExchangeStreamCaptureMode(&strictness_));
+  }
+
+ private:
+  cudaStreamCaptureMode strictness_;
+};
+
+// Protects against enum cudaStreamCaptureStatus implementation changes.
+// Some compilers seem not to like static_assert without the messages.
+static_assert(
+    int(cudaStreamCaptureStatus::cudaStreamCaptureStatusNone) == 0,
+    "unexpected int(cudaStreamCaptureStatusNone) value");
+static_assert(
+    int(cudaStreamCaptureStatus::cudaStreamCaptureStatusActive) == 1,
+    "unexpected int(cudaStreamCaptureStatusActive) value");
+static_assert(
+    int(cudaStreamCaptureStatus::cudaStreamCaptureStatusInvalidated) == 2,
+    "unexpected int(cudaStreamCaptureStatusInvalidated) value");
+
+enum class CaptureStatus : int {
+  None = int(cudaStreamCaptureStatus::cudaStreamCaptureStatusNone),
+  Active = int(cudaStreamCaptureStatus::cudaStreamCaptureStatusActive),
+  Invalidated = int(cudaStreamCaptureStatus::cudaStreamCaptureStatusInvalidated)
+};
+
+inline std::ostream& operator<<(std::ostream& os, CaptureStatus status) {
+  switch (status) {
+    case CaptureStatus::None:
+      os << "cudaStreamCaptureStatusNone";
+      break;
+    case CaptureStatus::Active:
+      os << "cudaStreamCaptureStatusActive";
+      break;
+    case CaptureStatus::Invalidated:
+      os << "cudaStreamCaptureStatusInvalidated";
+      break;
+    default:
+      TORCH_INTERNAL_ASSERT(
+          false, "Unknown CUDA graph CaptureStatus", int(status));
+  }
+  return os;
+}
+
+// Use this version where you're sure a CUDA context exists already.
+inline CaptureStatus currentStreamCaptureStatusMayInitCtx() {
+  cudaStreamCaptureStatus is_capturing{cudaStreamCaptureStatusNone};
+  C10_CUDA_CHECK(
+      cudaStreamIsCapturing(c10::cuda::getCurrentCUDAStream(), &is_capturing));
+  return CaptureStatus(is_capturing);
+}
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGuard.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a076d5637b101dcc85effea655816470781d965
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAGuard.h
@@ -0,0 +1,306 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/core/impl/InlineDeviceGuard.h>
+#include <c10/core/impl/InlineStreamGuard.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/cuda/impl/CUDAGuardImpl.h>
+
+namespace c10::cuda {
+
+// This code is kind of boilerplatey.  See Note [Whither the DeviceGuard
+// boilerplate]
+
+/// A variant of DeviceGuard that is specialized for CUDA.  It accepts
+/// integer indices (interpreting them as CUDA devices) and is a little
+/// more efficient than DeviceGuard (it compiles to straight line
+/// cudaSetDevice/cudaGetDevice calls); however, it can only be used
+/// from code that links against CUDA directly.
+struct CUDAGuard {
+  /// No default constructor; see Note [Omitted default constructor from RAII]
+  explicit CUDAGuard() = delete;
+
+  /// Set the current CUDA device to the passed device index.
+  explicit CUDAGuard(DeviceIndex device_index) : guard_(device_index) {}
+
+  /// Sets the current CUDA device to the passed device.  Errors if the passed
+  /// device is not a CUDA device.
+  explicit CUDAGuard(Device device) : guard_(device) {}
+
+  // Copy is not allowed
+  CUDAGuard(const CUDAGuard&) = delete;
+  CUDAGuard& operator=(const CUDAGuard&) = delete;
+
+  // Move is not allowed (there is no uninitialized state)
+  CUDAGuard(CUDAGuard&& other) = delete;
+  CUDAGuard& operator=(CUDAGuard&& other) = delete;
+  ~CUDAGuard() = default;
+
+  /// Sets the CUDA device to the given device.  Errors if the given device
+  /// is not a CUDA device.
+  void set_device(Device device) {
+    guard_.set_device(device);
+  }
+
+  /// Sets the CUDA device to the given device.  Errors if the given device
+  /// is not a CUDA device.  (This method is provided for uniformity with
+  /// DeviceGuard).
+  void reset_device(Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// Sets the CUDA device to the given device index.
+  void set_index(DeviceIndex device_index) {
+    guard_.set_index(device_index);
+  }
+
+  /// Returns the device that was set upon construction of the guard
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the last device that was set via `set_device`, if any, otherwise
+  /// the device passed during construction.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+ private:
+  /// The guard for the current device.
+  c10::impl::InlineDeviceGuard<impl::CUDAGuardImpl> guard_;
+};
+
+/// A variant of OptionalDeviceGuard that is specialized for CUDA.  See
+/// CUDAGuard for when you can use this.
+struct OptionalCUDAGuard {
+  /// Create an uninitialized OptionalCUDAGuard.
+  explicit OptionalCUDAGuard() : guard_() {}
+
+  /// Set the current CUDA device to the passed Device, if it is not nullopt.
+  explicit OptionalCUDAGuard(std::optional<Device> device_opt)
+      : guard_(device_opt) {}
+
+  /// Set the current CUDA device to the passed device index, if it is not
+  /// nullopt
+  explicit OptionalCUDAGuard(std::optional<DeviceIndex> device_index_opt)
+      : guard_(device_index_opt) {}
+
+  // Copy is not allowed
+  OptionalCUDAGuard(const OptionalCUDAGuard&) = delete;
+  OptionalCUDAGuard& operator=(const OptionalCUDAGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalCUDAGuard(OptionalCUDAGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalCUDAGuard& operator=(OptionalCUDAGuard&& other) = delete;
+  ~OptionalCUDAGuard() = default;
+
+  /// Sets the CUDA device to the given device, initializing the guard if it
+  /// is not already initialized.  Errors if the given device is not a CUDA
+  /// device.
+  void set_device(Device device) {
+    guard_.set_device(device);
+  }
+
+  /// Sets the CUDA device to the given device, initializing the guard if it is
+  /// not already initialized.  Errors if the given device is not a CUDA device.
+  /// (This method is provided for uniformity with OptionalDeviceGuard).
+  void reset_device(Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// Sets the CUDA device to the given device index, initializing the guard if
+  /// it is not already initialized.
+  void set_index(DeviceIndex device_index) {
+    guard_.set_index(device_index);
+  }
+
+  /// Returns the device that was set immediately prior to initialization of the
+  /// guard, or nullopt if the guard is uninitialized.
+  std::optional<Device> original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device, if the guard is initialized,
+  /// or nullopt if the guard is uninitialized.
+  std::optional<Device> current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Restore the original CUDA device, resetting this guard to uninitialized
+  /// state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalDeviceGuard<impl::CUDAGuardImpl> guard_;
+};
+
+/// A variant of StreamGuard that is specialized for CUDA.  See CUDAGuard
+/// for when you can use this.
+struct CUDAStreamGuard {
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit CUDAStreamGuard() = delete;
+
+  /// Set the current CUDA device to the device associated with the passed
+  /// stream, and set the current CUDA stream on that device to the passed
+  /// stream. Errors if the Stream is not a CUDA stream.
+  explicit CUDAStreamGuard(Stream stream) : guard_(stream) {}
+  ~CUDAStreamGuard() = default;
+
+  /// Copy is disallowed
+  CUDAStreamGuard(const CUDAStreamGuard&) = delete;
+  CUDAStreamGuard& operator=(const CUDAStreamGuard&) = delete;
+
+  /// Move is disallowed, as CUDAStreamGuard does not have an uninitialized
+  /// state, which is required for moves on types with nontrivial destructors.
+  CUDAStreamGuard(CUDAStreamGuard&& other) = delete;
+  CUDAStreamGuard& operator=(CUDAStreamGuard&& other) = delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Errors if the stream passed is not a CUDA stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// on CUDA, use CUDAMultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the CUDA stream that was set at the time the guard was
+  /// constructed.
+  CUDAStream original_stream() const {
+    return CUDAStream(CUDAStream::UNCHECKED, guard_.original_stream());
+  }
+
+  /// Returns the most recent CUDA stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  CUDAStream current_stream() const {
+    return CUDAStream(CUDAStream::UNCHECKED, guard_.current_stream());
+  }
+
+  /// Returns the most recent CUDA device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Returns the CUDA device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+ private:
+  c10::impl::InlineStreamGuard<impl::CUDAGuardImpl> guard_;
+};
+
+/// A variant of OptionalStreamGuard that is specialized for CUDA.  See
+/// CUDAGuard for when you can use this.
+struct OptionalCUDAStreamGuard {
+  /// Create an uninitialized guard.
+  explicit OptionalCUDAStreamGuard() : guard_() {}
+
+  /// Set the current CUDA device to the device associated with the passed
+  /// stream, and set the current CUDA stream on that device to the passed
+  /// stream. Errors if the Stream is not a CUDA stream.
+  explicit OptionalCUDAStreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit OptionalCUDAStreamGuard(std::optional<Stream> stream_opt)
+      : guard_(stream_opt) {}
+
+  /// Copy is disallowed
+  OptionalCUDAStreamGuard(const OptionalCUDAStreamGuard&) = delete;
+  OptionalCUDAStreamGuard& operator=(const OptionalCUDAStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalCUDAStreamGuard(OptionalCUDAStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalCUDAStreamGuard& operator=(OptionalCUDAStreamGuard&& other) = delete;
+  ~OptionalCUDAStreamGuard() = default;
+
+  /// Resets the currently set CUDA stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the guard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the CUDA stream that was set at the time the guard was most
+  /// recently initialized, or nullopt if the guard is uninitialized.
+  std::optional<CUDAStream> original_stream() const {
+    auto r = guard_.original_stream();
+    if (r.has_value()) {
+      return CUDAStream(CUDAStream::UNCHECKED, r.value());
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  /// Returns the most recent CUDA stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  std::optional<CUDAStream> current_stream() const {
+    auto r = guard_.current_stream();
+    if (r.has_value()) {
+      return CUDAStream(CUDAStream::UNCHECKED, r.value());
+    } else {
+      return std::nullopt;
+    }
+  }
+
+  /// Restore the original CUDA device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalStreamGuard<impl::CUDAGuardImpl> guard_;
+};
+
+/// A variant of MultiStreamGuard that is specialized for CUDA.
+struct CUDAMultiStreamGuard {
+  explicit CUDAMultiStreamGuard(ArrayRef<CUDAStream> streams)
+      : guard_(unwrapStreams(streams)) {}
+
+  /// Copy is disallowed
+  CUDAMultiStreamGuard(const CUDAMultiStreamGuard&) = delete;
+  CUDAMultiStreamGuard& operator=(const CUDAMultiStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  CUDAMultiStreamGuard(CUDAMultiStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  CUDAMultiStreamGuard& operator=(CUDAMultiStreamGuard&& other) = delete;
+  ~CUDAMultiStreamGuard() = default;
+
+ private:
+  c10::impl::InlineMultiStreamGuard<impl::CUDAGuardImpl> guard_;
+
+  static std::vector<Stream> unwrapStreams(ArrayRef<CUDAStream> cudaStreams) {
+    std::vector<Stream> streams;
+    streams.reserve(cudaStreams.size());
+    for (const CUDAStream& cudaStream : cudaStreams) {
+      streams.push_back(cudaStream);
+    }
+    return streams;
+  }
+};
+
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMacros.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMacros.h
new file mode 100644
index 0000000000000000000000000000000000000000..4fe25918c3082426ad9e49f8282366eb282bb1df
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMacros.h
@@ -0,0 +1,51 @@
+#pragma once
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+
+// We have not yet modified the AMD HIP build to generate this file so
+// we add an extra option to specifically ignore it.
+#ifndef C10_CUDA_NO_CMAKE_CONFIGURE_FILE
+#include <c10/cuda/impl/cuda_cmake_macros.h>
+#endif // C10_CUDA_NO_CMAKE_CONFIGURE_FILE
+
+#endif
+
+// See c10/macros/Export.h for a detailed explanation of what the function
+// of these macros are.  We need one set of macros for every separate library
+// we build.
+
+#ifdef _WIN32
+#if defined(C10_CUDA_BUILD_SHARED_LIBS)
+#define C10_CUDA_EXPORT __declspec(dllexport)
+#define C10_CUDA_IMPORT __declspec(dllimport)
+#else
+#define C10_CUDA_EXPORT
+#define C10_CUDA_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_CUDA_EXPORT __attribute__((__visibility__("default")))
+#else // defined(__GNUC__)
+#define C10_CUDA_EXPORT
+#endif // defined(__GNUC__)
+#define C10_CUDA_IMPORT C10_CUDA_EXPORT
+#endif // _WIN32
+
+// This one is being used by libc10_cuda.so
+#ifdef C10_CUDA_BUILD_MAIN_LIB
+#define C10_CUDA_API C10_CUDA_EXPORT
+#else
+#define C10_CUDA_API C10_CUDA_IMPORT
+#endif
+
+/**
+ * The maximum number of GPUs that we recognizes. Increasing this beyond the
+ * initial limit of 16 broke Caffe2 testing, hence the ifdef guards.
+ * This value cannot be more than 128 because our DeviceIndex is a uint8_t.
+o */
+#ifdef FBCODE_CAFFE2
+// fbcode depends on this value being 16
+#define C10_COMPILE_TIME_MAX_GPUS 16
+#else
+#define C10_COMPILE_TIME_MAX_GPUS 120
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMathCompat.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMathCompat.h
new file mode 100644
index 0000000000000000000000000000000000000000..58fcebf616f82162bf99877c33a68394c939f776
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMathCompat.h
@@ -0,0 +1,152 @@
+#pragma once
+
+/* This file defines math functions compatible across different gpu
+ * platforms (currently CUDA and HIP).
+ */
+#if defined(__CUDACC__) || defined(__HIPCC__)
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+
+#ifdef __HIPCC__
+#define __MATH_FUNCTIONS_DECL__ inline C10_DEVICE
+#else /* __HIPCC__ */
+#ifdef __CUDACC_RTC__
+#define __MATH_FUNCTIONS_DECL__ C10_HOST_DEVICE
+#else /* __CUDACC_RTC__ */
+#define __MATH_FUNCTIONS_DECL__ inline C10_HOST_DEVICE
+#endif /* __CUDACC_RTC__ */
+#endif /* __HIPCC__ */
+
+namespace c10::cuda::compat {
+
+__MATH_FUNCTIONS_DECL__ float abs(float x) {
+  return ::fabsf(x);
+}
+__MATH_FUNCTIONS_DECL__ double abs(double x) {
+  return ::fabs(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float exp(float x) {
+  return ::expf(x);
+}
+__MATH_FUNCTIONS_DECL__ double exp(double x) {
+  return ::exp(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float ceil(float x) {
+  return ::ceilf(x);
+}
+__MATH_FUNCTIONS_DECL__ double ceil(double x) {
+  return ::ceil(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float copysign(float x, float y) {
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
+  return ::copysignf(x, y);
+#else
+  // std::copysign gets ICE/Segfaults with gcc 7.5/8 on arm64
+  // (e.g. Jetson), see PyTorch PR #51834
+  // This host function needs to be here for the compiler but is never used
+  TORCH_INTERNAL_ASSERT(
+      false, "CUDAMathCompat copysign should not run on the CPU");
+#endif
+}
+__MATH_FUNCTIONS_DECL__ double copysign(double x, double y) {
+#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
+  return ::copysign(x, y);
+#else
+  // see above
+  TORCH_INTERNAL_ASSERT(
+      false, "CUDAMathCompat copysign should not run on the CPU");
+#endif
+}
+
+__MATH_FUNCTIONS_DECL__ float floor(float x) {
+  return ::floorf(x);
+}
+__MATH_FUNCTIONS_DECL__ double floor(double x) {
+  return ::floor(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float log(float x) {
+  return ::logf(x);
+}
+__MATH_FUNCTIONS_DECL__ double log(double x) {
+  return ::log(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float log1p(float x) {
+  return ::log1pf(x);
+}
+
+__MATH_FUNCTIONS_DECL__ double log1p(double x) {
+  return ::log1p(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float max(float x, float y) {
+  return ::fmaxf(x, y);
+}
+__MATH_FUNCTIONS_DECL__ double max(double x, double y) {
+  return ::fmax(x, y);
+}
+
+__MATH_FUNCTIONS_DECL__ float min(float x, float y) {
+  return ::fminf(x, y);
+}
+__MATH_FUNCTIONS_DECL__ double min(double x, double y) {
+  return ::fmin(x, y);
+}
+
+__MATH_FUNCTIONS_DECL__ float pow(float x, float y) {
+  return ::powf(x, y);
+}
+__MATH_FUNCTIONS_DECL__ double pow(double x, double y) {
+  return ::pow(x, y);
+}
+
+__MATH_FUNCTIONS_DECL__ void sincos(float x, float* sptr, float* cptr) {
+  return ::sincosf(x, sptr, cptr);
+}
+__MATH_FUNCTIONS_DECL__ void sincos(double x, double* sptr, double* cptr) {
+  return ::sincos(x, sptr, cptr);
+}
+
+__MATH_FUNCTIONS_DECL__ float sqrt(float x) {
+  return ::sqrtf(x);
+}
+__MATH_FUNCTIONS_DECL__ double sqrt(double x) {
+  return ::sqrt(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float rsqrt(float x) {
+  return ::rsqrtf(x);
+}
+__MATH_FUNCTIONS_DECL__ double rsqrt(double x) {
+  return ::rsqrt(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float tan(float x) {
+  return ::tanf(x);
+}
+__MATH_FUNCTIONS_DECL__ double tan(double x) {
+  return ::tan(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float tanh(float x) {
+  return ::tanhf(x);
+}
+__MATH_FUNCTIONS_DECL__ double tanh(double x) {
+  return ::tanh(x);
+}
+
+__MATH_FUNCTIONS_DECL__ float normcdf(float x) {
+  return ::normcdff(x);
+}
+__MATH_FUNCTIONS_DECL__ double normcdf(double x) {
+  return ::normcdf(x);
+}
+
+} // namespace c10::cuda::compat
+
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMiscFunctions.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMiscFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc3fced770ba8db64d0656b7020ebe7f78664f30
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAMiscFunctions.h
@@ -0,0 +1,12 @@
+#pragma once
+// this file is to avoid circular dependency between CUDAFunctions.h and
+// CUDAExceptions.h
+
+#include <c10/cuda/CUDAMacros.h>
+
+#include <mutex>
+
+namespace c10::cuda {
+C10_CUDA_API const char* get_cuda_check_suffix() noexcept;
+C10_CUDA_API std::mutex* getFreeMutex();
+} // namespace c10::cuda
diff --git a/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAStream.h b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAStream.h
new file mode 100644
index 0000000000000000000000000000000000000000..05c314469f87c067e64e18b6b3448569fad27582
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/cuda/CUDAStream.h
@@ -0,0 +1,268 @@
+#pragma once
+
+#include <cuda_runtime_api.h>
+
+#include <c10/core/DeviceGuard.h>
+#include <c10/core/Stream.h>
+#include <c10/cuda/CUDAFunctions.h>
+#include <c10/util/Exception.h>
+
+/*
+ * Stream pool note.
+ *
+ * A CUDAStream is an abstraction of an actual cuStream on the GPU. CUDAStreams
+ * are backed by cuStreams, but they use several pools to minimize the costs
+ * associated with creating, retaining, and destroying cuStreams.
+ *
+ * There are three pools per device, and a device's pools are lazily created.
+ *
+ * The first pool contains only the default stream. When the default stream
+ * is requested it's returned.
+ *
+ * The second pool is the "low priority" or "default priority" streams. In
+ * HIP builds there is no distinction between streams in this pool and streams
+ * in the third pool (below). There are 32 of these streams per device, and
+ * when a stream is requested one of these streams is returned round-robin.
+ * That is, the first stream requested is at index 0, the second at index 1...
+ * to index 31, then index 0 again.
+ *
+ * This means that if 33 low priority streams are requested, the first and
+ * last streams requested are actually the same stream (under the covers)
+ * and kernels enqueued on them cannot run concurrently.
+ *
+ * The third pool is the "high priority" streams. The third pool acts like
+ * the second pool except the streams are created with a higher priority.
+ *
+ * These pools suggest that stream users should prefer many short-lived streams,
+ * as the cost of acquiring and releasing streams is effectively zero. If
+ * many longer-lived streams are required in performance critical scenarios
+ * then the functionality here may need to be extended to allow, for example,
+ * "reserving" a subset of the pool so that other streams do not accidentally
+ * overlap the performance critical streams.
+ *
+ * Note: although the notion of "current stream for device" is thread local
+ * (every OS thread has a separate current stream, as one might expect),
+ * the stream pool is global across all threads; stream 0 is always stream 0
+ * no matter which thread you use it on.  Multiple threads can synchronize
+ * on the same stream.  Although the CUDA documentation is not very clear
+ * on the matter, streams are thread safe; e.g., it is safe to enqueue
+ * a kernel on the same stream from two different threads.
+ */
+
+namespace c10::cuda {
+
+static constexpr int max_compile_time_stream_priorities = 4;
+
+// Value object representing a CUDA stream.  This is just a wrapper
+// around c10::Stream, but it comes with a little extra CUDA-specific
+// functionality (conversion to cudaStream_t), and a guarantee that
+// the wrapped c10::Stream really is a CUDA stream.
+class C10_CUDA_API CUDAStream {
+ public:
+  enum Unchecked { UNCHECKED };
+
+  /// Construct a CUDAStream from a Stream.  This construction is checked,
+  /// and will raise an error if the Stream is not, in fact, a CUDA stream.
+  explicit CUDAStream(Stream stream) : stream_(stream) {
+    TORCH_CHECK(stream_.device_type() == DeviceType::CUDA);
+  }
+
+  /// Construct a CUDAStream from a Stream with no error checking.
+  /// This constructor uses the "named" constructor idiom, and can
+  /// be invoked as: CUDAStream(CUDAStream::UNCHECKED, stream)
+  explicit CUDAStream(Unchecked, Stream stream) : stream_(stream) {}
+
+  bool operator==(const CUDAStream& other) const noexcept {
+    return unwrap() == other.unwrap();
+  }
+
+  bool operator!=(const CUDAStream& other) const noexcept {
+    return unwrap() != other.unwrap();
+  }
+
+  /// Implicit conversion to cudaStream_t.
+  operator cudaStream_t() const {
+    return stream();
+  }
+
+  /// Implicit conversion to Stream (a.k.a., forget that the stream is a
+  /// CUDA stream).
+  operator Stream() const {
+    return unwrap();
+  }
+
+  /// Used to avoid baking in device type explicitly to Python-side API.
+  DeviceType device_type() const {
+    return DeviceType::CUDA;
+  }
+
+  /// Get the CUDA device index that this stream is associated with.
+  DeviceIndex device_index() const {
+    return stream_.device_index();
+  }
+
+  /// Get the full Device that this stream is associated with.  The Device
+  /// is guaranteed to be a CUDA device.
+  Device device() const {
+    return Device(DeviceType::CUDA, device_index());
+  }
+
+  /// Return the stream ID corresponding to this particular stream.
+  StreamId id() const {
+    return stream_.id();
+  }
+
+  bool query() const {
+    DeviceGuard guard{stream_.device()};
+    cudaError_t err = C10_CUDA_ERROR_HANDLED(cudaStreamQuery(stream()));
+
+    if (err == cudaSuccess) {
+      return true;
+    } else if (err != cudaErrorNotReady) {
+      C10_CUDA_CHECK(err);
+    } else {
+      // ignore and clear the error if not ready
+      (void)cudaGetLastError();
+    }
+
+    return false;
+  }
+
+  void synchronize() const {
+    DeviceGuard guard{stream_.device()};
+    c10::cuda::stream_synchronize(stream());
+  }
+
+  int priority() const {
+    DeviceGuard guard{stream_.device()};
+    int priority = 0;
+    C10_CUDA_CHECK(cudaStreamGetPriority(stream(), &priority));
+    return priority;
+  }
+
+  /// Explicit conversion to cudaStream_t.
+  cudaStream_t stream() const;
+
+  /// Explicit conversion to Stream.
+  Stream unwrap() const {
+    return stream_;
+  }
+
+  /// Reversibly pack a CUDAStream into a struct representation.
+  /// Previously the stream's data was packed into a single int64_t,
+  /// as it was assumed the fields would not require more than
+  /// 64 bits of storage in total.
+  /// See https://github.com/pytorch/pytorch/issues/75854
+  /// for more information regarding newer platforms that may violate
+  /// this assumption.
+  ///
+  /// The CUDAStream can be unpacked using unpack().
+  struct c10::StreamData3 pack3() const {
+    return stream_.pack3();
+  }
+
+  // Unpack a CUDAStream from the 3 fields generated by pack().
+  static CUDAStream unpack3(
+      StreamId stream_id,
+      DeviceIndex device_index,
+      DeviceType device_type) {
+    return CUDAStream(Stream::unpack3(stream_id, device_index, device_type));
+  }
+
+  static std::tuple<int, int> priority_range() {
+    // Note: this returns the range of priority **supported by PyTorch**, not
+    // the range of priority **supported by CUDA**. The former is a subset of
+    // the latter.
+    int least_priority = 0, greatest_priority = 0;
+    C10_CUDA_CHECK(
+        cudaDeviceGetStreamPriorityRange(&least_priority, &greatest_priority));
+#ifdef USE_ROCM
+    // See Note [HIP stream priorities]
+    TORCH_INTERNAL_ASSERT(
+        least_priority == 1, "Unexpected HIP stream priority range");
+    least_priority = 0;
+#else
+    TORCH_INTERNAL_ASSERT(
+        least_priority == 0, "Unexpected CUDA stream priority range");
+#endif
+    TORCH_INTERNAL_ASSERT(
+        greatest_priority <= -1, "Unexpected CUDA stream priority range");
+    greatest_priority = std::max(
+        -c10::cuda::max_compile_time_stream_priorities + 1, greatest_priority);
+    return std::make_tuple(least_priority, greatest_priority);
+  }
+
+  // Deleted for now; use CUDAEvent::block instead
+  // void synchronize_with(const CUDAEvent& event) const;
+
+ private:
+  Stream stream_;
+};
+
+/**
+ * Get a new stream from the CUDA stream pool.  You can think of this
+ * as "creating" a new stream, but no such creation actually happens;
+ * instead, streams are preallocated from the pool and returned in a
+ * round-robin fashion.
+ *
+ * You can request a stream from the high priority pool by setting
+ * isHighPriority to true, or a stream for a specific device by setting device
+ * (defaulting to the current CUDA stream.)
+ */
+C10_API CUDAStream
+getStreamFromPool(const bool isHighPriority = false, DeviceIndex device = -1);
+// no default priority to disambiguate overloads
+C10_API CUDAStream
+getStreamFromPool(const int priority, DeviceIndex device = -1);
+
+/**
+ * Get a CUDAStream from a externally allocated one.
+ *
+ * This is mainly for interoperability with different libraries where we
+ * want to operate on a non-torch allocated stream for data exchange or similar
+ * purposes
+ */
+C10_API CUDAStream
+getStreamFromExternal(cudaStream_t ext_stream, DeviceIndex device_index);
+
+/**
+ * Get the default CUDA stream, for the passed CUDA device, or for the
+ * current device if no device index is passed.  The default stream is
+ * where most computation occurs when you aren't explicitly using
+ * streams.
+ */
+C10_API CUDAStream getDefaultCUDAStream(DeviceIndex device_index = -1);
+
+/**
+ * Get the current CUDA stream, for the passed CUDA device, or for the
+ * current device if no device index is passed.  The current CUDA stream
+ * will usually be the default CUDA stream for the device, but it may
+ * be different if someone called 'setCurrentCUDAStream' or used 'StreamGuard'
+ * or 'CUDAStreamGuard'.
+ */
+C10_API CUDAStream getCurrentCUDAStream(DeviceIndex device_index = -1);
+
+/**
+ * Set the current stream on the device of the passed in stream to be
+ * the passed in stream.  Yes, you read that right: this function
+ * has *nothing* to do with the current device: it toggles the current
+ * stream of the device of the passed stream.
+ *
+ * Confused?  Avoid using this function; prefer using 'CUDAStreamGuard' instead
+ * (which will switch both your current device and current stream in the way you
+ * expect, and reset it back to its original state afterwards).
+ */
+C10_API void setCurrentCUDAStream(CUDAStream stream);
+
+C10_API std::ostream& operator<<(std::ostream& stream, const CUDAStream& s);
+
+} // namespace c10::cuda
+
+namespace std {
+template <>
+struct hash<c10::cuda::CUDAStream> {
+  size_t operator()(c10::cuda::CUDAStream s) const noexcept {
+    return std::hash<c10::Stream>{}(s.unwrap());
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/intrusive_ptr.h b/lib/python3.11/site-packages/torch/include/c10/util/intrusive_ptr.h
new file mode 100644
index 0000000000000000000000000000000000000000..288b19df0a6c88b79250c0e8207187d46d235932
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/intrusive_ptr.h
@@ -0,0 +1,1076 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <c10/util/MaybeOwned.h>
+#include <atomic>
+#include <climits>
+#include <memory>
+#include <type_traits>
+
+namespace pybind11 {
+template <typename, typename...>
+class class_;
+}
+
+namespace c10 {
+class intrusive_ptr_target;
+namespace raw {
+namespace weak_intrusive_ptr {
+inline void incref(intrusive_ptr_target* self);
+}
+namespace intrusive_ptr {
+inline void incref(intrusive_ptr_target* self);
+}
+
+// constructor tag used by intrusive_ptr constructors
+struct DontIncreaseRefcount {};
+} // namespace raw
+
+namespace detail {
+constexpr uint32_t kImpracticallyHugeReferenceCount = 0x0FFFFFFF;
+} // namespace detail
+
+/**
+ * intrusive_ptr<T> is an alternative to shared_ptr<T> that has better
+ * performance because it does the refcounting intrusively
+ * (i.e. in a member of the object itself).
+ * Your class T needs to inherit from intrusive_ptr_target to allow it to be
+ * used in an intrusive_ptr<T>. Your class's constructor should not allow
+ *`this` to escape to other threads or create an intrusive_ptr from `this`.
+ */
+
+// Note [Stack allocated intrusive_ptr_target safety]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// A well known problem with std::enable_shared_from_this is that it
+// allows you to create a std::shared_ptr from a stack allocated object,
+// which is totally bogus because the object will die once you return
+// from the stack.  In intrusive_ptr, we can detect that this has occurred,
+// because we set the refcount/weakcount of objects which inherit from
+// intrusive_ptr_target to zero, *unless* we can prove that the object
+// was dynamically allocated (e.g., via make_intrusive).
+//
+// Thus, whenever you transmute a T* into a intrusive_ptr<T>, we check
+// and make sure that the refcount isn't zero (or, a more subtle
+// test for weak_intrusive_ptr<T>, for which the refcount may validly
+// be zero, but the weak refcount better not be zero), because that
+// tells us if the object was allocated by us.  If it wasn't, no
+// intrusive_ptr for you!
+
+// NOLINTNEXTLINE(cppcoreguidelines-virtual-class-destructor)
+class C10_API intrusive_ptr_target {
+  // Note [Weak references for intrusive refcounting]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // Here's the scheme:
+  //
+  //  - refcount == number of strong references to the object
+  //    weakcount == number of weak references to the object,
+  //      plus one more if refcount > 0
+  //    An invariant: refcount > 0  =>  weakcount > 0
+  //
+  //  - c10::StorageImpl stays live as long as there are any strong
+  //    or weak pointers to it (weakcount > 0, since strong
+  //    references count as a +1 to weakcount)
+  //
+  //  - finalizers are called and data_ptr is deallocated when refcount == 0
+  //
+  //  - Once refcount == 0, it can never again be > 0 (the transition
+  //    from > 0 to == 0 is monotonic)
+  //
+  //  - When you access c10::StorageImpl via a weak pointer, you must
+  //    atomically increment the use count, if it is greater than 0.
+  //    If it is not, you must report that the storage is dead.
+  //
+  mutable std::atomic<uint32_t> refcount_;
+  mutable std::atomic<uint32_t> weakcount_;
+
+  template <typename T, typename NullType>
+  friend class intrusive_ptr;
+  friend inline void raw::intrusive_ptr::incref(intrusive_ptr_target* self);
+
+  template <typename T, typename NullType>
+  friend class weak_intrusive_ptr;
+  friend inline void raw::weak_intrusive_ptr::incref(
+      intrusive_ptr_target* self);
+
+  template <typename T>
+  friend struct ExclusivelyOwnedTensorTraits;
+
+ protected:
+  // protected destructor. We never want to destruct intrusive_ptr_target*
+  // directly.
+  virtual ~intrusive_ptr_target() {
+// Disable -Wterminate and -Wexceptions so we're allowed to use assertions
+// (i.e. throw exceptions) in a destructor.
+// We also have to disable -Wunknown-warning-option and -Wpragmas, because
+// some other compilers don't know about -Wterminate or -Wexceptions and
+// will show a warning about unknown warning options otherwise.
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma warning(push)
+#pragma warning( \
+    disable : 4297) // function assumed not to throw an exception but does
+#else
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wpragmas"
+#pragma GCC diagnostic ignored "-Wunknown-warning-option"
+#pragma GCC diagnostic ignored "-Wterminate"
+#pragma GCC diagnostic ignored "-Wexceptions"
+#endif
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        // Second condition is there to accommodate
+        // unsafe_adapt_non_heap_allocated: since we are doing our own
+        // deallocation in that case, it is correct for each
+        // expected_decref to have happened (some user code tried to
+        // decref and thus free the object, but it didn't happen right
+        // away) or not (no user code tried to free the object, and
+        // now it's getting destroyed through whatever mechanism the
+        // caller of unsafe_adapt_non_heap_allocated wanted to
+        // use). We choose our reference count such that the count
+        // will not dip below kImpracticallyHugeReferenceCount regardless.
+        refcount_.load() == 0 ||
+            refcount_.load() >= detail::kImpracticallyHugeReferenceCount,
+        "Tried to destruct an intrusive_ptr_target that still has intrusive_ptr to it; refcount was ",
+        refcount_.load());
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        // See ~intrusive_ptr for optimization that will frequently result in 1
+        // at destruction time.
+        weakcount_.load() == 1 || weakcount_.load() == 0 ||
+            weakcount_.load() == detail::kImpracticallyHugeReferenceCount - 1 ||
+            weakcount_.load() == detail::kImpracticallyHugeReferenceCount,
+        "Tried to destruct an intrusive_ptr_target that still has weak_intrusive_ptr to it");
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma warning(pop)
+#else
+#pragma GCC diagnostic pop
+#endif
+  }
+
+  constexpr intrusive_ptr_target() noexcept : refcount_(0), weakcount_(0) {}
+
+  // intrusive_ptr_target supports copy and move: but refcount and weakcount
+  // don't participate (since they are intrinsic properties of the memory
+  // location)
+  intrusive_ptr_target(intrusive_ptr_target&& /*other*/) noexcept
+      : intrusive_ptr_target() {}
+
+  intrusive_ptr_target& operator=(intrusive_ptr_target&& /*other*/) noexcept {
+    return *this;
+  }
+
+  intrusive_ptr_target(const intrusive_ptr_target& /*other*/) noexcept
+      : intrusive_ptr_target() {}
+
+  intrusive_ptr_target& operator=(
+      const intrusive_ptr_target& /*other*/) noexcept {
+    return *this;
+  }
+
+ private:
+  /**
+   * This is called when refcount reaches zero.
+   * You can override this to release expensive resources.
+   * There might still be weak references, so your object might not get
+   * destructed yet, but you can assume the object isn't used anymore,
+   * i.e. no more calls to methods or accesses to members (we just can't
+   * destruct it yet because we need the weakcount accessible).
+   *
+   * If there are no weak references (i.e. your class is about to be
+   * destructed), this function WILL NOT be called.
+   */
+  virtual void release_resources() {}
+};
+
+namespace detail {
+template <class TTarget>
+struct intrusive_target_default_null_type final {
+  static constexpr TTarget* singleton() noexcept {
+    return nullptr;
+  }
+};
+
+template <class TTarget, class ToNullType, class FromNullType>
+TTarget* assign_ptr_(TTarget* rhs) {
+  if (FromNullType::singleton() == rhs) {
+    return ToNullType::singleton();
+  } else {
+    return rhs;
+  }
+}
+
+// Increment needs to be acquire-release to make use_count() and
+// unique() reliable.
+inline uint32_t atomic_refcount_increment(std::atomic<uint32_t>& refcount) {
+  return refcount.fetch_add(1, std::memory_order_acq_rel) + 1;
+}
+
+// weak_use_count() is only used for testing, so we don't need it to
+// be reliable. Relaxed should be fine.
+inline uint32_t atomic_weakcount_increment(std::atomic<uint32_t>& weakcount) {
+  return weakcount.fetch_add(1, std::memory_order_relaxed) + 1;
+}
+
+// Both decrements need to be acquire-release for correctness. See
+// e.g. std::shared_ptr implementation.
+inline uint32_t atomic_refcount_decrement(std::atomic<uint32_t>& refcount) {
+  return refcount.fetch_sub(1, std::memory_order_acq_rel) - 1;
+}
+
+inline uint32_t atomic_weakcount_decrement(std::atomic<uint32_t>& weakcount) {
+  return weakcount.fetch_sub(1, std::memory_order_acq_rel) - 1;
+}
+
+} // namespace detail
+
+template <class TTarget, class NullType>
+class weak_intrusive_ptr;
+
+template <
+    class TTarget,
+    class NullType = detail::intrusive_target_default_null_type<TTarget>>
+class intrusive_ptr final {
+ private:
+//  the following static assert would be nice to have but it requires
+//  the target class T to be fully defined when intrusive_ptr<T> is instantiated
+//  this is a problem for classes that contain pointers to themselves
+//  static_assert(
+//      std::is_base_of_v<intrusive_ptr_target, TTarget>,
+//      "intrusive_ptr can only be used for classes that inherit from
+//      intrusive_ptr_target.");
+#ifndef _WIN32
+  // This static_assert triggers on MSVC
+  //  error C2131: expression did not evaluate to a constant
+  static_assert(
+      // NOLINTNEXTLINE(misc-redundant-expression)
+      NullType::singleton() == NullType::singleton(),
+      "NullType must have a constexpr singleton() method");
+#endif
+  static_assert(
+      std::is_base_of_v<
+          TTarget,
+          std::remove_pointer_t<decltype(NullType::singleton())>>,
+      "NullType::singleton() must return a element_type* pointer");
+
+  TTarget* target_;
+
+  template <typename T>
+  friend struct ExclusivelyOwnedTensorTraits;
+  template <class TTarget2, class NullType2>
+  friend class intrusive_ptr;
+  friend class weak_intrusive_ptr<TTarget, NullType>;
+
+  // Make pybind11::class_ be a friend class of intrusive_ptr, so that custom
+  // smart holder in pybind11 could access the private constructor of
+  // intrusive_ptr(T*) which took the ownership of the object. This is required
+  // by customer holder macro PYBIND11_DECLARE_HOLDER_TYPE, where it uses
+  // intrusive_ptr(TTarget*) to initialize and take ownership of the object. For
+  // details, see
+  // https://pybind11.readthedocs.io/en/stable/advanced/smart_ptrs.html#custom-smart-pointers
+  template <typename, typename...>
+  friend class pybind11::class_;
+
+  void retain_() {
+    if (target_ != NullType::singleton()) {
+      uint32_t new_refcount =
+          detail::atomic_refcount_increment(target_->refcount_);
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          new_refcount != 1,
+          "intrusive_ptr: Cannot increase refcount after it reached zero.");
+    }
+  }
+
+  void reset_() noexcept {
+    if (target_ != NullType::singleton() &&
+        detail::atomic_refcount_decrement(target_->refcount_) == 0) {
+      // See comment above about weakcount. As long as refcount>0,
+      // weakcount is one larger than the actual number of weak references.
+      // So we need to decrement it here.
+      bool should_delete =
+          target_->weakcount_.load(std::memory_order_acquire) == 1;
+      if (!should_delete) {
+        // justification for const_cast: release_resources is basically a
+        // destructor and a destructor always mutates the object, even for const
+        // objects. NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+        const_cast<std::remove_const_t<TTarget>*>(target_)->release_resources();
+        should_delete =
+            detail::atomic_weakcount_decrement(target_->weakcount_) == 0;
+      }
+      if (should_delete) {
+        delete target_;
+      }
+    }
+  }
+
+  // raw pointer constructors are not public because we shouldn't make
+  // intrusive_ptr out of raw pointers except from inside the make_intrusive(),
+  // reclaim() and weak_intrusive_ptr::lock() implementations.
+
+  // This constructor will increase the ref counter for you.
+  // This constructor will be used by the make_intrusive(), and also pybind11,
+  // which wrap the intrusive_ptr holder around the raw pointer and incref
+  // correspondingly (pybind11 requires raw pointer constructor to incref by
+  // default).
+  explicit intrusive_ptr(TTarget* target)
+      : intrusive_ptr(target, raw::DontIncreaseRefcount{}) {
+    if (target_ != NullType::singleton()) {
+      // We just created result.target_, so we know no other thread has
+      // access to it, so we know we needn't care about memory ordering.
+      // (On x86_64, a store with memory_order_relaxed generates a plain old
+      // `mov`, whereas an atomic increment does a lock-prefixed `add`, which is
+      // much more expensive: https://godbolt.org/z/eKPzj8.)
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          target_->refcount_ == 0 && target_->weakcount_ == 0,
+          "intrusive_ptr: Newly-created target had non-zero refcounts. Does its "
+          "constructor do something strange like incref or create an "
+          "intrusive_ptr from `this`?");
+      target_->refcount_.store(1, std::memory_order_relaxed);
+      target_->weakcount_.store(1, std::memory_order_relaxed);
+    }
+  }
+
+ public:
+  using element_type = TTarget;
+
+  intrusive_ptr() noexcept
+      : intrusive_ptr(NullType::singleton(), raw::DontIncreaseRefcount{}) {}
+
+  intrusive_ptr(std::nullptr_t) noexcept
+      : intrusive_ptr(NullType::singleton(), raw::DontIncreaseRefcount{}) {}
+
+  // This constructor will not increase the ref counter for you.
+  // We use the tagged dispatch mechanism to explicitly mark this constructor
+  // to not increase the refcount
+  explicit intrusive_ptr(TTarget* target, raw::DontIncreaseRefcount) noexcept
+      : target_(target) {}
+
+  explicit intrusive_ptr(std::unique_ptr<TTarget> rhs) noexcept
+      : intrusive_ptr(rhs.release()) {}
+
+  intrusive_ptr(intrusive_ptr&& rhs) noexcept : target_(rhs.target_) {
+    rhs.target_ = NullType::singleton();
+  }
+
+  template <class From, class FromNullType>
+  // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+  /* implicit */ intrusive_ptr(intrusive_ptr<From, FromNullType>&& rhs) noexcept
+      : target_(
+            detail::assign_ptr_<TTarget, NullType, FromNullType>(rhs.target_)) {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. intrusive_ptr move constructor got pointer of wrong type.");
+    rhs.target_ = FromNullType::singleton();
+  }
+
+  intrusive_ptr(const intrusive_ptr& rhs) : target_(rhs.target_) {
+    retain_();
+  }
+
+  template <class From, class FromNullType>
+  /* implicit */ intrusive_ptr(const intrusive_ptr<From, FromNullType>& rhs)
+      : target_(
+            detail::assign_ptr_<TTarget, NullType, FromNullType>(rhs.target_)) {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. intrusive_ptr copy constructor got pointer of wrong type.");
+    retain_();
+  }
+
+  ~intrusive_ptr() noexcept {
+    reset_();
+  }
+
+  intrusive_ptr& operator=(intrusive_ptr&& rhs) & noexcept {
+    // NOLINTNEXTLINE(*assign*)
+    return this->template operator= <TTarget, NullType>(std::move(rhs));
+  }
+
+  template <class From, class FromNullType>
+  intrusive_ptr& operator=(intrusive_ptr<From, FromNullType>&& rhs) & noexcept {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. intrusive_ptr move assignment got pointer of wrong type.");
+    intrusive_ptr tmp = std::move(rhs);
+    swap(tmp);
+    return *this;
+  }
+
+  // Assignment is implemented using copy and swap. That's safe for self
+  // assignment.
+  // NOLINTNEXTLINE(bugprone-unhandled-self-assignment)
+  intrusive_ptr& operator=(const intrusive_ptr& rhs) & noexcept {
+    // NOLINTNEXTLINE(*assign-operator, *assignment-signature)
+    return this->template operator= <TTarget, NullType>(rhs);
+  }
+
+  template <class From, class FromNullType>
+  intrusive_ptr& operator=(
+      const intrusive_ptr<From, NullType>& rhs) & noexcept {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. intrusive_ptr copy assignment got pointer of wrong type.");
+    intrusive_ptr tmp = rhs;
+    swap(tmp);
+    return *this;
+  }
+
+  TTarget* get() const noexcept {
+    return target_;
+  }
+
+  TTarget& operator*() const noexcept {
+    return *target_;
+  }
+
+  TTarget* operator->() const noexcept {
+    return target_;
+  }
+
+  operator bool() const noexcept {
+    return target_ != NullType::singleton();
+  }
+
+  void reset() noexcept {
+    reset_();
+    target_ = NullType::singleton();
+  }
+
+  void swap(intrusive_ptr& rhs) noexcept {
+    std::swap(target_, rhs.target_);
+  }
+
+  // We do a lot of null-pointer checks in our code, good to have this be cheap.
+  bool defined() const noexcept {
+    return target_ != NullType::singleton();
+  }
+
+  uint32_t use_count() const noexcept {
+    if (target_ == NullType::singleton()) {
+      return 0;
+    }
+    return target_->refcount_.load(std::memory_order_acquire);
+  }
+
+  uint32_t weak_use_count() const noexcept {
+    if (target_ == NullType::singleton()) {
+      return 0;
+    }
+    return target_->weakcount_.load(std::memory_order_acquire);
+  }
+
+  bool unique() const noexcept {
+    return use_count() == 1;
+  }
+
+  /**
+   * Returns an owning (!) pointer to the underlying object and makes the
+   * intrusive_ptr instance invalid. That means the refcount is not decreased.
+   * You *must* put the returned pointer back into a intrusive_ptr using
+   * intrusive_ptr::reclaim(ptr) to properly destruct it.
+   * This is helpful for C APIs.
+   */
+  TTarget* release() noexcept {
+    // NOLINTNEXTLINE(clang-analyzer-core.uninitialized.Assign)
+    TTarget* result = target_;
+    target_ = NullType::singleton();
+    return result;
+  }
+
+  /**
+   * Takes an owning pointer to TTarget* and creates an intrusive_ptr that takes
+   * over ownership. That means the refcount is not increased.
+   * This is the counter-part to intrusive_ptr::release() and the pointer
+   * passed in *must* have been created using intrusive_ptr::release().
+   */
+  static intrusive_ptr reclaim(TTarget* owning_ptr) {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        owning_ptr == NullType::singleton() ||
+            owning_ptr->refcount_.load() == 0 || owning_ptr->weakcount_.load(),
+        "TTarget violates the invariant that refcount > 0  =>  weakcount > 0");
+    return intrusive_ptr(owning_ptr, raw::DontIncreaseRefcount{});
+  }
+
+  /**
+   * Takes an owning pointer to TTarget* and creates an intrusive_ptr
+   * representing a new reference, i.e. the raw pointer retains
+   * ownership.
+   */
+  static intrusive_ptr reclaim_copy(TTarget* owning_ptr) {
+    auto ret = reclaim(owning_ptr);
+    ret.retain_();
+    return ret;
+  }
+
+  /**
+   * Allocate a heap object with args and wrap it inside a intrusive_ptr and
+   * incref. This is a helper function to let make_intrusive() access private
+   * intrusive_ptr constructors.
+   */
+  template <class... Args>
+  static intrusive_ptr make(Args&&... args) {
+    return intrusive_ptr(new TTarget(std::forward<Args>(args)...));
+  }
+
+  /**
+   * Turn a new instance of TTarget (e.g., literally allocated
+   * using new TTarget(...) into an intrusive_ptr.  If possible,
+   * use intrusive_ptr::make instead which statically guarantees
+   * that the allocation was done properly.
+   *
+   * At the moment, the only reason this method exists is because
+   * pybind11 holder types expect to be able to allocate in
+   * this way (because pybind11 handles the new allocation itself).
+   */
+  static intrusive_ptr unsafe_steal_from_new(TTarget* raw_ptr) {
+    return intrusive_ptr(raw_ptr);
+  }
+
+  /**
+   * Turn an instance of TTarget that should not be reference counted
+   * (e.g., allocated into an arena with placement new) into an
+   * intrusive_ptr. This is gratuitously unsafe and should only be
+   * used if you can guarantee that the pointer will not escape and be
+   * refcounted as normal.
+   *
+   * `expected_decrefs` is a debugging parameter: it indicates the
+   * number of strong owners the intrusive_ptr_target in question is
+   * expected to get. In most use cases, this will likely be 1.
+   *
+   * The reason this method exists is for manually sharing
+   * StorageImpls across Tensors in the static runtime. It needs
+   * access to private intrusive_ptr members so that the refcounts can
+   * be initialized to custom values.
+   */
+  static intrusive_ptr unsafe_adapt_non_heap_allocated(
+      TTarget* raw_ptr,
+      uint32_t expected_decrefs) {
+    intrusive_ptr result(raw_ptr, raw::DontIncreaseRefcount{});
+    // kImpracticallyHugeReferenceCount is impractically huge for a reference
+    // count, while being in no danger of overflowing uint32_t. We actually only
+    // need to initialize the refcount to 2 -- we are just doing an unbalanced
+    // incref to prevent the non-heap-allocated target from being
+    // freed, and we are optimizing that incref by directly
+    // initializing the refcounts rather than doing an expensive
+    // atomic increment. The reason to use kImpracticallyHugeReferenceCount is
+    // to accommodate the debug assertions in ~intrusive_ptr_target.
+#ifdef NDEBUG
+    expected_decrefs = 0;
+#endif
+    result.target_->refcount_.store(
+        detail::kImpracticallyHugeReferenceCount + expected_decrefs,
+        std::memory_order_relaxed);
+    result.target_->weakcount_.store(
+        detail::kImpracticallyHugeReferenceCount, std::memory_order_relaxed);
+    return result;
+  }
+
+  /**
+   * Turn a **non-owning raw pointer** to an intrusive_ptr.  It is
+   * the moral equivalent of enable_shared_from_this on a shared pointer.
+   *
+   * This method is only valid for objects that are already live.  If
+   * you are looking for the moral equivalent of unique_ptr<T>(T*)
+   * constructor, see steal_from_new.
+   *
+   * TODO: https://github.com/pytorch/pytorch/issues/56482
+   */
+  static intrusive_ptr unsafe_reclaim_from_nonowning(TTarget* raw_ptr) {
+    // See Note [Stack allocated intrusive_ptr_target safety]
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        raw_ptr == NullType::singleton() || raw_ptr->refcount_.load() > 0,
+        "intrusive_ptr: Can only reclaim pointers that are owned by someone");
+    auto ptr = reclaim(raw_ptr); // doesn't increase refcount
+    ptr.retain_();
+    return ptr;
+  }
+};
+
+template <
+    class TTarget,
+    class NullType = detail::intrusive_target_default_null_type<TTarget>,
+    class... Args>
+inline intrusive_ptr<TTarget, NullType> make_intrusive(Args&&... args) {
+  return intrusive_ptr<TTarget, NullType>::make(std::forward<Args>(args)...);
+}
+
+template <class TTarget, class NullType>
+inline void swap(
+    intrusive_ptr<TTarget, NullType>& lhs,
+    intrusive_ptr<TTarget, NullType>& rhs) noexcept {
+  lhs.swap(rhs);
+}
+
+// To allow intrusive_ptr inside std::map or std::set, we need operator<
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator<(
+    const intrusive_ptr<TTarget1, NullType1>& lhs,
+    const intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return lhs.get() < rhs.get();
+}
+
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator==(
+    const intrusive_ptr<TTarget1, NullType1>& lhs,
+    const intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return lhs.get() == rhs.get();
+}
+
+template <class TTarget1, class NullType1>
+inline bool operator==(
+    const intrusive_ptr<TTarget1, NullType1>& lhs,
+    std::nullptr_t) noexcept {
+  return lhs.get() == nullptr;
+}
+
+template <class TTarget2, class NullType2>
+inline bool operator==(
+    std::nullptr_t,
+    const intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return nullptr == rhs.get();
+}
+
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator!=(
+    const intrusive_ptr<TTarget1, NullType1>& lhs,
+    const intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return !operator==(lhs, rhs);
+}
+
+template <class TTarget1, class NullType1>
+inline bool operator!=(
+    const intrusive_ptr<TTarget1, NullType1>& lhs,
+    std::nullptr_t) noexcept {
+  return !operator==(lhs, nullptr);
+}
+
+template <class TTarget2, class NullType2>
+inline bool operator!=(
+    std::nullptr_t,
+    const intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return !operator==(nullptr, rhs);
+}
+template <typename T>
+struct MaybeOwnedTraits<c10::intrusive_ptr<T>> {
+  using owned_type = c10::intrusive_ptr<T>;
+  using borrow_type = c10::intrusive_ptr<T>;
+
+  static borrow_type createBorrow(const owned_type& from) {
+    return borrow_type::reclaim(from.get());
+  }
+
+  static void assignBorrow(borrow_type& lhs, const borrow_type& rhs) {
+    lhs.release();
+    lhs = borrow_type::reclaim(rhs.get());
+  }
+
+  static void destroyBorrow(borrow_type& toDestroy) {
+    toDestroy.release();
+  }
+
+  static const owned_type& referenceFromBorrow(
+      const borrow_type& borrow) noexcept {
+    return borrow;
+  }
+
+  static const owned_type* pointerFromBorrow(
+      const borrow_type& borrow) noexcept {
+    return &borrow;
+  }
+
+  static bool debugBorrowIsValid(const borrow_type& /*borrow*/) noexcept {
+    return true;
+  }
+};
+
+template <
+    typename TTarget,
+    class NullType = detail::intrusive_target_default_null_type<TTarget>>
+class weak_intrusive_ptr final {
+ private:
+  static_assert(
+      std::is_base_of_v<intrusive_ptr_target, TTarget>,
+      "intrusive_ptr can only be used for classes that inherit from intrusive_ptr_target.");
+#ifndef _WIN32
+  // This static_assert triggers on MSVC
+  //  error C2131: expression did not evaluate to a constant
+  static_assert(
+      NullType::singleton() == NullType::singleton(),
+      "NullType must have a constexpr singleton() method");
+#endif
+  static_assert(
+      std::is_base_of_v<
+          TTarget,
+          std::remove_pointer_t<decltype(NullType::singleton())>>,
+      "NullType::singleton() must return a element_type* pointer");
+
+  TTarget* target_;
+
+  template <class TTarget2, class NullType2>
+  friend class weak_intrusive_ptr;
+
+  void retain_() {
+    if (target_ != NullType::singleton()) {
+      uint32_t new_weakcount =
+          detail::atomic_weakcount_increment(target_->weakcount_);
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          new_weakcount != 1,
+          "weak_intrusive_ptr: Cannot increase weakcount after it reached zero.");
+    }
+  }
+
+  void reset_() noexcept {
+    if (target_ != NullType::singleton() &&
+        detail::atomic_weakcount_decrement(target_->weakcount_) == 0) {
+      // NOLINTNEXTLINE(clang-analyzer-cplusplus.NewDelete)
+      delete target_;
+    }
+    target_ = NullType::singleton();
+  }
+
+  constexpr explicit weak_intrusive_ptr(TTarget* target) : target_(target) {}
+
+ public:
+  using element_type = TTarget;
+
+  explicit weak_intrusive_ptr(const intrusive_ptr<TTarget, NullType>& ptr)
+      : weak_intrusive_ptr(ptr.get()) {
+    retain_();
+  }
+
+  weak_intrusive_ptr(weak_intrusive_ptr&& rhs) noexcept : target_(rhs.target_) {
+    rhs.target_ = NullType::singleton();
+  }
+
+  template <class From, class FromNullType>
+  /* implicit */ weak_intrusive_ptr(
+      // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+      weak_intrusive_ptr<From, FromNullType>&& rhs) noexcept
+      : target_(
+            detail::assign_ptr_<TTarget, NullType, FromNullType>(rhs.target_)) {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. weak_intrusive_ptr move constructor got pointer of wrong type.");
+    rhs.target_ = FromNullType::singleton();
+  }
+
+  weak_intrusive_ptr(const weak_intrusive_ptr& rhs) : target_(rhs.target_) {
+    retain_();
+  }
+
+  template <class From, class FromNullType>
+  /* implicit */ weak_intrusive_ptr(
+      const weak_intrusive_ptr<From, FromNullType>& rhs)
+      : target_(
+            detail::assign_ptr_<TTarget, NullType, FromNullType>(rhs.target_)) {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. weak_intrusive_ptr copy constructor got pointer of wrong type.");
+    retain_();
+  }
+
+  ~weak_intrusive_ptr() noexcept {
+    reset_();
+  }
+
+  weak_intrusive_ptr& operator=(weak_intrusive_ptr&& rhs) & noexcept {
+    // NOLINTNEXTLINE(*assign*)
+    return this->template operator= <TTarget, NullType>(std::move(rhs));
+  }
+
+  template <class From, class FromNullType>
+  weak_intrusive_ptr& operator=(
+      weak_intrusive_ptr<From, FromNullType>&& rhs) & noexcept {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. weak_intrusive_ptr move assignment got pointer of wrong type.");
+    weak_intrusive_ptr tmp = std::move(rhs);
+    swap(tmp);
+    return *this;
+  }
+
+  weak_intrusive_ptr& operator=(const weak_intrusive_ptr& rhs) & noexcept {
+    if (this == &rhs) {
+      return *this;
+    }
+    // NOLINTNEXTLINE(*assign*)
+    return this->template operator= <TTarget, NullType>(rhs);
+  }
+
+  weak_intrusive_ptr& operator=(
+      const intrusive_ptr<TTarget, NullType>& rhs) & noexcept {
+    weak_intrusive_ptr tmp(rhs);
+    swap(tmp);
+    return *this;
+  }
+
+  template <class From, class FromNullType>
+  weak_intrusive_ptr& operator=(
+      const weak_intrusive_ptr<From, NullType>& rhs) & noexcept {
+    static_assert(
+        std::is_convertible_v<From*, TTarget*>,
+        "Type mismatch. weak_intrusive_ptr copy assignment got pointer of wrong type.");
+    weak_intrusive_ptr tmp = rhs;
+    swap(tmp);
+    return *this;
+  }
+
+  void reset() noexcept {
+    reset_();
+  }
+
+  void swap(weak_intrusive_ptr& rhs) noexcept {
+    TTarget* tmp = target_;
+    target_ = rhs.target_;
+    rhs.target_ = tmp;
+  }
+
+  // NB: This should ONLY be used by the std::hash implementation
+  // for weak_intrusive_ptr.  Another way you could do this is
+  // friend std::hash<weak_intrusive_ptr>, but this triggers two
+  // bugs:
+  //
+  //  (1) It triggers an nvcc bug, where std::hash in a friend class
+  //      declaration gets preprocessed into hash, which then cannot
+  //      actually be found.  The error in this case looks like:
+  //
+  //        error: no template named 'hash'; did you mean 'std::hash'?
+  //
+  //  (2) On OS X, std::hash is declared as a struct, not a class.
+  //      This twings:
+  //
+  //        error: class 'hash' was previously declared as a struct
+  //        [-Werror,-Wmismatched-tags]
+  //
+  // Both of these are work-aroundable, but on the whole, I decided
+  // it would be simpler and easier to make work if we just expose
+  // an unsafe getter for target_
+  //
+  TTarget* _unsafe_get_target() const noexcept {
+    return target_;
+  }
+
+  uint32_t use_count() const noexcept {
+    if (target_ == NullType::singleton()) {
+      return 0;
+    }
+    return target_->refcount_.load(
+        std::memory_order_acquire); // refcount, not weakcount!
+  }
+
+  uint32_t weak_use_count() const noexcept {
+    if (target_ == NullType::singleton()) {
+      return 0;
+    }
+    return target_->weakcount_.load(std::memory_order_acquire);
+  }
+
+  bool expired() const noexcept {
+    return use_count() == 0;
+  }
+
+  intrusive_ptr<TTarget, NullType> lock() const noexcept {
+    if (expired()) {
+      return intrusive_ptr<TTarget, NullType>();
+    } else {
+      auto refcount = target_->refcount_.load(std::memory_order_seq_cst);
+      do {
+        if (refcount == 0) {
+          // Object already destructed, no strong references left anymore.
+          // Return nullptr.
+          return intrusive_ptr<TTarget, NullType>();
+        }
+      } while (
+          !target_->refcount_.compare_exchange_weak(refcount, refcount + 1));
+      return intrusive_ptr<TTarget, NullType>(
+          target_, raw::DontIncreaseRefcount{});
+    }
+  }
+
+  /**
+   * Returns an owning (but still only weakly referenced) pointer to the
+   * underlying object and makes the weak_intrusive_ptr instance invalid.
+   * That means the weakcount is not decreased.
+   * You *must* put the returned pointer back into a weak_intrusive_ptr using
+   * weak_intrusive_ptr::reclaim(ptr) to properly destruct it.
+   * This is helpful for C APIs.
+   */
+  TTarget* release() noexcept {
+    TTarget* result = target_;
+    target_ = NullType::singleton();
+    return result;
+  }
+
+  /**
+   * Takes an owning (but must be weakly referenced) pointer to TTarget* and
+   * creates a weak_intrusive_ptr that takes over ownership.
+   * This means that the weakcount is not increased.
+   * This is the counter-part to weak_intrusive_ptr::release() and the pointer
+   * passed in *must* have been created using weak_intrusive_ptr::release().
+   */
+  static weak_intrusive_ptr reclaim(TTarget* owning_weak_ptr) {
+    // See Note [Stack allocated intrusive_ptr_target safety]
+    // if refcount > 0, weakcount must be >1 for weak references to exist.
+    // see weak counting explanation at top of this file.
+    // if refcount == 0, weakcount only must be >0.
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        owning_weak_ptr == NullType::singleton() ||
+            owning_weak_ptr->weakcount_.load() > 1 ||
+            (owning_weak_ptr->refcount_.load() == 0 &&
+             owning_weak_ptr->weakcount_.load() > 0),
+        "weak_intrusive_ptr: Can only weak_intrusive_ptr::reclaim() owning pointers that were created using weak_intrusive_ptr::release().");
+    return weak_intrusive_ptr(owning_weak_ptr);
+  }
+
+  /**
+   * Takes a pointer to TTarget* (may be weak or strong) and creates a
+   * new weak_intrusive_ptr representing a new weak reference, i.e.
+   * the raw pointer retains ownership.
+   */
+  static weak_intrusive_ptr reclaim_copy(TTarget* owning_ptr) {
+    auto ret = reclaim(owning_ptr);
+    ret.retain_();
+    return ret;
+  }
+
+  template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+  friend bool operator<(
+      const weak_intrusive_ptr<TTarget1, NullType1>& lhs,
+      const weak_intrusive_ptr<TTarget2, NullType2>& rhs) noexcept;
+  template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+  friend bool operator==(
+      const weak_intrusive_ptr<TTarget1, NullType1>& lhs,
+      const weak_intrusive_ptr<TTarget2, NullType2>& rhs) noexcept;
+};
+
+template <class TTarget, class NullType>
+inline void swap(
+    weak_intrusive_ptr<TTarget, NullType>& lhs,
+    weak_intrusive_ptr<TTarget, NullType>& rhs) noexcept {
+  lhs.swap(rhs);
+}
+
+// To allow weak_intrusive_ptr inside std::map or std::set, we need operator<
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator<(
+    const weak_intrusive_ptr<TTarget1, NullType1>& lhs,
+    const weak_intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return lhs.target_ < rhs.target_;
+}
+
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator==(
+    const weak_intrusive_ptr<TTarget1, NullType1>& lhs,
+    const weak_intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return lhs.target_ == rhs.target_;
+}
+
+template <class TTarget1, class NullType1, class TTarget2, class NullType2>
+inline bool operator!=(
+    const weak_intrusive_ptr<TTarget1, NullType1>& lhs,
+    const weak_intrusive_ptr<TTarget2, NullType2>& rhs) noexcept {
+  return !operator==(lhs, rhs);
+}
+
+// Alias for documentary purposes, to more easily distinguish
+// weak raw intrusive pointers from intrusive pointers.
+using weak_intrusive_ptr_target = intrusive_ptr_target;
+
+// This namespace provides some methods for working with
+// raw pointers that subclass intrusive_ptr_target.  They are not provided
+// as methods on intrusive_ptr_target, because ideally you would not need these
+// methods at all (use smart pointers), but if you are dealing with legacy code
+// that still needs to pass around raw pointers, you may find these quite
+// useful.
+//
+// An important usage note: some functions are only valid if you have a
+// strong raw pointer to the object, while others are only valid if you
+// have a weak raw pointer to the object.  ONLY call intrusive_ptr namespace
+// functions on strong pointers, and weak_intrusive_ptr namespace functions
+// on weak pointers.  If you mix it up, you may get an assert failure.
+namespace raw {
+
+namespace intrusive_ptr {
+
+// WARNING: Unlike the reclaim() API, it is NOT valid to pass
+// NullType::singleton to this function
+inline void incref(intrusive_ptr_target* self) {
+  if (self) {
+    detail::atomic_refcount_increment(self->refcount_);
+  }
+}
+
+// WARNING: Unlike the reclaim() API, it is NOT valid to pass
+// NullType::singleton to this function
+inline void decref(intrusive_ptr_target* self) {
+  // Let it die
+  c10::intrusive_ptr<intrusive_ptr_target>::reclaim(self);
+  // NB: Caller still has 'self' pointer, but it's now invalid.
+  // If you want more safety, used the actual c10::intrusive_ptr class
+}
+
+template <typename T>
+inline T* make_weak(T* self) {
+  // NB: 'this' is a strong pointer, but we return a weak pointer
+  auto ptr = c10::intrusive_ptr<T>::reclaim(self);
+  c10::weak_intrusive_ptr<T> wptr(ptr);
+  ptr.release();
+  return wptr.release();
+}
+
+inline uint32_t use_count(intrusive_ptr_target* self) {
+  auto ptr = c10::intrusive_ptr<intrusive_ptr_target>::reclaim(self);
+  auto r = ptr.use_count();
+  ptr.release();
+  return r;
+}
+
+} // namespace intrusive_ptr
+
+namespace weak_intrusive_ptr {
+
+inline void incref(weak_intrusive_ptr_target* self) {
+  detail::atomic_weakcount_increment(self->weakcount_);
+}
+
+inline void decref(weak_intrusive_ptr_target* self) {
+  // Let it die
+  c10::weak_intrusive_ptr<intrusive_ptr_target>::reclaim(self);
+  // NB: You still "have" the 'self' pointer, but it's now invalid.
+  // If you want more safety, used the actual c10::weak_intrusive_ptr class
+}
+
+template <typename T>
+inline T* lock(T* self) {
+  auto wptr = c10::weak_intrusive_ptr<T>::reclaim(self);
+  auto ptr = wptr.lock();
+  wptr.release();
+  return ptr.release();
+}
+
+// This gives the STRONG refcount of a WEAK pointer
+inline uint32_t use_count(weak_intrusive_ptr_target* self) {
+  auto wptr = c10::weak_intrusive_ptr<intrusive_ptr_target>::reclaim(self);
+  auto r = wptr.use_count();
+  wptr.release();
+  return r;
+}
+
+} // namespace weak_intrusive_ptr
+
+} // namespace raw
+
+} // namespace c10
+
+namespace std {
+// To allow intrusive_ptr and weak_intrusive_ptr inside std::unordered_map or
+// std::unordered_set, we need std::hash
+template <class TTarget, class NullType>
+struct hash<c10::intrusive_ptr<TTarget, NullType>> {
+  size_t operator()(const c10::intrusive_ptr<TTarget, NullType>& x) const {
+    return std::hash<TTarget*>()(x.get());
+  }
+};
+template <class TTarget, class NullType>
+struct hash<c10::weak_intrusive_ptr<TTarget, NullType>> {
+  size_t operator()(const c10::weak_intrusive_ptr<TTarget, NullType>& x) const {
+    return std::hash<TTarget*>()(x._unsafe_get_target());
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/llvmMathExtras.h b/lib/python3.11/site-packages/torch/include/c10/util/llvmMathExtras.h
new file mode 100644
index 0000000000000000000000000000000000000000..82651f7858c773fe4592dc915a3c1fc967038ddb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/llvmMathExtras.h
@@ -0,0 +1,906 @@
+//===-- llvm/Support/MathExtras.h - Useful math functions -------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains some functions that are useful for math stuff.
+//
+//===----------------------------------------------------------------------===//
+
+#pragma once
+
+#include <c10/util/bit_cast.h>
+
+#include <algorithm>
+#include <cassert>
+#include <climits>
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <limits>
+#include <type_traits>
+
+#ifdef __ANDROID_NDK__
+#include <android/api-level.h>
+#endif
+
+#ifndef __has_builtin
+#define __has_builtin(x) 0
+#endif
+
+#ifndef LLVM_GNUC_PREREQ
+#if defined(__GNUC__) && defined(__GNUC_MINOR__) && defined(__GNUC_PATCHLEVEL__)
+#define LLVM_GNUC_PREREQ(maj, min, patch)                             \
+  ((__GNUC__ << 20) + (__GNUC_MINOR__ << 10) + __GNUC_PATCHLEVEL__ >= \
+   ((maj) << 20) + ((min) << 10) + (patch))
+#elif defined(__GNUC__) && defined(__GNUC_MINOR__)
+#define LLVM_GNUC_PREREQ(maj, min, patch) \
+  ((__GNUC__ << 20) + (__GNUC_MINOR__ << 10) >= ((maj) << 20) + ((min) << 10))
+#else
+#define LLVM_GNUC_PREREQ(maj, min, patch) 0
+#endif
+#endif
+
+#ifdef _MSC_VER
+// Declare these intrinsics manually rather including intrin.h. It's very
+// expensive, and MathExtras.h is popular.
+// #include <intrin.h>
+extern "C" {
+unsigned char _BitScanForward(unsigned long* _Index, unsigned long _Mask);
+unsigned char _BitScanForward64(unsigned long* _Index, unsigned __int64 _Mask);
+unsigned char _BitScanReverse(unsigned long* _Index, unsigned long _Mask);
+unsigned char _BitScanReverse64(unsigned long* _Index, unsigned __int64 _Mask);
+}
+#endif
+
+namespace c10::llvm {
+/// The behavior an operation has on an input of 0.
+enum ZeroBehavior {
+  /// The returned value is undefined.
+  ZB_Undefined,
+  /// The returned value is numeric_limits<T>::max()
+  ZB_Max,
+  /// The returned value is numeric_limits<T>::digits
+  ZB_Width
+};
+
+namespace detail {
+template <typename T, std::size_t SizeOfT>
+struct TrailingZerosCounter {
+  static std::size_t count(T Val, ZeroBehavior) {
+    if (!Val)
+      return std::numeric_limits<T>::digits;
+    if (Val & 0x1)
+      return 0;
+
+    // Bisection method.
+    std::size_t ZeroBits = 0;
+    T Shift = std::numeric_limits<T>::digits >> 1;
+    T Mask = std::numeric_limits<T>::max() >> Shift;
+    while (Shift) {
+      if ((Val & Mask) == 0) {
+        Val >>= Shift;
+        ZeroBits |= Shift;
+      }
+      Shift >>= 1;
+      Mask >>= Shift;
+    }
+    return ZeroBits;
+  }
+};
+
+#if (defined(__GNUC__) && __GNUC__ >= 4) || defined(_MSC_VER)
+template <typename T>
+struct TrailingZerosCounter<T, 4> {
+  static std::size_t count(T Val, ZeroBehavior ZB) {
+    if (ZB != ZB_Undefined && Val == 0)
+      return 32;
+
+#if __has_builtin(__builtin_ctz) || LLVM_GNUC_PREREQ(4, 0, 0)
+    return __builtin_ctz(Val);
+#elif defined(_MSC_VER)
+    unsigned long Index;
+    _BitScanForward(&Index, Val);
+    return Index;
+#endif
+  }
+};
+
+#if !defined(_MSC_VER) || defined(_M_X64)
+template <typename T>
+struct TrailingZerosCounter<T, 8> {
+  static std::size_t count(T Val, ZeroBehavior ZB) {
+    if (ZB != ZB_Undefined && Val == 0)
+      return 64;
+
+#if __has_builtin(__builtin_ctzll) || LLVM_GNUC_PREREQ(4, 0, 0)
+    return __builtin_ctzll(Val);
+#elif defined(_MSC_VER)
+    unsigned long Index;
+    _BitScanForward64(&Index, Val);
+    return Index;
+#endif
+  }
+};
+#endif
+#endif
+} // namespace detail
+
+/// Count number of 0's from the least significant bit to the most
+///   stopping at the first 1.
+///
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of 0. Only ZB_Width and ZB_Undefined are
+///   valid arguments.
+template <typename T>
+std::size_t countTrailingZeros(T Val, ZeroBehavior ZB = ZB_Width) {
+  static_assert(
+      std::numeric_limits<T>::is_integer && !std::numeric_limits<T>::is_signed,
+      "Only unsigned integral types are allowed.");
+  return llvm::detail::TrailingZerosCounter<T, sizeof(T)>::count(Val, ZB);
+}
+
+namespace detail {
+template <typename T, std::size_t SizeOfT>
+struct LeadingZerosCounter {
+  static std::size_t count(T Val, ZeroBehavior) {
+    if (!Val)
+      return std::numeric_limits<T>::digits;
+
+    // Bisection method.
+    std::size_t ZeroBits = 0;
+    for (T Shift = std::numeric_limits<T>::digits >> 1; Shift; Shift >>= 1) {
+      T Tmp = Val >> Shift;
+      if (Tmp)
+        Val = Tmp;
+      else
+        ZeroBits |= Shift;
+    }
+    return ZeroBits;
+  }
+};
+
+#if (defined(__GNUC__) && __GNUC__ >= 4) || defined(_MSC_VER)
+template <typename T>
+struct LeadingZerosCounter<T, 4> {
+  static std::size_t count(T Val, ZeroBehavior ZB) {
+    if (ZB != ZB_Undefined && Val == 0)
+      return 32;
+
+#if __has_builtin(__builtin_clz) || LLVM_GNUC_PREREQ(4, 0, 0)
+    return __builtin_clz(Val);
+#elif defined(_MSC_VER)
+    unsigned long Index;
+    _BitScanReverse(&Index, Val);
+    return Index ^ 31;
+#endif
+  }
+};
+
+#if !defined(_MSC_VER) || defined(_M_X64)
+template <typename T>
+struct LeadingZerosCounter<T, 8> {
+  static std::size_t count(T Val, ZeroBehavior ZB) {
+    if (ZB != ZB_Undefined && Val == 0)
+      return 64;
+
+#if __has_builtin(__builtin_clzll) || LLVM_GNUC_PREREQ(4, 0, 0)
+    return __builtin_clzll(Val);
+#elif defined(_MSC_VER)
+    unsigned long Index;
+    _BitScanReverse64(&Index, Val);
+    return Index ^ 63;
+#endif
+  }
+};
+#endif
+#endif
+} // namespace detail
+
+/// Count number of 0's from the most significant bit to the least
+///   stopping at the first 1.
+///
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of 0. Only ZB_Width and ZB_Undefined are
+///   valid arguments.
+template <typename T>
+std::size_t countLeadingZeros(T Val, ZeroBehavior ZB = ZB_Width) {
+  static_assert(
+      std::numeric_limits<T>::is_integer && !std::numeric_limits<T>::is_signed,
+      "Only unsigned integral types are allowed.");
+  return llvm::detail::LeadingZerosCounter<T, sizeof(T)>::count(Val, ZB);
+}
+
+/// Get the index of the first set bit starting from the least
+///   significant bit.
+///
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of 0. Only ZB_Max and ZB_Undefined are
+///   valid arguments.
+template <typename T>
+T findFirstSet(T Val, ZeroBehavior ZB = ZB_Max) {
+  if (ZB == ZB_Max && Val == 0)
+    return std::numeric_limits<T>::max();
+
+  return countTrailingZeros(Val, ZB_Undefined);
+}
+
+/// Create a bitmask with the N right-most bits set to 1, and all other
+/// bits set to 0.  Only unsigned types are allowed.
+template <typename T>
+T maskTrailingOnes(unsigned N) {
+  static_assert(std::is_unsigned_v<T>, "Invalid type!");
+  const unsigned Bits = CHAR_BIT * sizeof(T);
+  assert(N <= Bits && "Invalid bit index");
+  return N == 0 ? 0 : (T(-1) >> (Bits - N));
+}
+
+/// Create a bitmask with the N left-most bits set to 1, and all other
+/// bits set to 0.  Only unsigned types are allowed.
+template <typename T>
+T maskLeadingOnes(unsigned N) {
+  return ~maskTrailingOnes<T>(CHAR_BIT * sizeof(T) - N);
+}
+
+/// Create a bitmask with the N right-most bits set to 0, and all other
+/// bits set to 1.  Only unsigned types are allowed.
+template <typename T>
+T maskTrailingZeros(unsigned N) {
+  return maskLeadingOnes<T>(CHAR_BIT * sizeof(T) - N);
+}
+
+/// Create a bitmask with the N left-most bits set to 0, and all other
+/// bits set to 1.  Only unsigned types are allowed.
+template <typename T>
+T maskLeadingZeros(unsigned N) {
+  return maskTrailingOnes<T>(CHAR_BIT * sizeof(T) - N);
+}
+
+/// Get the index of the last set bit starting from the least
+///   significant bit.
+///
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of 0. Only ZB_Max and ZB_Undefined are
+///   valid arguments.
+template <typename T>
+T findLastSet(T Val, ZeroBehavior ZB = ZB_Max) {
+  if (ZB == ZB_Max && Val == 0)
+    return std::numeric_limits<T>::max();
+
+  // Use ^ instead of - because both gcc and llvm can remove the associated ^
+  // in the __builtin_clz intrinsic on x86.
+  return countLeadingZeros(Val, ZB_Undefined) ^
+      (std::numeric_limits<T>::digits - 1);
+}
+
+/// Macro compressed bit reversal table for 256 bits.
+///
+/// http://graphics.stanford.edu/~seander/bithacks.html#BitReverseTable
+/// NOLINTNEXTLINE(*c-arrays*)
+static constexpr unsigned char BitReverseTable256[256] = {
+#define R2(n) n, n + 2 * 64, n + 1 * 64, n + 3 * 64
+#define R4(n) R2(n), R2(n + 2 * 16), R2(n + 1 * 16), R2(n + 3 * 16)
+#define R6(n) R4(n), R4(n + 2 * 4), R4(n + 1 * 4), R4(n + 3 * 4)
+    R6(0),
+    R6(2),
+    R6(1),
+    R6(3)
+#undef R2
+#undef R4
+#undef R6
+};
+
+/// Reverse the bits in \p Val.
+template <typename T>
+T reverseBits(T Val) {
+  // NOLINTNEXTLINE(*c-arrays*)
+  unsigned char in[sizeof(Val)];
+  // NOLINTNEXTLINE(*c-arrays*)
+  unsigned char out[sizeof(Val)];
+  std::memcpy(in, &Val, sizeof(Val));
+  for (unsigned i = 0; i < sizeof(Val); ++i)
+    out[(sizeof(Val) - i) - 1] = BitReverseTable256[in[i]];
+  std::memcpy(&Val, out, sizeof(Val));
+  return Val;
+}
+
+// NOTE: The following support functions use the _32/_64 extensions instead of
+// type overloading so that signed and unsigned integers can be used without
+// ambiguity.
+
+/// Return the high 32 bits of a 64 bit value.
+constexpr inline uint32_t Hi_32(uint64_t Value) {
+  return static_cast<uint32_t>(Value >> 32);
+}
+
+/// Return the low 32 bits of a 64 bit value.
+constexpr inline uint32_t Lo_32(uint64_t Value) {
+  return static_cast<uint32_t>(Value);
+}
+
+/// Make a 64-bit integer from a high / low pair of 32-bit integers.
+constexpr inline uint64_t Make_64(uint32_t High, uint32_t Low) {
+  return ((uint64_t)High << 32) | (uint64_t)Low;
+}
+
+/// Checks if an integer fits into the given bit width.
+template <unsigned N>
+constexpr inline bool isInt(int64_t x) {
+  return N >= 64 ||
+      (-(INT64_C(1) << (N - 1)) <= x && x < (INT64_C(1) << (N - 1)));
+}
+// Template specializations to get better code for common cases.
+template <>
+constexpr inline bool isInt<8>(int64_t x) {
+  return static_cast<int8_t>(x) == x;
+}
+template <>
+constexpr inline bool isInt<16>(int64_t x) {
+  return static_cast<int16_t>(x) == x;
+}
+template <>
+constexpr inline bool isInt<32>(int64_t x) {
+  return static_cast<int32_t>(x) == x;
+}
+
+/// Checks if a signed integer is an N bit number shifted left by S.
+template <unsigned N, unsigned S>
+constexpr inline bool isShiftedInt(int64_t x) {
+  static_assert(
+      N > 0, "isShiftedInt<0> doesn't make sense (refers to a 0-bit number.");
+  static_assert(N + S <= 64, "isShiftedInt<N, S> with N + S > 64 is too wide.");
+  return isInt<N + S>(x) && (x % (UINT64_C(1) << S) == 0);
+}
+
+/// Checks if an unsigned integer fits into the given bit width.
+///
+/// This is written as two functions rather than as simply
+///
+///   return N >= 64 || X < (UINT64_C(1) << N);
+///
+/// to keep MSVC from (incorrectly) warning on isUInt<64> that we're shifting
+/// left too many places.
+template <unsigned N>
+constexpr inline std::enable_if_t<(N < 64), bool> isUInt(uint64_t X) {
+  static_assert(N > 0, "isUInt<0> doesn't make sense");
+  return X < (UINT64_C(1) << (N));
+}
+template <unsigned N>
+constexpr inline std::enable_if_t<N >= 64, bool> isUInt(uint64_t /*X*/) {
+  return true;
+}
+
+// Template specializations to get better code for common cases.
+template <>
+constexpr inline bool isUInt<8>(uint64_t x) {
+  return static_cast<uint8_t>(x) == x;
+}
+template <>
+constexpr inline bool isUInt<16>(uint64_t x) {
+  return static_cast<uint16_t>(x) == x;
+}
+template <>
+constexpr inline bool isUInt<32>(uint64_t x) {
+  return static_cast<uint32_t>(x) == x;
+}
+
+/// Checks if a unsigned integer is an N bit number shifted left by S.
+template <unsigned N, unsigned S>
+constexpr inline bool isShiftedUInt(uint64_t x) {
+  static_assert(
+      N > 0, "isShiftedUInt<0> doesn't make sense (refers to a 0-bit number)");
+  static_assert(
+      N + S <= 64, "isShiftedUInt<N, S> with N + S > 64 is too wide.");
+  // Per the two static_asserts above, S must be strictly less than 64.  So
+  // 1 << S is not undefined behavior.
+  return isUInt<N + S>(x) && (x % (UINT64_C(1) << S) == 0);
+}
+
+/// Gets the maximum value for a N-bit unsigned integer.
+inline uint64_t maxUIntN(uint64_t N) {
+  assert(N > 0 && N <= 64 && "integer width out of range");
+
+  // uint64_t(1) << 64 is undefined behavior, so we can't do
+  //   (uint64_t(1) << N) - 1
+  // without checking first that N != 64.  But this works and doesn't have a
+  // branch.
+  return UINT64_MAX >> (64 - N);
+}
+
+// Ignore the false warning "Arithmetic overflow" for MSVC
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4146)
+#endif
+
+/// Gets the minimum value for a N-bit signed integer.
+inline int64_t minIntN(int64_t N) {
+  assert(N > 0 && N <= 64 && "integer width out of range");
+  // NOLINTNEXTLINE(*-narrowing-conversions)
+  return -(UINT64_C(1) << (N - 1));
+}
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+/// Gets the maximum value for a N-bit signed integer.
+inline int64_t maxIntN(int64_t N) {
+  assert(N > 0 && N <= 64 && "integer width out of range");
+
+  // This relies on two's complement wraparound when N == 64, so we convert to
+  // int64_t only at the very end to avoid UB.
+  // NOLINTNEXTLINE(*-narrowing-conversions)
+  return (UINT64_C(1) << (N - 1)) - 1;
+}
+
+/// Checks if an unsigned integer fits into the given (dynamic) bit width.
+inline bool isUIntN(unsigned N, uint64_t x) {
+  return N >= 64 || x <= maxUIntN(N);
+}
+
+/// Checks if an signed integer fits into the given (dynamic) bit width.
+inline bool isIntN(unsigned N, int64_t x) {
+  return N >= 64 || (minIntN(N) <= x && x <= maxIntN(N));
+}
+
+/// Return true if the argument is a non-empty sequence of ones starting at the
+/// least significant bit with the remainder zero (32 bit version).
+/// Ex. isMask_32(0x0000FFFFU) == true.
+constexpr inline bool isMask_32(uint32_t Value) {
+  return Value && ((Value + 1) & Value) == 0;
+}
+
+/// Return true if the argument is a non-empty sequence of ones starting at the
+/// least significant bit with the remainder zero (64 bit version).
+constexpr inline bool isMask_64(uint64_t Value) {
+  return Value && ((Value + 1) & Value) == 0;
+}
+
+/// Return true if the argument contains a non-empty sequence of ones with the
+/// remainder zero (32 bit version.) Ex. isShiftedMask_32(0x0000FF00U) == true.
+constexpr inline bool isShiftedMask_32(uint32_t Value) {
+  return Value && isMask_32((Value - 1) | Value);
+}
+
+/// Return true if the argument contains a non-empty sequence of ones with the
+/// remainder zero (64 bit version.)
+constexpr inline bool isShiftedMask_64(uint64_t Value) {
+  return Value && isMask_64((Value - 1) | Value);
+}
+
+/// Return true if the argument is a power of two > 0.
+/// Ex. isPowerOf2_32(0x00100000U) == true (32 bit edition.)
+constexpr inline bool isPowerOf2_32(uint32_t Value) {
+  return Value && !(Value & (Value - 1));
+}
+
+/// Return true if the argument is a power of two > 0 (64 bit edition.)
+constexpr inline bool isPowerOf2_64(uint64_t Value) {
+  return Value && !(Value & (Value - 1));
+}
+
+/// Count the number of ones from the most significant bit to the first
+/// zero bit.
+///
+/// Ex. countLeadingOnes(0xFF0FFF00) == 8.
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of all ones. Only ZB_Width and
+/// ZB_Undefined are valid arguments.
+template <typename T>
+std::size_t countLeadingOnes(T Value, ZeroBehavior ZB = ZB_Width) {
+  static_assert(
+      std::numeric_limits<T>::is_integer && !std::numeric_limits<T>::is_signed,
+      "Only unsigned integral types are allowed.");
+  return countLeadingZeros<T>(~Value, ZB);
+}
+
+/// Count the number of ones from the least significant bit to the first
+/// zero bit.
+///
+/// Ex. countTrailingOnes(0x00FF00FF) == 8.
+/// Only unsigned integral types are allowed.
+///
+/// \param ZB the behavior on an input of all ones. Only ZB_Width and
+/// ZB_Undefined are valid arguments.
+template <typename T>
+std::size_t countTrailingOnes(T Value, ZeroBehavior ZB = ZB_Width) {
+  static_assert(
+      std::numeric_limits<T>::is_integer && !std::numeric_limits<T>::is_signed,
+      "Only unsigned integral types are allowed.");
+  return countTrailingZeros<T>(~Value, ZB);
+}
+
+namespace detail {
+template <typename T, std::size_t SizeOfT>
+struct PopulationCounter {
+  static unsigned count(T Value) {
+    // Generic version, forward to 32 bits.
+    static_assert(SizeOfT <= 4, "Not implemented!");
+#if defined(__GNUC__) && __GNUC__ >= 4
+    return __builtin_popcount(Value);
+#else
+    uint32_t v = Value;
+    v = v - ((v >> 1) & 0x55555555);
+    v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
+    return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24;
+#endif
+  }
+};
+
+template <typename T>
+struct PopulationCounter<T, 8> {
+  static unsigned count(T Value) {
+#if defined(__GNUC__) && __GNUC__ >= 4
+    return __builtin_popcountll(Value);
+#else
+    uint64_t v = Value;
+    v = v - ((v >> 1) & 0x5555555555555555ULL);
+    v = (v & 0x3333333333333333ULL) + ((v >> 2) & 0x3333333333333333ULL);
+    v = (v + (v >> 4)) & 0x0F0F0F0F0F0F0F0FULL;
+    return unsigned((uint64_t)(v * 0x0101010101010101ULL) >> 56);
+#endif
+  }
+};
+} // namespace detail
+
+/// Count the number of set bits in a value.
+/// Ex. countPopulation(0xF000F000) = 8
+/// Returns 0 if the word is zero.
+template <typename T>
+inline unsigned countPopulation(T Value) {
+  static_assert(
+      std::numeric_limits<T>::is_integer && !std::numeric_limits<T>::is_signed,
+      "Only unsigned integral types are allowed.");
+  return detail::PopulationCounter<T, sizeof(T)>::count(Value);
+}
+
+/// Return the log base 2 of the specified value.
+inline double Log2(double Value) {
+#if defined(__ANDROID_API__) && __ANDROID_API__ < 18
+  return __builtin_log(Value) / __builtin_log(2.0);
+#else
+  return log2(Value);
+#endif
+}
+
+/// Return the floor log base 2 of the specified value, -1 if the value is zero.
+/// (32 bit edition.)
+/// Ex. Log2_32(32) == 5, Log2_32(1) == 0, Log2_32(0) == -1, Log2_32(6) == 2
+inline unsigned Log2_32(uint32_t Value) {
+  return static_cast<unsigned>(31 - countLeadingZeros(Value));
+}
+
+/// Return the floor log base 2 of the specified value, -1 if the value is zero.
+/// (64 bit edition.)
+inline unsigned Log2_64(uint64_t Value) {
+  return static_cast<unsigned>(63 - countLeadingZeros(Value));
+}
+
+/// Return the ceil log base 2 of the specified value, 32 if the value is zero.
+/// (32 bit edition).
+/// Ex. Log2_32_Ceil(32) == 5, Log2_32_Ceil(1) == 0, Log2_32_Ceil(6) == 3
+inline unsigned Log2_32_Ceil(uint32_t Value) {
+  return static_cast<unsigned>(32 - countLeadingZeros(Value - 1));
+}
+
+/// Return the ceil log base 2 of the specified value, 64 if the value is zero.
+/// (64 bit edition.)
+inline unsigned Log2_64_Ceil(uint64_t Value) {
+  return static_cast<unsigned>(64 - countLeadingZeros(Value - 1));
+}
+
+/// Return the greatest common divisor of the values using Euclid's algorithm.
+inline uint64_t GreatestCommonDivisor64(uint64_t A, uint64_t B) {
+  while (B) {
+    uint64_t T = B;
+    B = A % B;
+    A = T;
+  }
+  return A;
+}
+
+/// This function takes a 64-bit integer and returns the bit equivalent double.
+inline double BitsToDouble(uint64_t Bits) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  double D;
+  static_assert(sizeof(uint64_t) == sizeof(double), "Unexpected type sizes");
+  memcpy(&D, &Bits, sizeof(Bits));
+  return D;
+}
+
+/// This function takes a 32-bit integer and returns the bit equivalent float.
+inline float BitsToFloat(uint32_t Bits) {
+  // TODO: Use std::bit_cast once C++20 becomes available.
+  return c10::bit_cast<float>(Bits);
+}
+
+/// This function takes a double and returns the bit equivalent 64-bit integer.
+/// Note that copying doubles around changes the bits of NaNs on some hosts,
+/// notably x86, so this routine cannot be used if these bits are needed.
+inline uint64_t DoubleToBits(double Double) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  uint64_t Bits;
+  static_assert(sizeof(uint64_t) == sizeof(double), "Unexpected type sizes");
+  memcpy(&Bits, &Double, sizeof(Double));
+  return Bits;
+}
+
+/// This function takes a float and returns the bit equivalent 32-bit integer.
+/// Note that copying floats around changes the bits of NaNs on some hosts,
+/// notably x86, so this routine cannot be used if these bits are needed.
+inline uint32_t FloatToBits(float Float) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  uint32_t Bits;
+  static_assert(sizeof(uint32_t) == sizeof(float), "Unexpected type sizes");
+  memcpy(&Bits, &Float, sizeof(Float));
+  return Bits;
+}
+
+/// A and B are either alignments or offsets. Return the minimum alignment that
+/// may be assumed after adding the two together.
+constexpr inline uint64_t MinAlign(uint64_t A, uint64_t B) {
+  // The largest power of 2 that divides both A and B.
+  //
+  // Replace "-Value" by "1+~Value" in the following commented code to avoid
+  // MSVC warning C4146
+  //    return (A | B) & -(A | B);
+  return (A | B) & (1 + ~(A | B));
+}
+
+/// Aligns \c Addr to \c Alignment bytes, rounding up.
+///
+/// Alignment should be a power of two.  This method rounds up, so
+/// alignAddr(7, 4) == 8 and alignAddr(8, 4) == 8.
+inline uintptr_t alignAddr(const void* Addr, size_t Alignment) {
+  assert(
+      Alignment && isPowerOf2_64((uint64_t)Alignment) &&
+      "Alignment is not a power of two!");
+
+  assert((uintptr_t)Addr + Alignment - 1 >= (uintptr_t)Addr);
+
+  return (((uintptr_t)Addr + Alignment - 1) & ~(uintptr_t)(Alignment - 1));
+}
+
+/// Returns the necessary adjustment for aligning \c Ptr to \c Alignment
+/// bytes, rounding up.
+inline size_t alignmentAdjustment(const void* Ptr, size_t Alignment) {
+  return alignAddr(Ptr, Alignment) - (uintptr_t)Ptr;
+}
+
+/// Returns the next power of two (in 64-bits) that is strictly greater than A.
+/// Returns zero on overflow.
+inline uint64_t NextPowerOf2(uint64_t A) {
+  A |= (A >> 1);
+  A |= (A >> 2);
+  A |= (A >> 4);
+  A |= (A >> 8);
+  A |= (A >> 16);
+  A |= (A >> 32);
+  return A + 1;
+}
+
+/// Returns the power of two which is less than or equal to the given value.
+/// Essentially, it is a floor operation across the domain of powers of two.
+inline uint64_t PowerOf2Floor(uint64_t A) {
+  if (!A)
+    return 0;
+  return 1ull << (63 - countLeadingZeros(A, ZB_Undefined));
+}
+
+/// Returns the power of two which is greater than or equal to the given value.
+/// Essentially, it is a ceil operation across the domain of powers of two.
+inline uint64_t PowerOf2Ceil(uint64_t A) {
+  if (!A)
+    return 0;
+  return NextPowerOf2(A - 1);
+}
+
+/// Returns the next integer (mod 2**64) that is greater than or equal to
+/// \p Value and is a multiple of \p Align. \p Align must be non-zero.
+///
+/// If non-zero \p Skew is specified, the return value will be a minimal
+/// integer that is greater than or equal to \p Value and equal to
+/// \p Align * N + \p Skew for some integer N. If \p Skew is larger than
+/// \p Align, its value is adjusted to '\p Skew mod \p Align'.
+///
+/// Examples:
+/// \code
+///   alignTo(5, 8) = 8
+///   alignTo(17, 8) = 24
+///   alignTo(~0LL, 8) = 0
+///   alignTo(321, 255) = 510
+///
+///   alignTo(5, 8, 7) = 7
+///   alignTo(17, 8, 1) = 17
+///   alignTo(~0LL, 8, 3) = 3
+///   alignTo(321, 255, 42) = 552
+/// \endcode
+inline uint64_t alignTo(uint64_t Value, uint64_t Align, uint64_t Skew = 0) {
+  assert(Align != 0u && "Align can't be 0.");
+  Skew %= Align;
+  return (Value + Align - 1 - Skew) / Align * Align + Skew;
+}
+
+/// Returns the next integer (mod 2**64) that is greater than or equal to
+/// \p Value and is a multiple of \c Align. \c Align must be non-zero.
+template <uint64_t Align>
+constexpr inline uint64_t alignTo(uint64_t Value) {
+  static_assert(Align != 0u, "Align must be non-zero");
+  return (Value + Align - 1) / Align * Align;
+}
+
+/// Returns the integer ceil(Numerator / Denominator).
+inline uint64_t divideCeil(uint64_t Numerator, uint64_t Denominator) {
+  return alignTo(Numerator, Denominator) / Denominator;
+}
+
+/// \c alignTo for contexts where a constant expression is required.
+/// \sa alignTo
+///
+/// \todo FIXME: remove when \c constexpr becomes really \c constexpr
+template <uint64_t Align>
+struct AlignTo {
+  static_assert(Align != 0u, "Align must be non-zero");
+  template <uint64_t Value>
+  struct from_value {
+    static const uint64_t value = (Value + Align - 1) / Align * Align;
+  };
+};
+
+/// Returns the largest uint64_t less than or equal to \p Value and is
+/// \p Skew mod \p Align. \p Align must be non-zero
+inline uint64_t alignDown(uint64_t Value, uint64_t Align, uint64_t Skew = 0) {
+  assert(Align != 0u && "Align can't be 0.");
+  Skew %= Align;
+  return (Value - Skew) / Align * Align + Skew;
+}
+
+/// Returns the offset to the next integer (mod 2**64) that is greater than
+/// or equal to \p Value and is a multiple of \p Align. \p Align must be
+/// non-zero.
+inline uint64_t OffsetToAlignment(uint64_t Value, uint64_t Align) {
+  return alignTo(Value, Align) - Value;
+}
+
+/// Sign-extend the number in the bottom B bits of X to a 32-bit integer.
+/// Requires 0 < B <= 32.
+template <unsigned B>
+constexpr inline int32_t SignExtend32(uint32_t X) {
+  static_assert(B > 0, "Bit width can't be 0.");
+  static_assert(B <= 32, "Bit width out of range.");
+  return int32_t(X << (32 - B)) >> (32 - B);
+}
+
+/// Sign-extend the number in the bottom B bits of X to a 32-bit integer.
+/// Requires 0 < B < 32.
+inline int32_t SignExtend32(uint32_t X, unsigned B) {
+  assert(B > 0 && "Bit width can't be 0.");
+  assert(B <= 32 && "Bit width out of range.");
+  return int32_t(X << (32 - B)) >> (32 - B);
+}
+
+/// Sign-extend the number in the bottom B bits of X to a 64-bit integer.
+/// Requires 0 < B < 64.
+template <unsigned B>
+constexpr inline int64_t SignExtend64(uint64_t x) {
+  static_assert(B > 0, "Bit width can't be 0.");
+  static_assert(B <= 64, "Bit width out of range.");
+  return int64_t(x << (64 - B)) >> (64 - B);
+}
+
+/// Sign-extend the number in the bottom B bits of X to a 64-bit integer.
+/// Requires 0 < B < 64.
+inline int64_t SignExtend64(uint64_t X, unsigned B) {
+  assert(B > 0 && "Bit width can't be 0.");
+  assert(B <= 64 && "Bit width out of range.");
+  return int64_t(X << (64 - B)) >> (64 - B);
+}
+
+/// Subtract two unsigned integers, X and Y, of type T and return the absolute
+/// value of the result.
+template <typename T>
+std::enable_if_t<std::is_unsigned_v<T>, T> AbsoluteDifference(T X, T Y) {
+  return std::max(X, Y) - std::min(X, Y);
+}
+
+/// Add two unsigned integers, X and Y, of type T.  Clamp the result to the
+/// maximum representable value of T on overflow.  ResultOverflowed indicates if
+/// the result is larger than the maximum representable value of type T.
+template <typename T>
+std::enable_if_t<std::is_unsigned_v<T>, T> SaturatingAdd(
+    T X,
+    T Y,
+    bool* ResultOverflowed = nullptr) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  bool Dummy;
+  bool& Overflowed = ResultOverflowed ? *ResultOverflowed : Dummy;
+  // Hacker's Delight, p. 29
+  T Z = X + Y;
+  Overflowed = (Z < X || Z < Y);
+  if (Overflowed)
+    return std::numeric_limits<T>::max();
+  else
+    return Z;
+}
+
+/// Multiply two unsigned integers, X and Y, of type T.  Clamp the result to the
+/// maximum representable value of T on overflow.  ResultOverflowed indicates if
+/// the result is larger than the maximum representable value of type T.
+template <typename T>
+std::enable_if_t<std::is_unsigned_v<T>, T> SaturatingMultiply(
+    T X,
+    T Y,
+    bool* ResultOverflowed = nullptr) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  bool Dummy;
+  bool& Overflowed = ResultOverflowed ? *ResultOverflowed : Dummy;
+
+  // Hacker's Delight, p. 30 has a different algorithm, but we don't use that
+  // because it fails for uint16_t (where multiplication can have undefined
+  // behavior due to promotion to int), and requires a division in addition
+  // to the multiplication.
+
+  Overflowed = false;
+
+  // Log2(Z) would be either Log2Z or Log2Z + 1.
+  // Special case: if X or Y is 0, Log2_64 gives -1, and Log2Z
+  // will necessarily be less than Log2Max as desired.
+  int Log2Z = Log2_64(X) + Log2_64(Y);
+  const T Max = std::numeric_limits<T>::max();
+  int Log2Max = Log2_64(Max);
+  if (Log2Z < Log2Max) {
+    return X * Y;
+  }
+  if (Log2Z > Log2Max) {
+    Overflowed = true;
+    return Max;
+  }
+
+  // We're going to use the top bit, and maybe overflow one
+  // bit past it. Multiply all but the bottom bit then add
+  // that on at the end.
+  T Z = (X >> 1) * Y;
+  if (Z & ~(Max >> 1)) {
+    Overflowed = true;
+    return Max;
+  }
+  Z <<= 1;
+  if (X & 1)
+    return SaturatingAdd(Z, Y, ResultOverflowed);
+
+  return Z;
+}
+
+/// Multiply two unsigned integers, X and Y, and add the unsigned integer, A to
+/// the product. Clamp the result to the maximum representable value of T on
+/// overflow. ResultOverflowed indicates if the result is larger than the
+/// maximum representable value of type T.
+template <typename T>
+std::enable_if_t<std::is_unsigned_v<T>, T> SaturatingMultiplyAdd(
+    T X,
+    T Y,
+    T A,
+    bool* ResultOverflowed = nullptr) {
+  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+  bool Dummy;
+  bool& Overflowed = ResultOverflowed ? *ResultOverflowed : Dummy;
+
+  T Product = SaturatingMultiply(X, Y, &Overflowed);
+  if (Overflowed)
+    return Product;
+
+  return SaturatingAdd(A, Product, &Overflowed);
+}
+
+/// Use this rather than HUGE_VALF; the latter causes warnings on MSVC.
+extern const float huge_valf;
+} // namespace c10::llvm
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/logging_is_google_glog.h b/lib/python3.11/site-packages/torch/include/c10/util/logging_is_google_glog.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5470d22cecd34cff4cb8e324d743fa354d285b8
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/logging_is_google_glog.h
@@ -0,0 +1,109 @@
+#ifndef C10_UTIL_LOGGING_IS_GOOGLE_GLOG_H_
+#define C10_UTIL_LOGGING_IS_GOOGLE_GLOG_H_
+
+#include <map>
+#include <set>
+#include <vector>
+
+#include <iomanip> // because some of the caffe2 code uses e.g. std::setw
+// Using google glog. For glog 0.3.2 versions, stl_logging.h needs to be before
+// logging.h to actually use stl_logging. Because template magic.
+// In addition, we do not do stl logging in .cu files because nvcc does not like
+// it. Some mobile platforms do not like stl_logging, so we add an
+// overload in that case as well.
+
+#ifdef __CUDACC__
+#include <cuda.h>
+#endif
+
+#if !defined(__CUDACC__) && !defined(C10_USE_MINIMAL_GLOG)
+#include <glog/stl_logging.h>
+
+// Old versions of glog don't declare this using declaration, so help
+// them out.  Fortunately, C++ won't complain if you declare the same
+// using declaration multiple times.
+namespace std {
+using ::operator<<;
+}
+
+#else // !defined(__CUDACC__) && !defined(C10_USE_MINIMAL_GLOG)
+
+// In the cudacc compiler scenario, we will simply ignore the container
+// printout feature. Basically we need to register a fake overload for
+// vector/string - here, we just ignore the entries in the logs.
+
+namespace std {
+#define INSTANTIATE_FOR_CONTAINER(container)                      \
+  template <class... Types>                                       \
+  ostream& operator<<(ostream& out, const container<Types...>&) { \
+    return out;                                                   \
+  }
+
+INSTANTIATE_FOR_CONTAINER(vector)
+INSTANTIATE_FOR_CONTAINER(map)
+INSTANTIATE_FOR_CONTAINER(set)
+#undef INSTANTIATE_FOR_CONTAINER
+} // namespace std
+
+#endif
+
+#include <glog/logging.h>
+
+// Additional macros on top of glog
+#define TORCH_CHECK_EQ(val1, val2) CHECK_EQ(val1, val2)
+#define TORCH_CHECK_NE(val1, val2) CHECK_NE(val1, val2)
+#define TORCH_CHECK_LE(val1, val2) CHECK_LE(val1, val2)
+#define TORCH_CHECK_LT(val1, val2) CHECK_LT(val1, val2)
+#define TORCH_CHECK_GE(val1, val2) CHECK_GE(val1, val2)
+#define TORCH_CHECK_GT(val1, val2) CHECK_GT(val1, val2)
+
+#ifndef NDEBUG
+#define TORCH_DCHECK_EQ(val1, val2) DCHECK_EQ(val1, val2)
+#define TORCH_DCHECK_NE(val1, val2) DCHECK_NE(val1, val2)
+#define TORCH_DCHECK_LE(val1, val2) DCHECK_LE(val1, val2)
+#define TORCH_DCHECK_LT(val1, val2) DCHECK_LT(val1, val2)
+#define TORCH_DCHECK_GE(val1, val2) DCHECK_GE(val1, val2)
+#define TORCH_DCHECK_GT(val1, val2) DCHECK_GT(val1, val2)
+#else // !NDEBUG
+// These versions generate no code in optimized mode.
+#define TORCH_DCHECK_EQ(val1, val2) \
+  while (false)                     \
+  DCHECK_EQ(val1, val2)
+#define TORCH_DCHECK_NE(val1, val2) \
+  while (false)                     \
+  DCHECK_NE(val1, val2)
+#define TORCH_DCHECK_LE(val1, val2) \
+  while (false)                     \
+  DCHECK_LE(val1, val2)
+#define TORCH_DCHECK_LT(val1, val2) \
+  while (false)                     \
+  DCHECK_LT(val1, val2)
+#define TORCH_DCHECK_GE(val1, val2) \
+  while (false)                     \
+  DCHECK_GE(val1, val2)
+#define TORCH_DCHECK_GT(val1, val2) \
+  while (false)                     \
+  DCHECK_GT(val1, val2)
+#endif // NDEBUG
+
+// Check that a pointer is not null.
+#define TORCH_CHECK_NOTNULL(val) CHECK_NOTNULL(val)
+
+#ifndef NDEBUG
+// Debug only version of TORCH_CHECK_NOTNULL
+#define TORCH_DCHECK_NOTNULL(val) DCHECK_NOTNULL(val)
+#else // !NDEBUG
+// Optimized version - generates no code.
+#define TORCH_DCHECK_NOTNULL(val) \
+  while (false)                   \
+  DCHECK_NOTNULL(val)
+#endif // NDEBUG
+
+// Log with source location information override (to be used in generic
+// warning/error handlers implemented as functions, not macros)
+//
+// Note, we don't respect GOOGLE_STRIP_LOG here for simplicity
+#define LOG_AT_FILE_LINE(n, file, line) \
+  ::google::LogMessage(file, line, ::google::GLOG_##n).stream()
+
+#endif // C10_UTIL_LOGGING_IS_GOOGLE_GLOG_H_
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/logging_is_not_google_glog.h b/lib/python3.11/site-packages/torch/include/c10/util/logging_is_not_google_glog.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e9ff45948bbbaa2e807b4111cb8e7fe5f4e6484
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/logging_is_not_google_glog.h
@@ -0,0 +1,258 @@
+#ifndef C10_UTIL_LOGGING_IS_NOT_GOOGLE_GLOG_H_
+#define C10_UTIL_LOGGING_IS_NOT_GOOGLE_GLOG_H_
+
+#include <chrono>
+#include <climits>
+#include <ctime>
+#include <iomanip>
+#include <map>
+#include <ostream>
+#include <set>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include <c10/util/Flags.h>
+
+const char CAFFE2_SEVERITY_PREFIX[] = "FEWIV";
+
+namespace c10 {
+
+// Log severity level constants.
+const int GLOG_FATAL = 3;
+const int GLOG_ERROR = 2;
+const int GLOG_WARNING = 1;
+const int GLOG_INFO = 0;
+
+class C10_API MessageLogger {
+ public:
+  MessageLogger(const char* file, int line, int severity);
+  ~MessageLogger();
+  // Return the stream associated with the logger object.
+  std::stringstream& stream() {
+    return stream_;
+  }
+
+ private:
+  // When there is a fatal log, we simply abort.
+  void DealWithFatal() {
+    abort();
+  }
+
+  const char* tag_;
+  std::stringstream stream_;
+  int severity_;
+};
+
+// This class is used to explicitly ignore values in the conditional
+// logging macros.  This avoids compiler warnings like "value computed
+// is not used" and "statement has no effect".
+class C10_API LoggerVoidify {
+ public:
+  LoggerVoidify() = default;
+  // This has to be an operator with a precedence lower than << but
+  // higher than ?:
+  void operator&(const std::ostream& s [[maybe_unused]]) {}
+};
+
+// Log a message and terminate.
+template <class T>
+void LogMessageFatal(const char* file, int line, const T& message) {
+  MessageLogger(file, line, GLOG_FATAL).stream() << message;
+}
+
+// Helpers for TORCH_CHECK_NOTNULL(). Two are necessary to support both raw
+// pointers and smart pointers.
+template <typename T>
+T& CheckNotNullCommon(const char* file, int line, const char* names, T& t) {
+  if (t == nullptr) {
+    LogMessageFatal(file, line, std::string(names));
+  }
+  return t;
+}
+
+template <typename T>
+T* CheckNotNull(const char* file, int line, const char* names, T* t) {
+  return CheckNotNullCommon(file, line, names, t);
+}
+
+template <typename T>
+T& CheckNotNull(const char* file, int line, const char* names, T& t) {
+  return CheckNotNullCommon(file, line, names, t);
+}
+} // namespace c10
+
+// ---------------------- Logging Macro definitions --------------------------
+
+static_assert(
+    CAFFE2_LOG_THRESHOLD <= ::c10::GLOG_FATAL,
+    "CAFFE2_LOG_THRESHOLD should at most be GLOG_FATAL.");
+// If n is under the compile time caffe log threshold, The _CAFFE_LOG(n)
+// should not generate anything in optimized code.
+#define LOG(n)                                 \
+  if (::c10::GLOG_##n >= CAFFE2_LOG_THRESHOLD) \
+  ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_##n).stream()
+#define VLOG(n)                   \
+  if (-n >= CAFFE2_LOG_THRESHOLD) \
+  ::c10::MessageLogger(__FILE__, __LINE__, -n).stream()
+
+#define LOG_IF(n, condition)                                  \
+  if (::c10::GLOG_##n >= CAFFE2_LOG_THRESHOLD && (condition)) \
+  ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_##n).stream()
+#define VLOG_IF(n, condition)                    \
+  if (-n >= CAFFE2_LOG_THRESHOLD && (condition)) \
+  ::c10::MessageLogger(__FILE__, __LINE__, -n).stream()
+
+#define VLOG_IS_ON(verboselevel) (CAFFE2_LOG_THRESHOLD <= -(verboselevel))
+
+// Log with source location information override (to be used in generic
+// warning/error handlers implemented as functions, not macros)
+#define LOG_AT_FILE_LINE(n, file, line)        \
+  if (::c10::GLOG_##n >= CAFFE2_LOG_THRESHOLD) \
+  ::c10::MessageLogger(file, line, ::c10::GLOG_##n).stream()
+
+// Log only if condition is met.  Otherwise evaluates to void.
+#define FATAL_IF(condition)            \
+  condition ? (void)0                  \
+            : ::c10::LoggerVoidify() & \
+          ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_FATAL).stream()
+
+// Check for a given boolean condition.
+#define CHECK(condition) FATAL_IF(condition) << "Check failed: " #condition " "
+
+#ifndef NDEBUG
+// Debug only version of CHECK
+#define DCHECK(condition) FATAL_IF(condition) << "Check failed: " #condition " "
+#define DLOG(severity) LOG(severity)
+#else // NDEBUG
+// Optimized version - generates no code.
+#define DCHECK(condition) \
+  while (false)           \
+  CHECK(condition)
+
+#define DLOG(n)                   \
+  true ? (void)0                  \
+       : ::c10::LoggerVoidify() & \
+          ::c10::MessageLogger(__FILE__, __LINE__, ::c10::GLOG_##n).stream()
+#endif // NDEBUG
+
+#define TORCH_CHECK_OP(val1, val2, op)                                        \
+  FATAL_IF(((val1)op(val2))) << "Check failed: " #val1 " " #op " " #val2 " (" \
+                             << (val1) << " vs. " << (val2) << ") "
+
+// TORCH_CHECK_OP macro definitions
+#define TORCH_CHECK_EQ(val1, val2) TORCH_CHECK_OP(val1, val2, ==)
+#define TORCH_CHECK_NE(val1, val2) TORCH_CHECK_OP(val1, val2, !=)
+#define TORCH_CHECK_LE(val1, val2) TORCH_CHECK_OP(val1, val2, <=)
+#define TORCH_CHECK_LT(val1, val2) TORCH_CHECK_OP(val1, val2, <)
+#define TORCH_CHECK_GE(val1, val2) TORCH_CHECK_OP(val1, val2, >=)
+#define TORCH_CHECK_GT(val1, val2) TORCH_CHECK_OP(val1, val2, >)
+
+#ifndef NDEBUG
+// Debug only versions of TORCH_CHECK_OP macros.
+#define TORCH_DCHECK_EQ(val1, val2) TORCH_CHECK_OP(val1, val2, ==)
+#define TORCH_DCHECK_NE(val1, val2) TORCH_CHECK_OP(val1, val2, !=)
+#define TORCH_DCHECK_LE(val1, val2) TORCH_CHECK_OP(val1, val2, <=)
+#define TORCH_DCHECK_LT(val1, val2) TORCH_CHECK_OP(val1, val2, <)
+#define TORCH_DCHECK_GE(val1, val2) TORCH_CHECK_OP(val1, val2, >=)
+#define TORCH_DCHECK_GT(val1, val2) TORCH_CHECK_OP(val1, val2, >)
+#else // !NDEBUG
+// These versions generate no code in optimized mode.
+#define TORCH_DCHECK_EQ(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, ==)
+#define TORCH_DCHECK_NE(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, !=)
+#define TORCH_DCHECK_LE(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, <=)
+#define TORCH_DCHECK_LT(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, <)
+#define TORCH_DCHECK_GE(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, >=)
+#define TORCH_DCHECK_GT(val1, val2) \
+  while (false)                     \
+  TORCH_CHECK_OP(val1, val2, >)
+#endif // NDEBUG
+
+// Check that a pointer is not null.
+#define TORCH_CHECK_NOTNULL(val) \
+  ::c10::CheckNotNull(           \
+      __FILE__, __LINE__, "Check failed: '" #val "' Must be non NULL", (val))
+
+#ifndef NDEBUG
+// Debug only version of TORCH_CHECK_NOTNULL
+#define TORCH_DCHECK_NOTNULL(val) \
+  ::c10::CheckNotNull(            \
+      __FILE__, __LINE__, "Check failed: '" #val "' Must be non NULL", (val))
+#else // !NDEBUG
+// Optimized version - generates no code.
+#define TORCH_DCHECK_NOTNULL(val) \
+  while (false)                   \
+  TORCH_CHECK_NOTNULL(val)
+#endif // NDEBUG
+
+// ---------------------- Support for std objects --------------------------
+// These are adapted from glog to support a limited set of logging capability
+// for STL objects.
+
+namespace std {
+// Forward declare these two, and define them after all the container streams
+// operators so that we can recurse from pair -> container -> container -> pair
+// properly.
+template <class First, class Second>
+std::ostream& operator<<(std::ostream& out, const std::pair<First, Second>& p);
+} // namespace std
+
+namespace c10 {
+template <class Iter>
+void PrintSequence(std::ostream& ss, Iter begin, Iter end);
+} // namespace c10
+
+namespace std {
+#define INSTANTIATE_FOR_CONTAINER(container)               \
+  template <class... Types>                                \
+  std::ostream& operator<<(                                \
+      std::ostream& out, const container<Types...>& seq) { \
+    c10::PrintSequence(out, seq.begin(), seq.end());       \
+    return out;                                            \
+  }
+
+INSTANTIATE_FOR_CONTAINER(std::vector)
+INSTANTIATE_FOR_CONTAINER(std::map)
+INSTANTIATE_FOR_CONTAINER(std::set)
+#undef INSTANTIATE_FOR_CONTAINER
+
+template <class First, class Second>
+inline std::ostream& operator<<(
+    std::ostream& out,
+    const std::pair<First, Second>& p) {
+  out << '(' << p.first << ", " << p.second << ')';
+  return out;
+}
+
+inline std::ostream& operator<<(std::ostream& out, const std::nullptr_t&) {
+  out << "(null)";
+  return out;
+}
+} // namespace std
+
+namespace c10 {
+template <class Iter>
+inline void PrintSequence(std::ostream& out, Iter begin, Iter end) {
+  // Output at most 100 elements -- appropriate if used for logging.
+  for (int i = 0; begin != end && i < 100; ++i, ++begin) {
+    if (i > 0)
+      out << ' ';
+    out << *begin;
+  }
+  if (begin != end) {
+    out << " ...";
+  }
+}
+} // namespace c10
+
+#endif // C10_UTIL_LOGGING_IS_NOT_GOOGLE_GLOG_H_
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/numa.h b/lib/python3.11/site-packages/torch/include/c10/util/numa.h
new file mode 100644
index 0000000000000000000000000000000000000000..c1177095ef2af5d1c66c998bc300a7c39273ab41
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/numa.h
@@ -0,0 +1,41 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/Flags.h>
+#include <cstddef>
+
+C10_DECLARE_bool(caffe2_cpu_numa_enabled);
+
+namespace c10 {
+
+/**
+ * Check whether NUMA is enabled
+ */
+C10_API bool IsNUMAEnabled();
+
+/**
+ * Bind to a given NUMA node
+ */
+C10_API void NUMABind(int numa_node_id);
+
+/**
+ * Get the NUMA id for a given pointer `ptr`
+ */
+C10_API int GetNUMANode(const void* ptr);
+
+/**
+ * Get number of NUMA nodes
+ */
+C10_API int GetNumNUMANodes();
+
+/**
+ * Move the memory pointed to by `ptr` of a given size to another NUMA node
+ */
+C10_API void NUMAMove(void* ptr, size_t size, int numa_node_id);
+
+/**
+ * Get the current NUMA node id
+ */
+C10_API int GetCurrentNUMANode();
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/order_preserving_flat_hash_map.h b/lib/python3.11/site-packages/torch/include/c10/util/order_preserving_flat_hash_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..fd8196432c994c96a945bd0a7172dd07aaff8f8d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/order_preserving_flat_hash_map.h
@@ -0,0 +1,2217 @@
+// Taken from
+// https://github.com/skarupke/flat_hash_map/blob/2c4687431f978f02a3780e24b8b701d22aa32d9c/flat_hash_map.hpp
+// with fixes applied:
+// - https://github.com/skarupke/flat_hash_map/pull/25
+// - https://github.com/skarupke/flat_hash_map/pull/26
+// - replace size_t with uint64_t to fix it for 32bit
+// - add "GCC diagnostic" pragma to ignore -Wshadow
+// - make sherwood_v3_table::convertible_to_iterator public because GCC5 seems
+// to have issues with it otherwise
+// - fix compiler warnings in operator templated_iterator<const value_type>
+// - make use of 'if constexpr' and eliminate AssignIfTrue template
+
+//          Copyright Malte Skarupke 2017.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See http://www.boost.org/LICENSE_1_0.txt)
+
+// Modified to maintain insertion and deletion order through a doubly-linked
+// list
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <initializer_list>
+#include <iterator>
+#include <memory>
+#include <stdexcept>
+#include <type_traits>
+#include <utility>
+
+#ifdef _MSC_VER
+#define SKA_NOINLINE(...) __declspec(noinline) __VA_ARGS__
+#else
+#define SKA_NOINLINE(...) __VA_ARGS__ __attribute__((noinline))
+#endif
+
+namespace ska_ordered {
+
+struct prime_number_hash_policy;
+struct power_of_two_hash_policy;
+struct fibonacci_hash_policy;
+
+namespace detailv3 {
+template <typename Result, typename Functor>
+struct functor_storage : Functor {
+  functor_storage() = default;
+  functor_storage(const Functor& functor) : Functor(functor) {}
+  template <typename... Args>
+  Result operator()(Args&&... args) {
+    return static_cast<Functor&>(*this)(std::forward<Args>(args)...);
+  }
+  template <typename... Args>
+  Result operator()(Args&&... args) const {
+    return static_cast<const Functor&>(*this)(std::forward<Args>(args)...);
+  }
+};
+template <typename Result, typename... Args>
+struct functor_storage<Result, Result (*)(Args...)> {
+  typedef Result (*function_ptr)(Args...);
+  function_ptr function;
+  functor_storage(function_ptr function) : function(function) {}
+  Result operator()(Args... args) const {
+    return function(std::forward<Args>(args)...);
+  }
+  operator function_ptr&() {
+    return function;
+  }
+  operator const function_ptr&() {
+    return function;
+  }
+};
+template <typename key_type, typename value_type, typename hasher>
+struct KeyOrValueHasher : functor_storage<uint64_t, hasher> {
+  typedef functor_storage<uint64_t, hasher> hasher_storage;
+  KeyOrValueHasher() = default;
+  KeyOrValueHasher(const hasher& hash) : hasher_storage(hash) {}
+  uint64_t operator()(const key_type& key) {
+    return static_cast<hasher_storage&>(*this)(key);
+  }
+  uint64_t operator()(const key_type& key) const {
+    return static_cast<const hasher_storage&>(*this)(key);
+  }
+  uint64_t operator()(const value_type& value) {
+    return static_cast<hasher_storage&>(*this)(value.first);
+  }
+  uint64_t operator()(const value_type& value) const {
+    return static_cast<const hasher_storage&>(*this)(value.first);
+  }
+  template <typename F, typename S>
+  uint64_t operator()(const std::pair<F, S>& value) {
+    return static_cast<hasher_storage&>(*this)(value.first);
+  }
+  template <typename F, typename S>
+  uint64_t operator()(const std::pair<F, S>& value) const {
+    return static_cast<const hasher_storage&>(*this)(value.first);
+  }
+};
+template <typename key_type, typename value_type, typename key_equal>
+struct KeyOrValueEquality : functor_storage<bool, key_equal> {
+  typedef functor_storage<bool, key_equal> equality_storage;
+  KeyOrValueEquality() = default;
+  KeyOrValueEquality(const key_equal& equality) : equality_storage(equality) {}
+  bool operator()(const key_type& lhs, const key_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs, rhs);
+  }
+  bool operator()(const key_type& lhs, const value_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs, rhs.first);
+  }
+  bool operator()(const value_type& lhs, const key_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs);
+  }
+  bool operator()(const value_type& lhs, const value_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs.first);
+  }
+  template <typename F, typename S>
+  bool operator()(const key_type& lhs, const std::pair<F, S>& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs, rhs.first);
+  }
+  template <typename F, typename S>
+  bool operator()(const std::pair<F, S>& lhs, const key_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs);
+  }
+  template <typename F, typename S>
+  bool operator()(const value_type& lhs, const std::pair<F, S>& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs.first);
+  }
+  template <typename F, typename S>
+  bool operator()(const std::pair<F, S>& lhs, const value_type& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs.first);
+  }
+  template <typename FL, typename SL, typename FR, typename SR>
+  bool operator()(const std::pair<FL, SL>& lhs, const std::pair<FR, SR>& rhs) {
+    return static_cast<equality_storage&>(*this)(lhs.first, rhs.first);
+  }
+};
+static constexpr int8_t min_lookups = 4;
+template <typename T>
+// NOLINTNEXTLINE(cppcoreguidelines-special-member-functions)
+struct sherwood_v3_entry {
+  // NOLINTNEXTLINE(modernize-use-equals-default)
+  sherwood_v3_entry() {}
+  sherwood_v3_entry(int8_t distance_from_desired)
+      : distance_from_desired(distance_from_desired) {}
+  // NOLINTNEXTLINE(modernize-use-equals-default)
+  ~sherwood_v3_entry() {}
+
+  bool has_value() const {
+    return distance_from_desired >= 0;
+  }
+  bool is_empty() const {
+    return distance_from_desired < 0;
+  }
+  bool is_at_desired_position() const {
+    return distance_from_desired <= 0;
+  }
+  template <typename... Args>
+  void emplace(int8_t distance, Args&&... args) {
+    new (std::addressof(value)) T(std::forward<Args>(args)...);
+    distance_from_desired = distance;
+  }
+
+  void destroy_value() {
+    value.~T();
+    distance_from_desired = -1;
+  }
+
+  sherwood_v3_entry<T>* prev = nullptr;
+  sherwood_v3_entry<T>* next = nullptr;
+  int8_t distance_from_desired = -1;
+  static constexpr int8_t special_end_value = 0;
+  union {
+    T value;
+  };
+};
+
+inline int8_t log2(uint64_t value) {
+  static constexpr std::array<int8_t, 64> table = {
+      63, 0,  58, 1,  59, 47, 53, 2,  60, 39, 48, 27, 54, 33, 42, 3,
+      61, 51, 37, 40, 49, 18, 28, 20, 55, 30, 34, 11, 43, 14, 22, 4,
+      62, 57, 46, 52, 38, 26, 32, 41, 50, 36, 17, 19, 29, 10, 13, 21,
+      56, 45, 25, 31, 35, 16, 9,  12, 44, 24, 15, 8,  23, 7,  6,  5};
+  value |= value >> 1;
+  value |= value >> 2;
+  value |= value >> 4;
+  value |= value >> 8;
+  value |= value >> 16;
+  value |= value >> 32;
+  return table[((value - (value >> 1)) * 0x07EDD5E59A4E28C2) >> 58];
+}
+
+inline uint64_t next_power_of_two(uint64_t i) {
+  --i;
+  i |= i >> 1;
+  i |= i >> 2;
+  i |= i >> 4;
+  i |= i >> 8;
+  i |= i >> 16;
+  i |= i >> 32;
+  ++i;
+  return i;
+}
+
+// Implementation taken from http://en.cppreference.com/w/cpp/types/void_t
+// (it takes CWG1558 into account and also works for older compilers)
+template <typename... Ts>
+struct make_void {
+  typedef void type;
+};
+template <typename... Ts>
+using void_t = typename make_void<Ts...>::type;
+
+template <typename T, typename = void>
+struct HashPolicySelector {
+  typedef fibonacci_hash_policy type;
+};
+template <typename T>
+struct HashPolicySelector<T, void_t<typename T::hash_policy>> {
+  typedef typename T::hash_policy type;
+};
+
+template <
+    typename T,
+    typename FindKey,
+    typename ArgumentHash,
+    typename Hasher,
+    typename ArgumentEqual,
+    typename Equal,
+    typename ArgumentAlloc,
+    typename EntryAlloc>
+class sherwood_v3_table : private EntryAlloc, private Hasher, private Equal {
+  using Entry = detailv3::sherwood_v3_entry<T>;
+  using AllocatorTraits = std::allocator_traits<EntryAlloc>;
+  using EntryPointer = typename AllocatorTraits::pointer;
+
+ public:
+  struct convertible_to_iterator;
+
+  using value_type = T;
+  using size_type = uint64_t;
+  using difference_type = std::ptrdiff_t;
+  using hasher = ArgumentHash;
+  using key_equal = ArgumentEqual;
+  using allocator_type = EntryAlloc;
+  using reference = value_type&;
+  using const_reference = const value_type&;
+  using pointer = value_type*;
+  using const_pointer = const value_type*;
+
+  sherwood_v3_table() = default;
+  explicit sherwood_v3_table(
+      size_type bucket_count,
+      const ArgumentHash& hash = ArgumentHash(),
+      const ArgumentEqual& equal = ArgumentEqual(),
+      const ArgumentAlloc& alloc = ArgumentAlloc())
+      : EntryAlloc(alloc), Hasher(hash), Equal(equal) {
+    rehash(bucket_count);
+  }
+  sherwood_v3_table(size_type bucket_count, const ArgumentAlloc& alloc)
+      : sherwood_v3_table(
+            bucket_count,
+            ArgumentHash(),
+            ArgumentEqual(),
+            alloc) {}
+  sherwood_v3_table(
+      size_type bucket_count,
+      const ArgumentHash& hash,
+      const ArgumentAlloc& alloc)
+      : sherwood_v3_table(bucket_count, hash, ArgumentEqual(), alloc) {}
+  explicit sherwood_v3_table(const ArgumentAlloc& alloc) : EntryAlloc(alloc) {}
+  template <typename It>
+  sherwood_v3_table(
+      It first,
+      It last,
+      size_type bucket_count = 0,
+      const ArgumentHash& hash = ArgumentHash(),
+      const ArgumentEqual& equal = ArgumentEqual(),
+      const ArgumentAlloc& alloc = ArgumentAlloc())
+      : sherwood_v3_table(bucket_count, hash, equal, alloc) {
+    insert(first, last);
+  }
+  template <typename It>
+  sherwood_v3_table(
+      It first,
+      It last,
+      size_type bucket_count,
+      const ArgumentAlloc& alloc)
+      : sherwood_v3_table(
+            first,
+            last,
+            bucket_count,
+            ArgumentHash(),
+            ArgumentEqual(),
+            alloc) {}
+  template <typename It>
+  sherwood_v3_table(
+      It first,
+      It last,
+      size_type bucket_count,
+      const ArgumentHash& hash,
+      const ArgumentAlloc& alloc)
+      : sherwood_v3_table(
+            first,
+            last,
+            bucket_count,
+            hash,
+            ArgumentEqual(),
+            alloc) {}
+  sherwood_v3_table(
+      std::initializer_list<T> il,
+      size_type bucket_count = 0,
+      const ArgumentHash& hash = ArgumentHash(),
+      const ArgumentEqual& equal = ArgumentEqual(),
+      const ArgumentAlloc& alloc = ArgumentAlloc())
+      : sherwood_v3_table(bucket_count, hash, equal, alloc) {
+    if (bucket_count == 0)
+      rehash(il.size());
+    insert(il.begin(), il.end());
+  }
+  sherwood_v3_table(
+      std::initializer_list<T> il,
+      size_type bucket_count,
+      const ArgumentAlloc& alloc)
+      : sherwood_v3_table(
+            il,
+            bucket_count,
+            ArgumentHash(),
+            ArgumentEqual(),
+            alloc) {}
+  sherwood_v3_table(
+      std::initializer_list<T> il,
+      size_type bucket_count,
+      const ArgumentHash& hash,
+      const ArgumentAlloc& alloc)
+      : sherwood_v3_table(il, bucket_count, hash, ArgumentEqual(), alloc) {}
+  sherwood_v3_table(const sherwood_v3_table& other)
+      : sherwood_v3_table(
+            other,
+            AllocatorTraits::select_on_container_copy_construction(
+                other.get_allocator())) {}
+  sherwood_v3_table(const sherwood_v3_table& other, const ArgumentAlloc& alloc)
+      : EntryAlloc(alloc),
+        Hasher(other),
+        Equal(other),
+        _max_load_factor(other._max_load_factor) {
+    rehash_for_other_container(other);
+    try {
+      insert(other.begin(), other.end());
+    } catch (...) {
+      clear();
+      deallocate_data(entries, num_slots_minus_one, max_lookups);
+      throw;
+    }
+  }
+  sherwood_v3_table(sherwood_v3_table&& other) noexcept
+      : EntryAlloc(std::move(other)),
+        Hasher(std::move(other)),
+        Equal(std::move(other)) {
+    swap_pointers(other);
+  }
+  sherwood_v3_table(
+      sherwood_v3_table&& other,
+      const ArgumentAlloc& alloc) noexcept
+      : EntryAlloc(alloc), Hasher(std::move(other)), Equal(std::move(other)) {
+    swap_pointers(other);
+  }
+  sherwood_v3_table& operator=(const sherwood_v3_table& other) {
+    if (this == std::addressof(other))
+      return *this;
+
+    clear();
+    if constexpr (AllocatorTraits::propagate_on_container_copy_assignment::
+                      value) {
+      if (static_cast<EntryAlloc&>(*this) !=
+          static_cast<const EntryAlloc&>(other)) {
+        reset_to_empty_state();
+      }
+      static_cast<EntryAlloc&>(*this) = other;
+    }
+    _max_load_factor = other._max_load_factor;
+    static_cast<Hasher&>(*this) = other;
+    static_cast<Equal&>(*this) = other;
+    rehash_for_other_container(other);
+    insert(other.begin(), other.end());
+    return *this;
+  }
+  sherwood_v3_table& operator=(sherwood_v3_table&& other) noexcept {
+    if (this == std::addressof(other))
+      return *this;
+    else if constexpr (AllocatorTraits::propagate_on_container_move_assignment::
+                           value) {
+      clear();
+      reset_to_empty_state();
+      static_cast<EntryAlloc&>(*this) = std::move(other);
+      swap_pointers(other);
+    } else if (
+        static_cast<EntryAlloc&>(*this) == static_cast<EntryAlloc&>(other)) {
+      swap_pointers(other);
+    } else {
+      clear();
+      _max_load_factor = other._max_load_factor;
+      rehash_for_other_container(other);
+      for (T& elem : other)
+        emplace(std::move(elem));
+      other.clear();
+    }
+    static_cast<Hasher&>(*this) = std::move(other);
+    static_cast<Equal&>(*this) = std::move(other);
+    return *this;
+  }
+  ~sherwood_v3_table() {
+    clear();
+    deallocate_data(entries, num_slots_minus_one, max_lookups);
+  }
+
+  const allocator_type& get_allocator() const {
+    return static_cast<const allocator_type&>(*this);
+  }
+  const ArgumentEqual& key_eq() const {
+    return static_cast<const ArgumentEqual&>(*this);
+  }
+  const ArgumentHash& hash_function() const {
+    return static_cast<const ArgumentHash&>(*this);
+  }
+
+  template <typename ValueType>
+  struct templated_iterator {
+    templated_iterator() = default;
+    templated_iterator(EntryPointer current) : current(current) {}
+    EntryPointer current = EntryPointer();
+
+    using iterator_category = std::forward_iterator_tag;
+    using value_type = ValueType;
+    using difference_type = ptrdiff_t;
+    using pointer = ValueType*;
+    using reference = ValueType&;
+
+    friend bool operator==(
+        const templated_iterator& lhs,
+        const templated_iterator& rhs) {
+      return lhs.current == rhs.current;
+    }
+    friend bool operator!=(
+        const templated_iterator& lhs,
+        const templated_iterator& rhs) {
+      return !(lhs == rhs);
+    }
+
+    templated_iterator& operator++() {
+      current = current->next;
+      return *this;
+    }
+    templated_iterator operator++(int) {
+      templated_iterator copy(*this);
+      ++*this;
+      return copy;
+    }
+
+    ValueType& operator*() const {
+      return current->value;
+    }
+    ValueType* operator->() const {
+      return std::addressof(current->value);
+    }
+
+    // the template automatically disables the operator when value_type is
+    // already const, because that would cause a lot of compiler warnings
+    // otherwise.
+    template <
+        class target_type = const value_type,
+        class = std::enable_if_t<
+            std::is_same_v<target_type, const value_type> &&
+            !std::is_same_v<target_type, value_type>>>
+    operator templated_iterator<target_type>() const {
+      return {current};
+    }
+  };
+  using iterator = templated_iterator<value_type>;
+  using const_iterator = templated_iterator<const value_type>;
+
+  iterator begin() {
+    return sentinel->next;
+  }
+  const_iterator begin() const {
+    return sentinel->next;
+  }
+  const_iterator cbegin() const {
+    return begin();
+  }
+  iterator end() {
+    return sentinel;
+  }
+  const_iterator end() const {
+    return sentinel;
+  }
+  const_iterator cend() const {
+    return end();
+  }
+
+  iterator find(const FindKey& key) {
+    uint64_t index =
+        hash_policy.index_for_hash(hash_object(key), num_slots_minus_one);
+    EntryPointer it = entries + ptrdiff_t(index);
+    for (int8_t distance = 0; it->distance_from_desired >= distance;
+         ++distance, ++it) {
+      if (compares_equal(key, it->value))
+        return {it};
+    }
+    return end();
+  }
+  const_iterator find(const FindKey& key) const {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+    return const_cast<sherwood_v3_table*>(this)->find(key);
+  }
+  uint64_t count(const FindKey& key) const {
+    return find(key) == end() ? 0 : 1;
+  }
+  std::pair<iterator, iterator> equal_range(const FindKey& key) {
+    iterator found = find(key);
+    if (found == end())
+      return {found, found};
+    else
+      return {found, std::next(found)};
+  }
+  std::pair<const_iterator, const_iterator> equal_range(
+      const FindKey& key) const {
+    const_iterator found = find(key);
+    if (found == end())
+      return {found, found};
+    else
+      return {found, std::next(found)};
+  }
+
+  template <typename Key, typename... Args>
+  std::pair<iterator, bool> emplace(Key&& key, Args&&... args) {
+    uint64_t index =
+        hash_policy.index_for_hash(hash_object(key), num_slots_minus_one);
+    EntryPointer current_entry = entries + ptrdiff_t(index);
+    int8_t distance_from_desired = 0;
+    for (; current_entry->distance_from_desired >= distance_from_desired;
+         ++current_entry, ++distance_from_desired) {
+      // insertion of an existing key does not change ordering
+      if (compares_equal(key, current_entry->value))
+        return {{current_entry}, false};
+    }
+    return emplace_new_key(
+        distance_from_desired,
+        current_entry,
+        std::forward<Key>(key),
+        std::forward<Args>(args)...);
+  }
+
+  std::pair<iterator, bool> insert(const value_type& value) {
+    return emplace(value);
+  }
+  std::pair<iterator, bool> insert(value_type&& value) {
+    return emplace(std::move(value));
+  }
+  template <typename... Args>
+  iterator emplace_hint(const_iterator, Args&&... args) {
+    return emplace(std::forward<Args>(args)...).first;
+  }
+  iterator insert(const_iterator, const value_type& value) {
+    return emplace(value).first;
+  }
+  iterator insert(const_iterator, value_type&& value) {
+    return emplace(std::move(value)).first;
+  }
+
+  template <typename It>
+  void insert(It begin, It end) {
+    for (; begin != end; ++begin) {
+      emplace(*begin);
+    }
+  }
+  void insert(std::initializer_list<value_type> il) {
+    insert(il.begin(), il.end());
+  }
+
+  void rehash(uint64_t num_buckets) {
+    num_buckets = std::max(
+        num_buckets,
+        static_cast<uint64_t>(std::ceil(
+            static_cast<double>(num_elements) /
+            static_cast<double>(_max_load_factor))));
+    if (num_buckets == 0) {
+      reset_to_empty_state();
+      return;
+    }
+    auto new_prime_index = hash_policy.next_size_over(num_buckets);
+    if (num_buckets == bucket_count())
+      return;
+    int8_t new_max_lookups = compute_max_lookups(num_buckets);
+    EntryPointer new_buckets(
+        AllocatorTraits::allocate(*this, num_buckets + new_max_lookups));
+    EntryPointer special_end_item =
+        new_buckets + static_cast<ptrdiff_t>(num_buckets + new_max_lookups - 1);
+    for (EntryPointer it = new_buckets; it != special_end_item; ++it)
+      it->distance_from_desired = -1;
+    special_end_item->distance_from_desired = Entry::special_end_value;
+    std::swap(entries, new_buckets);
+    std::swap(num_slots_minus_one, num_buckets);
+    --num_slots_minus_one;
+    hash_policy.commit(new_prime_index);
+    int8_t old_max_lookups = max_lookups;
+    max_lookups = new_max_lookups;
+    num_elements = 0;
+
+    auto start = sentinel->next;
+    // point sentinel to itself;
+    reset_list();
+    // reinsert list
+    for (EntryPointer it = start; it != sentinel;) {
+      auto next = it->next;
+      emplace(std::move(it->value));
+      it->destroy_value();
+      it = next;
+    }
+
+    deallocate_data(new_buckets, num_buckets, old_max_lookups);
+  }
+
+  void reserve(uint64_t num_elements_) {
+    uint64_t required_buckets = num_buckets_for_reserve(num_elements_);
+    if (required_buckets > bucket_count())
+      rehash(required_buckets);
+  }
+
+  void replace_linked_list_position(
+      EntryPointer to_be_replaced,
+      EntryPointer new_node) {
+    remove_from_list(new_node);
+    insert_after(new_node, to_be_replaced->prev);
+    remove_from_list(to_be_replaced);
+  }
+
+  // the return value is a type that can be converted to an iterator
+  // the reason for doing this is that it's not free to find the
+  // iterator pointing at the next element. if you care about the
+  // next iterator, turn the return value into an iterator
+  convertible_to_iterator erase(const_iterator to_erase) {
+    EntryPointer current = to_erase.current;
+    remove_from_list(current);
+    current->destroy_value();
+    --num_elements;
+
+    for (EntryPointer next = current + ptrdiff_t(1);
+         !next->is_at_desired_position();
+         ++current, ++next) {
+      // if an entry is being removed, and there are other entries with the
+      // same hash, the other entries get moved to their desired position by
+      // reinserting.
+      current->emplace(next->distance_from_desired - 1, std::move(next->value));
+      replace_linked_list_position(next, current);
+      next->destroy_value();
+    }
+    return {to_erase.current};
+  }
+
+  iterator erase(const_iterator begin_it, const_iterator end_it) {
+    // whenever an entry is removed and there are other entries with the same
+    // hash, the other entries must get moved to their desired position.
+    // any reference to a moved entry is invalidated.
+    // here, we iterate through the range, and make sure that we update
+    // the pointer to our next entry in the list or the end of the iterator
+    // when it is invalidated.
+
+    auto curr_iter = begin_it.current;
+    auto next_iter = curr_iter->next;
+    auto end_iter = end_it.current;
+
+    while (curr_iter != end_iter) {
+      remove_from_list(curr_iter);
+      curr_iter->destroy_value();
+      --num_elements;
+
+      for (EntryPointer next_hash_slot = curr_iter + ptrdiff_t(1);
+           !next_hash_slot->is_at_desired_position();
+           ++curr_iter, ++next_hash_slot) {
+        curr_iter->emplace(
+            next_hash_slot->distance_from_desired - 1,
+            std::move(next_hash_slot->value));
+        replace_linked_list_position(next_hash_slot, curr_iter);
+        next_hash_slot->destroy_value();
+
+        // we are invalidating next_iter or end_iter
+        if (next_hash_slot == end_iter) {
+          end_iter = curr_iter;
+        } else if (next_hash_slot == next_iter) {
+          next_iter = curr_iter;
+        }
+      }
+      curr_iter = next_iter;
+      next_iter = curr_iter->next;
+    }
+
+    return {end_iter};
+  }
+
+  uint64_t erase(const FindKey& key) {
+    auto found = find(key);
+    if (found == end())
+      return 0;
+    else {
+      erase(found);
+      return 1;
+    }
+  }
+
+  void clear() {
+    for (EntryPointer it = entries,
+                      end = it +
+             static_cast<ptrdiff_t>(num_slots_minus_one + max_lookups);
+         it != end;
+         ++it) {
+      if (it->has_value())
+        it->destroy_value();
+    }
+    reset_list();
+    num_elements = 0;
+  }
+
+  void shrink_to_fit() {
+    rehash_for_other_container(*this);
+  }
+
+  void swap(sherwood_v3_table& other) noexcept {
+    using std::swap;
+    swap_pointers(other);
+    swap(static_cast<ArgumentHash&>(*this), static_cast<ArgumentHash&>(other));
+    swap(
+        static_cast<ArgumentEqual&>(*this), static_cast<ArgumentEqual&>(other));
+    if (AllocatorTraits::propagate_on_container_swap::value)
+      swap(static_cast<EntryAlloc&>(*this), static_cast<EntryAlloc&>(other));
+  }
+
+  uint64_t size() const {
+    return num_elements;
+  }
+  uint64_t max_size() const {
+    return (AllocatorTraits::max_size(*this)) / sizeof(Entry);
+  }
+  uint64_t bucket_count() const {
+    return num_slots_minus_one ? num_slots_minus_one + 1 : 0;
+  }
+  size_type max_bucket_count() const {
+    return (AllocatorTraits::max_size(*this) - min_lookups) / sizeof(Entry);
+  }
+  uint64_t bucket(const FindKey& key) const {
+    return hash_policy.index_for_hash(hash_object(key), num_slots_minus_one);
+  }
+  float load_factor() const {
+    uint64_t buckets = bucket_count();
+    if (buckets)
+      return static_cast<float>(num_elements) / bucket_count();
+    else
+      return 0;
+  }
+  void max_load_factor(float value) {
+    _max_load_factor = value;
+  }
+  float max_load_factor() const {
+    return _max_load_factor;
+  }
+
+  bool empty() const {
+    return num_elements == 0;
+  }
+
+ private:
+  EntryPointer entries = empty_default_table();
+  uint64_t num_slots_minus_one = 0;
+  typename HashPolicySelector<ArgumentHash>::type hash_policy;
+  int8_t max_lookups = detailv3::min_lookups - 1;
+  float _max_load_factor = 0.5f;
+  uint64_t num_elements = 0;
+  std::unique_ptr<sherwood_v3_entry<T>> sentinel_val;
+
+  // head of doubly linked list
+  EntryPointer sentinel = initSentinel();
+
+  EntryPointer initSentinel() {
+    // needs to be a pointer so that hash map can be used with forward declared
+    // types
+    sentinel_val = std::make_unique<sherwood_v3_entry<T>>();
+    sentinel = sentinel_val.get();
+    reset_list();
+    return sentinel;
+  }
+
+  EntryPointer empty_default_table() {
+    EntryPointer result =
+        AllocatorTraits::allocate(*this, detailv3::min_lookups);
+    EntryPointer special_end_item =
+        result + static_cast<ptrdiff_t>(detailv3::min_lookups - 1);
+    for (EntryPointer it = result; it != special_end_item; ++it)
+      it->distance_from_desired = -1;
+    special_end_item->distance_from_desired = Entry::special_end_value;
+    return result;
+  }
+
+  static int8_t compute_max_lookups(uint64_t num_buckets) {
+    int8_t desired = detailv3::log2(num_buckets);
+    return std::max(detailv3::min_lookups, desired);
+  }
+
+  uint64_t num_buckets_for_reserve(uint64_t num_elements_) const {
+    return static_cast<uint64_t>(std::ceil(
+        static_cast<double>(num_elements_) /
+        std::min(0.5, static_cast<double>(_max_load_factor))));
+  }
+  void rehash_for_other_container(const sherwood_v3_table& other) {
+    rehash(
+        std::min(num_buckets_for_reserve(other.size()), other.bucket_count()));
+  }
+
+  void swap_pointers(sherwood_v3_table& other) {
+    using std::swap;
+    swap(hash_policy, other.hash_policy);
+    swap(entries, other.entries);
+    swap(num_slots_minus_one, other.num_slots_minus_one);
+    swap(num_elements, other.num_elements);
+    swap(max_lookups, other.max_lookups);
+    swap(_max_load_factor, other._max_load_factor);
+    swap(sentinel, other.sentinel);
+    swap(sentinel_val, other.sentinel_val);
+  }
+
+  void reset_list() {
+    sentinel->next = sentinel;
+    sentinel->prev = sentinel;
+  }
+
+  void remove_from_list(EntryPointer elem) {
+    elem->prev->next = elem->next;
+    elem->next->prev = elem->prev;
+  }
+
+  void insert_after(EntryPointer new_elem, EntryPointer prev) {
+    auto next = prev->next;
+
+    prev->next = new_elem;
+    new_elem->prev = prev;
+
+    new_elem->next = next;
+    next->prev = new_elem;
+  }
+
+  void swap_adjacent_nodes(EntryPointer before, EntryPointer after) {
+    // sentinel stays constant, so before->prev cannot equal after
+    auto before_prev = before->prev;
+    auto after_next = after->next;
+
+    before_prev->next = after;
+    after->prev = before_prev;
+
+    after_next->prev = before;
+    before->next = after_next;
+
+    before->prev = after;
+    after->next = before;
+  }
+
+  void swap_positions(EntryPointer p1, EntryPointer p2) {
+    if (p1 == p2) {
+      return;
+    }
+    if (p1->next == p2) {
+      return swap_adjacent_nodes(p1, p2);
+    } else if (p2->next == p1) {
+      return swap_adjacent_nodes(p2, p1);
+    }
+
+    auto p1_prev = p1->prev;
+    auto p1_next = p1->next;
+
+    auto p2_prev = p2->prev;
+    auto p2_next = p2->next;
+
+    p1_prev->next = p2;
+    p2->prev = p1_prev;
+
+    p1_next->prev = p2;
+    p2->next = p1_next;
+
+    p2_prev->next = p1;
+    p1->prev = p2_prev;
+
+    p2_next->prev = p1;
+    p1->next = p2_next;
+  }
+
+  void append_to_list(EntryPointer new_tail) {
+    insert_after(new_tail, sentinel->prev);
+  }
+
+  template <typename Key, typename... Args>
+  SKA_NOINLINE(std::pair<iterator, bool>)
+  emplace_new_key(
+      int8_t distance_from_desired,
+      EntryPointer current_entry,
+      Key&& key,
+      Args&&... args) {
+    using std::swap;
+    if (num_slots_minus_one == 0 || distance_from_desired == max_lookups ||
+        static_cast<double>(num_elements + 1) >
+            static_cast<double>(num_slots_minus_one + 1) *
+                static_cast<double>(_max_load_factor)) {
+      grow();
+      return emplace(std::forward<Key>(key), std::forward<Args>(args)...);
+    } else if (current_entry->is_empty()) {
+      current_entry->emplace(
+          distance_from_desired,
+          std::forward<Key>(key),
+          std::forward<Args>(args)...);
+      ++num_elements;
+      append_to_list(current_entry);
+      return {{current_entry}, true};
+    }
+    value_type to_insert(std::forward<Key>(key), std::forward<Args>(args)...);
+    swap(distance_from_desired, current_entry->distance_from_desired);
+    // We maintain the invariant that:
+    // - result.current_entry contains the new value we're inserting
+    //   and is in the LinkedList position of to_insert
+    // - to_insert contains the value that represents the position of
+    //   result.current_entry
+    swap(to_insert, current_entry->value);
+    iterator result = {current_entry};
+    for (++distance_from_desired, ++current_entry;; ++current_entry) {
+      if (current_entry->is_empty()) {
+        current_entry->emplace(distance_from_desired, std::move(to_insert));
+        append_to_list(current_entry);
+        // now we can swap back the displaced value to its correct position,
+        // putting the new value we're inserting to the front of the list
+        swap_positions(current_entry, result.current);
+        ++num_elements;
+        return {result, true};
+      } else if (current_entry->distance_from_desired < distance_from_desired) {
+        swap(distance_from_desired, current_entry->distance_from_desired);
+        swap(to_insert, current_entry->value);
+        // to maintain our invariants we need to swap positions
+        // of result.current & current_entry:
+        swap_positions(result.current, current_entry);
+        ++distance_from_desired;
+      } else {
+        ++distance_from_desired;
+        if (distance_from_desired == max_lookups) {
+          // the displaced element gets put back into its correct position
+          // we grow the hash table, and then try again to reinsert the new
+          // element
+          swap(to_insert, result.current->value);
+          grow();
+          return emplace(std::move(to_insert));
+        }
+      }
+    }
+  }
+
+  void grow() {
+    rehash(std::max(uint64_t(4), 2 * bucket_count()));
+  }
+
+  void deallocate_data(
+      EntryPointer begin,
+      uint64_t num_slots_minus_one_,
+      int8_t max_lookups_) {
+    AllocatorTraits::deallocate(
+        *this, begin, num_slots_minus_one_ + max_lookups_ + 1);
+  }
+
+  void reset_to_empty_state() {
+    deallocate_data(entries, num_slots_minus_one, max_lookups);
+    entries = empty_default_table();
+    num_slots_minus_one = 0;
+    hash_policy.reset();
+    max_lookups = detailv3::min_lookups - 1;
+  }
+
+  template <typename U>
+  uint64_t hash_object(const U& key) {
+    return static_cast<Hasher&>(*this)(key);
+  }
+  template <typename U>
+  uint64_t hash_object(const U& key) const {
+    return static_cast<const Hasher&>(*this)(key);
+  }
+  template <typename L, typename R>
+  bool compares_equal(const L& lhs, const R& rhs) {
+    return static_cast<Equal&>(*this)(lhs, rhs);
+  }
+
+ public:
+  struct convertible_to_iterator {
+    EntryPointer it;
+
+    operator iterator() {
+      if (it->has_value())
+        return {it};
+      else
+        return ++iterator{it};
+    }
+    operator const_iterator() {
+      if (it->has_value())
+        return {it};
+      else
+        return ++const_iterator{it};
+    }
+  };
+};
+} // namespace detailv3
+
+struct prime_number_hash_policy {
+  static uint64_t mod0(uint64_t) {
+    return 0llu;
+  }
+  static uint64_t mod2(uint64_t hash) {
+    return hash % 2llu;
+  }
+  static uint64_t mod3(uint64_t hash) {
+    return hash % 3llu;
+  }
+  static uint64_t mod5(uint64_t hash) {
+    return hash % 5llu;
+  }
+  static uint64_t mod7(uint64_t hash) {
+    return hash % 7llu;
+  }
+  static uint64_t mod11(uint64_t hash) {
+    return hash % 11llu;
+  }
+  static uint64_t mod13(uint64_t hash) {
+    return hash % 13llu;
+  }
+  static uint64_t mod17(uint64_t hash) {
+    return hash % 17llu;
+  }
+  static uint64_t mod23(uint64_t hash) {
+    return hash % 23llu;
+  }
+  static uint64_t mod29(uint64_t hash) {
+    return hash % 29llu;
+  }
+  static uint64_t mod37(uint64_t hash) {
+    return hash % 37llu;
+  }
+  static uint64_t mod47(uint64_t hash) {
+    return hash % 47llu;
+  }
+  static uint64_t mod59(uint64_t hash) {
+    return hash % 59llu;
+  }
+  static uint64_t mod73(uint64_t hash) {
+    return hash % 73llu;
+  }
+  static uint64_t mod97(uint64_t hash) {
+    return hash % 97llu;
+  }
+  static uint64_t mod127(uint64_t hash) {
+    return hash % 127llu;
+  }
+  static uint64_t mod151(uint64_t hash) {
+    return hash % 151llu;
+  }
+  static uint64_t mod197(uint64_t hash) {
+    return hash % 197llu;
+  }
+  static uint64_t mod251(uint64_t hash) {
+    return hash % 251llu;
+  }
+  static uint64_t mod313(uint64_t hash) {
+    return hash % 313llu;
+  }
+  static uint64_t mod397(uint64_t hash) {
+    return hash % 397llu;
+  }
+  static uint64_t mod499(uint64_t hash) {
+    return hash % 499llu;
+  }
+  static uint64_t mod631(uint64_t hash) {
+    return hash % 631llu;
+  }
+  static uint64_t mod797(uint64_t hash) {
+    return hash % 797llu;
+  }
+  static uint64_t mod1009(uint64_t hash) {
+    return hash % 1009llu;
+  }
+  static uint64_t mod1259(uint64_t hash) {
+    return hash % 1259llu;
+  }
+  static uint64_t mod1597(uint64_t hash) {
+    return hash % 1597llu;
+  }
+  static uint64_t mod2011(uint64_t hash) {
+    return hash % 2011llu;
+  }
+  static uint64_t mod2539(uint64_t hash) {
+    return hash % 2539llu;
+  }
+  static uint64_t mod3203(uint64_t hash) {
+    return hash % 3203llu;
+  }
+  static uint64_t mod4027(uint64_t hash) {
+    return hash % 4027llu;
+  }
+  static uint64_t mod5087(uint64_t hash) {
+    return hash % 5087llu;
+  }
+  static uint64_t mod6421(uint64_t hash) {
+    return hash % 6421llu;
+  }
+  static uint64_t mod8089(uint64_t hash) {
+    return hash % 8089llu;
+  }
+  static uint64_t mod10193(uint64_t hash) {
+    return hash % 10193llu;
+  }
+  static uint64_t mod12853(uint64_t hash) {
+    return hash % 12853llu;
+  }
+  static uint64_t mod16193(uint64_t hash) {
+    return hash % 16193llu;
+  }
+  static uint64_t mod20399(uint64_t hash) {
+    return hash % 20399llu;
+  }
+  static uint64_t mod25717(uint64_t hash) {
+    return hash % 25717llu;
+  }
+  static uint64_t mod32401(uint64_t hash) {
+    return hash % 32401llu;
+  }
+  static uint64_t mod40823(uint64_t hash) {
+    return hash % 40823llu;
+  }
+  static uint64_t mod51437(uint64_t hash) {
+    return hash % 51437llu;
+  }
+  static uint64_t mod64811(uint64_t hash) {
+    return hash % 64811llu;
+  }
+  static uint64_t mod81649(uint64_t hash) {
+    return hash % 81649llu;
+  }
+  static uint64_t mod102877(uint64_t hash) {
+    return hash % 102877llu;
+  }
+  static uint64_t mod129607(uint64_t hash) {
+    return hash % 129607llu;
+  }
+  static uint64_t mod163307(uint64_t hash) {
+    return hash % 163307llu;
+  }
+  static uint64_t mod205759(uint64_t hash) {
+    return hash % 205759llu;
+  }
+  static uint64_t mod259229(uint64_t hash) {
+    return hash % 259229llu;
+  }
+  static uint64_t mod326617(uint64_t hash) {
+    return hash % 326617llu;
+  }
+  static uint64_t mod411527(uint64_t hash) {
+    return hash % 411527llu;
+  }
+  static uint64_t mod518509(uint64_t hash) {
+    return hash % 518509llu;
+  }
+  static uint64_t mod653267(uint64_t hash) {
+    return hash % 653267llu;
+  }
+  static uint64_t mod823117(uint64_t hash) {
+    return hash % 823117llu;
+  }
+  static uint64_t mod1037059(uint64_t hash) {
+    return hash % 1037059llu;
+  }
+  static uint64_t mod1306601(uint64_t hash) {
+    return hash % 1306601llu;
+  }
+  static uint64_t mod1646237(uint64_t hash) {
+    return hash % 1646237llu;
+  }
+  static uint64_t mod2074129(uint64_t hash) {
+    return hash % 2074129llu;
+  }
+  static uint64_t mod2613229(uint64_t hash) {
+    return hash % 2613229llu;
+  }
+  static uint64_t mod3292489(uint64_t hash) {
+    return hash % 3292489llu;
+  }
+  static uint64_t mod4148279(uint64_t hash) {
+    return hash % 4148279llu;
+  }
+  static uint64_t mod5226491(uint64_t hash) {
+    return hash % 5226491llu;
+  }
+  static uint64_t mod6584983(uint64_t hash) {
+    return hash % 6584983llu;
+  }
+  static uint64_t mod8296553(uint64_t hash) {
+    return hash % 8296553llu;
+  }
+  static uint64_t mod10453007(uint64_t hash) {
+    return hash % 10453007llu;
+  }
+  static uint64_t mod13169977(uint64_t hash) {
+    return hash % 13169977llu;
+  }
+  static uint64_t mod16593127(uint64_t hash) {
+    return hash % 16593127llu;
+  }
+  static uint64_t mod20906033(uint64_t hash) {
+    return hash % 20906033llu;
+  }
+  static uint64_t mod26339969(uint64_t hash) {
+    return hash % 26339969llu;
+  }
+  static uint64_t mod33186281(uint64_t hash) {
+    return hash % 33186281llu;
+  }
+  static uint64_t mod41812097(uint64_t hash) {
+    return hash % 41812097llu;
+  }
+  static uint64_t mod52679969(uint64_t hash) {
+    return hash % 52679969llu;
+  }
+  static uint64_t mod66372617(uint64_t hash) {
+    return hash % 66372617llu;
+  }
+  static uint64_t mod83624237(uint64_t hash) {
+    return hash % 83624237llu;
+  }
+  static uint64_t mod105359939(uint64_t hash) {
+    return hash % 105359939llu;
+  }
+  static uint64_t mod132745199(uint64_t hash) {
+    return hash % 132745199llu;
+  }
+  static uint64_t mod167248483(uint64_t hash) {
+    return hash % 167248483llu;
+  }
+  static uint64_t mod210719881(uint64_t hash) {
+    return hash % 210719881llu;
+  }
+  static uint64_t mod265490441(uint64_t hash) {
+    return hash % 265490441llu;
+  }
+  static uint64_t mod334496971(uint64_t hash) {
+    return hash % 334496971llu;
+  }
+  static uint64_t mod421439783(uint64_t hash) {
+    return hash % 421439783llu;
+  }
+  static uint64_t mod530980861(uint64_t hash) {
+    return hash % 530980861llu;
+  }
+  static uint64_t mod668993977(uint64_t hash) {
+    return hash % 668993977llu;
+  }
+  static uint64_t mod842879579(uint64_t hash) {
+    return hash % 842879579llu;
+  }
+  static uint64_t mod1061961721(uint64_t hash) {
+    return hash % 1061961721llu;
+  }
+  static uint64_t mod1337987929(uint64_t hash) {
+    return hash % 1337987929llu;
+  }
+  static uint64_t mod1685759167(uint64_t hash) {
+    return hash % 1685759167llu;
+  }
+  static uint64_t mod2123923447(uint64_t hash) {
+    return hash % 2123923447llu;
+  }
+  static uint64_t mod2675975881(uint64_t hash) {
+    return hash % 2675975881llu;
+  }
+  static uint64_t mod3371518343(uint64_t hash) {
+    return hash % 3371518343llu;
+  }
+  static uint64_t mod4247846927(uint64_t hash) {
+    return hash % 4247846927llu;
+  }
+  static uint64_t mod5351951779(uint64_t hash) {
+    return hash % 5351951779llu;
+  }
+  static uint64_t mod6743036717(uint64_t hash) {
+    return hash % 6743036717llu;
+  }
+  static uint64_t mod8495693897(uint64_t hash) {
+    return hash % 8495693897llu;
+  }
+  static uint64_t mod10703903591(uint64_t hash) {
+    return hash % 10703903591llu;
+  }
+  static uint64_t mod13486073473(uint64_t hash) {
+    return hash % 13486073473llu;
+  }
+  static uint64_t mod16991387857(uint64_t hash) {
+    return hash % 16991387857llu;
+  }
+  static uint64_t mod21407807219(uint64_t hash) {
+    return hash % 21407807219llu;
+  }
+  static uint64_t mod26972146961(uint64_t hash) {
+    return hash % 26972146961llu;
+  }
+  static uint64_t mod33982775741(uint64_t hash) {
+    return hash % 33982775741llu;
+  }
+  static uint64_t mod42815614441(uint64_t hash) {
+    return hash % 42815614441llu;
+  }
+  static uint64_t mod53944293929(uint64_t hash) {
+    return hash % 53944293929llu;
+  }
+  static uint64_t mod67965551447(uint64_t hash) {
+    return hash % 67965551447llu;
+  }
+  static uint64_t mod85631228929(uint64_t hash) {
+    return hash % 85631228929llu;
+  }
+  static uint64_t mod107888587883(uint64_t hash) {
+    return hash % 107888587883llu;
+  }
+  static uint64_t mod135931102921(uint64_t hash) {
+    return hash % 135931102921llu;
+  }
+  static uint64_t mod171262457903(uint64_t hash) {
+    return hash % 171262457903llu;
+  }
+  static uint64_t mod215777175787(uint64_t hash) {
+    return hash % 215777175787llu;
+  }
+  static uint64_t mod271862205833(uint64_t hash) {
+    return hash % 271862205833llu;
+  }
+  static uint64_t mod342524915839(uint64_t hash) {
+    return hash % 342524915839llu;
+  }
+  static uint64_t mod431554351609(uint64_t hash) {
+    return hash % 431554351609llu;
+  }
+  static uint64_t mod543724411781(uint64_t hash) {
+    return hash % 543724411781llu;
+  }
+  static uint64_t mod685049831731(uint64_t hash) {
+    return hash % 685049831731llu;
+  }
+  static uint64_t mod863108703229(uint64_t hash) {
+    return hash % 863108703229llu;
+  }
+  static uint64_t mod1087448823553(uint64_t hash) {
+    return hash % 1087448823553llu;
+  }
+  static uint64_t mod1370099663459(uint64_t hash) {
+    return hash % 1370099663459llu;
+  }
+  static uint64_t mod1726217406467(uint64_t hash) {
+    return hash % 1726217406467llu;
+  }
+  static uint64_t mod2174897647073(uint64_t hash) {
+    return hash % 2174897647073llu;
+  }
+  static uint64_t mod2740199326961(uint64_t hash) {
+    return hash % 2740199326961llu;
+  }
+  static uint64_t mod3452434812973(uint64_t hash) {
+    return hash % 3452434812973llu;
+  }
+  static uint64_t mod4349795294267(uint64_t hash) {
+    return hash % 4349795294267llu;
+  }
+  static uint64_t mod5480398654009(uint64_t hash) {
+    return hash % 5480398654009llu;
+  }
+  static uint64_t mod6904869625999(uint64_t hash) {
+    return hash % 6904869625999llu;
+  }
+  static uint64_t mod8699590588571(uint64_t hash) {
+    return hash % 8699590588571llu;
+  }
+  static uint64_t mod10960797308051(uint64_t hash) {
+    return hash % 10960797308051llu;
+  }
+  static uint64_t mod13809739252051(uint64_t hash) {
+    return hash % 13809739252051llu;
+  }
+  static uint64_t mod17399181177241(uint64_t hash) {
+    return hash % 17399181177241llu;
+  }
+  static uint64_t mod21921594616111(uint64_t hash) {
+    return hash % 21921594616111llu;
+  }
+  static uint64_t mod27619478504183(uint64_t hash) {
+    return hash % 27619478504183llu;
+  }
+  static uint64_t mod34798362354533(uint64_t hash) {
+    return hash % 34798362354533llu;
+  }
+  static uint64_t mod43843189232363(uint64_t hash) {
+    return hash % 43843189232363llu;
+  }
+  static uint64_t mod55238957008387(uint64_t hash) {
+    return hash % 55238957008387llu;
+  }
+  static uint64_t mod69596724709081(uint64_t hash) {
+    return hash % 69596724709081llu;
+  }
+  static uint64_t mod87686378464759(uint64_t hash) {
+    return hash % 87686378464759llu;
+  }
+  static uint64_t mod110477914016779(uint64_t hash) {
+    return hash % 110477914016779llu;
+  }
+  static uint64_t mod139193449418173(uint64_t hash) {
+    return hash % 139193449418173llu;
+  }
+  static uint64_t mod175372756929481(uint64_t hash) {
+    return hash % 175372756929481llu;
+  }
+  static uint64_t mod220955828033581(uint64_t hash) {
+    return hash % 220955828033581llu;
+  }
+  static uint64_t mod278386898836457(uint64_t hash) {
+    return hash % 278386898836457llu;
+  }
+  static uint64_t mod350745513859007(uint64_t hash) {
+    return hash % 350745513859007llu;
+  }
+  static uint64_t mod441911656067171(uint64_t hash) {
+    return hash % 441911656067171llu;
+  }
+  static uint64_t mod556773797672909(uint64_t hash) {
+    return hash % 556773797672909llu;
+  }
+  static uint64_t mod701491027718027(uint64_t hash) {
+    return hash % 701491027718027llu;
+  }
+  static uint64_t mod883823312134381(uint64_t hash) {
+    return hash % 883823312134381llu;
+  }
+  static uint64_t mod1113547595345903(uint64_t hash) {
+    return hash % 1113547595345903llu;
+  }
+  static uint64_t mod1402982055436147(uint64_t hash) {
+    return hash % 1402982055436147llu;
+  }
+  static uint64_t mod1767646624268779(uint64_t hash) {
+    return hash % 1767646624268779llu;
+  }
+  static uint64_t mod2227095190691797(uint64_t hash) {
+    return hash % 2227095190691797llu;
+  }
+  static uint64_t mod2805964110872297(uint64_t hash) {
+    return hash % 2805964110872297llu;
+  }
+  static uint64_t mod3535293248537579(uint64_t hash) {
+    return hash % 3535293248537579llu;
+  }
+  static uint64_t mod4454190381383713(uint64_t hash) {
+    return hash % 4454190381383713llu;
+  }
+  static uint64_t mod5611928221744609(uint64_t hash) {
+    return hash % 5611928221744609llu;
+  }
+  static uint64_t mod7070586497075177(uint64_t hash) {
+    return hash % 7070586497075177llu;
+  }
+  static uint64_t mod8908380762767489(uint64_t hash) {
+    return hash % 8908380762767489llu;
+  }
+  static uint64_t mod11223856443489329(uint64_t hash) {
+    return hash % 11223856443489329llu;
+  }
+  static uint64_t mod14141172994150357(uint64_t hash) {
+    return hash % 14141172994150357llu;
+  }
+  static uint64_t mod17816761525534927(uint64_t hash) {
+    return hash % 17816761525534927llu;
+  }
+  static uint64_t mod22447712886978529(uint64_t hash) {
+    return hash % 22447712886978529llu;
+  }
+  static uint64_t mod28282345988300791(uint64_t hash) {
+    return hash % 28282345988300791llu;
+  }
+  static uint64_t mod35633523051069991(uint64_t hash) {
+    return hash % 35633523051069991llu;
+  }
+  static uint64_t mod44895425773957261(uint64_t hash) {
+    return hash % 44895425773957261llu;
+  }
+  static uint64_t mod56564691976601587(uint64_t hash) {
+    return hash % 56564691976601587llu;
+  }
+  static uint64_t mod71267046102139967(uint64_t hash) {
+    return hash % 71267046102139967llu;
+  }
+  static uint64_t mod89790851547914507(uint64_t hash) {
+    return hash % 89790851547914507llu;
+  }
+  static uint64_t mod113129383953203213(uint64_t hash) {
+    return hash % 113129383953203213llu;
+  }
+  static uint64_t mod142534092204280003(uint64_t hash) {
+    return hash % 142534092204280003llu;
+  }
+  static uint64_t mod179581703095829107(uint64_t hash) {
+    return hash % 179581703095829107llu;
+  }
+  static uint64_t mod226258767906406483(uint64_t hash) {
+    return hash % 226258767906406483llu;
+  }
+  static uint64_t mod285068184408560057(uint64_t hash) {
+    return hash % 285068184408560057llu;
+  }
+  static uint64_t mod359163406191658253(uint64_t hash) {
+    return hash % 359163406191658253llu;
+  }
+  static uint64_t mod452517535812813007(uint64_t hash) {
+    return hash % 452517535812813007llu;
+  }
+  static uint64_t mod570136368817120201(uint64_t hash) {
+    return hash % 570136368817120201llu;
+  }
+  static uint64_t mod718326812383316683(uint64_t hash) {
+    return hash % 718326812383316683llu;
+  }
+  static uint64_t mod905035071625626043(uint64_t hash) {
+    return hash % 905035071625626043llu;
+  }
+  static uint64_t mod1140272737634240411(uint64_t hash) {
+    return hash % 1140272737634240411llu;
+  }
+  static uint64_t mod1436653624766633509(uint64_t hash) {
+    return hash % 1436653624766633509llu;
+  }
+  static uint64_t mod1810070143251252131(uint64_t hash) {
+    return hash % 1810070143251252131llu;
+  }
+  static uint64_t mod2280545475268481167(uint64_t hash) {
+    return hash % 2280545475268481167llu;
+  }
+  static uint64_t mod2873307249533267101(uint64_t hash) {
+    return hash % 2873307249533267101llu;
+  }
+  static uint64_t mod3620140286502504283(uint64_t hash) {
+    return hash % 3620140286502504283llu;
+  }
+  static uint64_t mod4561090950536962147(uint64_t hash) {
+    return hash % 4561090950536962147llu;
+  }
+  static uint64_t mod5746614499066534157(uint64_t hash) {
+    return hash % 5746614499066534157llu;
+  }
+  static uint64_t mod7240280573005008577(uint64_t hash) {
+    return hash % 7240280573005008577llu;
+  }
+  static uint64_t mod9122181901073924329(uint64_t hash) {
+    return hash % 9122181901073924329llu;
+  }
+  static uint64_t mod11493228998133068689(uint64_t hash) {
+    return hash % 11493228998133068689llu;
+  }
+  static uint64_t mod14480561146010017169(uint64_t hash) {
+    return hash % 14480561146010017169llu;
+  }
+  static uint64_t mod18446744073709551557(uint64_t hash) {
+    return hash % 18446744073709551557llu;
+  }
+
+  using mod_function = uint64_t (*)(uint64_t);
+
+  mod_function next_size_over(uint64_t& size) const {
+    // prime numbers generated by the following method:
+    // 1. start with a prime p = 2
+    // 2. go to wolfram alpha and get p = NextPrime(2 * p)
+    // 3. repeat 2. until you overflow 64 bits
+    // you now have large gaps which you would hit if somebody called reserve()
+    // with an unlucky number.
+    // 4. to fill the gaps for every prime p go to wolfram alpha and get
+    // ClosestPrime(p * 2^(1/3)) and ClosestPrime(p * 2^(2/3)) and put those in
+    // the gaps
+    // 5. get PrevPrime(2^64) and put it at the end
+    // NOLINTNEXTLINE(*c-array*)
+    static constexpr const uint64_t prime_list[] = {
+        2llu,
+        3llu,
+        5llu,
+        7llu,
+        11llu,
+        13llu,
+        17llu,
+        23llu,
+        29llu,
+        37llu,
+        47llu,
+        59llu,
+        73llu,
+        97llu,
+        127llu,
+        151llu,
+        197llu,
+        251llu,
+        313llu,
+        397llu,
+        499llu,
+        631llu,
+        797llu,
+        1009llu,
+        1259llu,
+        1597llu,
+        2011llu,
+        2539llu,
+        3203llu,
+        4027llu,
+        5087llu,
+        6421llu,
+        8089llu,
+        10193llu,
+        12853llu,
+        16193llu,
+        20399llu,
+        25717llu,
+        32401llu,
+        40823llu,
+        51437llu,
+        64811llu,
+        81649llu,
+        102877llu,
+        129607llu,
+        163307llu,
+        205759llu,
+        259229llu,
+        326617llu,
+        411527llu,
+        518509llu,
+        653267llu,
+        823117llu,
+        1037059llu,
+        1306601llu,
+        1646237llu,
+        2074129llu,
+        2613229llu,
+        3292489llu,
+        4148279llu,
+        5226491llu,
+        6584983llu,
+        8296553llu,
+        10453007llu,
+        13169977llu,
+        16593127llu,
+        20906033llu,
+        26339969llu,
+        33186281llu,
+        41812097llu,
+        52679969llu,
+        66372617llu,
+        83624237llu,
+        105359939llu,
+        132745199llu,
+        167248483llu,
+        210719881llu,
+        265490441llu,
+        334496971llu,
+        421439783llu,
+        530980861llu,
+        668993977llu,
+        842879579llu,
+        1061961721llu,
+        1337987929llu,
+        1685759167llu,
+        2123923447llu,
+        2675975881llu,
+        3371518343llu,
+        4247846927llu,
+        5351951779llu,
+        6743036717llu,
+        8495693897llu,
+        10703903591llu,
+        13486073473llu,
+        16991387857llu,
+        21407807219llu,
+        26972146961llu,
+        33982775741llu,
+        42815614441llu,
+        53944293929llu,
+        67965551447llu,
+        85631228929llu,
+        107888587883llu,
+        135931102921llu,
+        171262457903llu,
+        215777175787llu,
+        271862205833llu,
+        342524915839llu,
+        431554351609llu,
+        543724411781llu,
+        685049831731llu,
+        863108703229llu,
+        1087448823553llu,
+        1370099663459llu,
+        1726217406467llu,
+        2174897647073llu,
+        2740199326961llu,
+        3452434812973llu,
+        4349795294267llu,
+        5480398654009llu,
+        6904869625999llu,
+        8699590588571llu,
+        10960797308051llu,
+        13809739252051llu,
+        17399181177241llu,
+        21921594616111llu,
+        27619478504183llu,
+        34798362354533llu,
+        43843189232363llu,
+        55238957008387llu,
+        69596724709081llu,
+        87686378464759llu,
+        110477914016779llu,
+        139193449418173llu,
+        175372756929481llu,
+        220955828033581llu,
+        278386898836457llu,
+        350745513859007llu,
+        441911656067171llu,
+        556773797672909llu,
+        701491027718027llu,
+        883823312134381llu,
+        1113547595345903llu,
+        1402982055436147llu,
+        1767646624268779llu,
+        2227095190691797llu,
+        2805964110872297llu,
+        3535293248537579llu,
+        4454190381383713llu,
+        5611928221744609llu,
+        7070586497075177llu,
+        8908380762767489llu,
+        11223856443489329llu,
+        14141172994150357llu,
+        17816761525534927llu,
+        22447712886978529llu,
+        28282345988300791llu,
+        35633523051069991llu,
+        44895425773957261llu,
+        56564691976601587llu,
+        71267046102139967llu,
+        89790851547914507llu,
+        113129383953203213llu,
+        142534092204280003llu,
+        179581703095829107llu,
+        226258767906406483llu,
+        285068184408560057llu,
+        359163406191658253llu,
+        452517535812813007llu,
+        570136368817120201llu,
+        718326812383316683llu,
+        905035071625626043llu,
+        1140272737634240411llu,
+        1436653624766633509llu,
+        1810070143251252131llu,
+        2280545475268481167llu,
+        2873307249533267101llu,
+        3620140286502504283llu,
+        4561090950536962147llu,
+        5746614499066534157llu,
+        7240280573005008577llu,
+        9122181901073924329llu,
+        11493228998133068689llu,
+        14480561146010017169llu,
+        18446744073709551557llu};
+    // NOLINTNEXTLINE(*c-array*)
+    static constexpr uint64_t (*const mod_functions[])(uint64_t) = {
+        &mod0,
+        &mod2,
+        &mod3,
+        &mod5,
+        &mod7,
+        &mod11,
+        &mod13,
+        &mod17,
+        &mod23,
+        &mod29,
+        &mod37,
+        &mod47,
+        &mod59,
+        &mod73,
+        &mod97,
+        &mod127,
+        &mod151,
+        &mod197,
+        &mod251,
+        &mod313,
+        &mod397,
+        &mod499,
+        &mod631,
+        &mod797,
+        &mod1009,
+        &mod1259,
+        &mod1597,
+        &mod2011,
+        &mod2539,
+        &mod3203,
+        &mod4027,
+        &mod5087,
+        &mod6421,
+        &mod8089,
+        &mod10193,
+        &mod12853,
+        &mod16193,
+        &mod20399,
+        &mod25717,
+        &mod32401,
+        &mod40823,
+        &mod51437,
+        &mod64811,
+        &mod81649,
+        &mod102877,
+        &mod129607,
+        &mod163307,
+        &mod205759,
+        &mod259229,
+        &mod326617,
+        &mod411527,
+        &mod518509,
+        &mod653267,
+        &mod823117,
+        &mod1037059,
+        &mod1306601,
+        &mod1646237,
+        &mod2074129,
+        &mod2613229,
+        &mod3292489,
+        &mod4148279,
+        &mod5226491,
+        &mod6584983,
+        &mod8296553,
+        &mod10453007,
+        &mod13169977,
+        &mod16593127,
+        &mod20906033,
+        &mod26339969,
+        &mod33186281,
+        &mod41812097,
+        &mod52679969,
+        &mod66372617,
+        &mod83624237,
+        &mod105359939,
+        &mod132745199,
+        &mod167248483,
+        &mod210719881,
+        &mod265490441,
+        &mod334496971,
+        &mod421439783,
+        &mod530980861,
+        &mod668993977,
+        &mod842879579,
+        &mod1061961721,
+        &mod1337987929,
+        &mod1685759167,
+        &mod2123923447,
+        &mod2675975881,
+        &mod3371518343,
+        &mod4247846927,
+        &mod5351951779,
+        &mod6743036717,
+        &mod8495693897,
+        &mod10703903591,
+        &mod13486073473,
+        &mod16991387857,
+        &mod21407807219,
+        &mod26972146961,
+        &mod33982775741,
+        &mod42815614441,
+        &mod53944293929,
+        &mod67965551447,
+        &mod85631228929,
+        &mod107888587883,
+        &mod135931102921,
+        &mod171262457903,
+        &mod215777175787,
+        &mod271862205833,
+        &mod342524915839,
+        &mod431554351609,
+        &mod543724411781,
+        &mod685049831731,
+        &mod863108703229,
+        &mod1087448823553,
+        &mod1370099663459,
+        &mod1726217406467,
+        &mod2174897647073,
+        &mod2740199326961,
+        &mod3452434812973,
+        &mod4349795294267,
+        &mod5480398654009,
+        &mod6904869625999,
+        &mod8699590588571,
+        &mod10960797308051,
+        &mod13809739252051,
+        &mod17399181177241,
+        &mod21921594616111,
+        &mod27619478504183,
+        &mod34798362354533,
+        &mod43843189232363,
+        &mod55238957008387,
+        &mod69596724709081,
+        &mod87686378464759,
+        &mod110477914016779,
+        &mod139193449418173,
+        &mod175372756929481,
+        &mod220955828033581,
+        &mod278386898836457,
+        &mod350745513859007,
+        &mod441911656067171,
+        &mod556773797672909,
+        &mod701491027718027,
+        &mod883823312134381,
+        &mod1113547595345903,
+        &mod1402982055436147,
+        &mod1767646624268779,
+        &mod2227095190691797,
+        &mod2805964110872297,
+        &mod3535293248537579,
+        &mod4454190381383713,
+        &mod5611928221744609,
+        &mod7070586497075177,
+        &mod8908380762767489,
+        &mod11223856443489329,
+        &mod14141172994150357,
+        &mod17816761525534927,
+        &mod22447712886978529,
+        &mod28282345988300791,
+        &mod35633523051069991,
+        &mod44895425773957261,
+        &mod56564691976601587,
+        &mod71267046102139967,
+        &mod89790851547914507,
+        &mod113129383953203213,
+        &mod142534092204280003,
+        &mod179581703095829107,
+        &mod226258767906406483,
+        &mod285068184408560057,
+        &mod359163406191658253,
+        &mod452517535812813007,
+        &mod570136368817120201,
+        &mod718326812383316683,
+        &mod905035071625626043,
+        &mod1140272737634240411,
+        &mod1436653624766633509,
+        &mod1810070143251252131,
+        &mod2280545475268481167,
+        &mod2873307249533267101,
+        &mod3620140286502504283,
+        &mod4561090950536962147,
+        &mod5746614499066534157,
+        &mod7240280573005008577,
+        &mod9122181901073924329,
+        &mod11493228998133068689,
+        &mod14480561146010017169,
+        &mod18446744073709551557};
+    const uint64_t* found = std::lower_bound(
+        std::begin(prime_list), std::end(prime_list) - 1, size);
+    size = *found;
+    return mod_functions[1 + found - prime_list];
+  }
+  void commit(mod_function new_mod_function) {
+    current_mod_function = new_mod_function;
+  }
+  void reset() {
+    current_mod_function = &mod0;
+  }
+
+  uint64_t index_for_hash(uint64_t hash, uint64_t /*num_slots_minus_one*/)
+      const {
+    return current_mod_function(hash);
+  }
+  uint64_t keep_in_range(uint64_t index, uint64_t num_slots_minus_one) const {
+    return index > num_slots_minus_one ? current_mod_function(index) : index;
+  }
+
+ private:
+  mod_function current_mod_function = &mod0;
+};
+
+struct power_of_two_hash_policy {
+  uint64_t index_for_hash(uint64_t hash, uint64_t num_slots_minus_one) const {
+    return hash & num_slots_minus_one;
+  }
+  uint64_t keep_in_range(uint64_t index, uint64_t num_slots_minus_one) const {
+    return index_for_hash(index, num_slots_minus_one);
+  }
+  int8_t next_size_over(uint64_t& size) const {
+    size = detailv3::next_power_of_two(size);
+    return 0;
+  }
+  void commit(int8_t) {}
+  void reset() {}
+};
+
+struct fibonacci_hash_policy {
+  uint64_t index_for_hash(uint64_t hash, uint64_t /*num_slots_minus_one*/)
+      const {
+    return (11400714819323198485ull * hash) >> shift;
+  }
+  uint64_t keep_in_range(uint64_t index, uint64_t num_slots_minus_one) const {
+    return index & num_slots_minus_one;
+  }
+
+  int8_t next_size_over(uint64_t& size) const {
+    size = std::max(uint64_t(2), detailv3::next_power_of_two(size));
+    return static_cast<int8_t>(64 - detailv3::log2(size));
+  }
+  void commit(int8_t shift_) {
+    shift = shift_;
+  }
+  void reset() {
+    shift = 63;
+  }
+
+ private:
+  int8_t shift = 63;
+};
+
+template <
+    typename K,
+    typename V,
+    typename H = std::hash<K>,
+    typename E = std::equal_to<K>,
+    typename A = std::allocator<std::pair<K, V>>>
+class order_preserving_flat_hash_map
+    : public detailv3::sherwood_v3_table<
+          std::pair<K, V>,
+          K,
+          H,
+          detailv3::KeyOrValueHasher<K, std::pair<K, V>, H>,
+          E,
+          detailv3::KeyOrValueEquality<K, std::pair<K, V>, E>,
+          A,
+          typename std::allocator_traits<A>::template rebind_alloc<
+              detailv3::sherwood_v3_entry<std::pair<K, V>>>> {
+  using Table = detailv3::sherwood_v3_table<
+      std::pair<K, V>,
+      K,
+      H,
+      detailv3::KeyOrValueHasher<K, std::pair<K, V>, H>,
+      E,
+      detailv3::KeyOrValueEquality<K, std::pair<K, V>, E>,
+      A,
+      typename std::allocator_traits<A>::template rebind_alloc<
+          detailv3::sherwood_v3_entry<std::pair<K, V>>>>;
+
+ public:
+  using key_type = K;
+  using mapped_type = V;
+
+  using Table::Table;
+  order_preserving_flat_hash_map() = default;
+
+  inline V& operator[](const K& key) {
+    return emplace(key, convertible_to_value()).first->second;
+  }
+  inline V& operator[](K&& key) {
+    return emplace(std::move(key), convertible_to_value()).first->second;
+  }
+  V& at(const K& key) {
+    auto found = this->find(key);
+    if (found == this->end())
+      throw std::out_of_range("Argument passed to at() was not in the map.");
+    return found->second;
+  }
+  const V& at(const K& key) const {
+    auto found = this->find(key);
+    if (found == this->end())
+      throw std::out_of_range("Argument passed to at() was not in the map.");
+    return found->second;
+  }
+
+  using Table::emplace;
+  std::pair<typename Table::iterator, bool> emplace() {
+    return emplace(key_type(), convertible_to_value());
+  }
+  template <typename M>
+  std::pair<typename Table::iterator, bool> insert_or_assign(
+      const key_type& key,
+      M&& m) {
+    auto emplace_result = emplace(key, std::forward<M>(m));
+    if (!emplace_result.second)
+      emplace_result.first->second = std::forward<M>(m);
+    return emplace_result;
+  }
+  template <typename M>
+  std::pair<typename Table::iterator, bool> insert_or_assign(
+      key_type&& key,
+      M&& m) {
+    auto emplace_result = emplace(std::move(key), std::forward<M>(m));
+    if (!emplace_result.second)
+      emplace_result.first->second = std::forward<M>(m);
+    return emplace_result;
+  }
+  template <typename M>
+  typename Table::iterator insert_or_assign(
+      typename Table::const_iterator,
+      const key_type& key,
+      M&& m) {
+    return insert_or_assign(key, std::forward<M>(m)).first;
+  }
+  template <typename M>
+  typename Table::iterator insert_or_assign(
+      typename Table::const_iterator,
+      key_type&& key,
+      M&& m) {
+    return insert_or_assign(std::move(key), std::forward<M>(m)).first;
+  }
+
+  friend bool operator==(
+      const order_preserving_flat_hash_map& lhs,
+      const order_preserving_flat_hash_map& rhs) {
+    if (lhs.size() != rhs.size())
+      return false;
+    for (const typename Table::value_type& value : lhs) {
+      auto found = rhs.find(value.first);
+      if (found == rhs.end() || value.second != found->second)
+        return false;
+    }
+    return true;
+  }
+  friend bool operator!=(
+      const order_preserving_flat_hash_map& lhs,
+      const order_preserving_flat_hash_map& rhs) {
+    return !(lhs == rhs);
+  }
+
+ private:
+  struct convertible_to_value {
+    operator V() const {
+      return V();
+    }
+  };
+};
+
+template <
+    typename T,
+    typename H = std::hash<T>,
+    typename E = std::equal_to<T>,
+    typename A = std::allocator<T>>
+class flat_hash_set
+    : public detailv3::sherwood_v3_table<
+          T,
+          T,
+          H,
+          detailv3::functor_storage<uint64_t, H>,
+          E,
+          detailv3::functor_storage<bool, E>,
+          A,
+          typename std::allocator_traits<A>::template rebind_alloc<
+              detailv3::sherwood_v3_entry<T>>> {
+  using Table = detailv3::sherwood_v3_table<
+      T,
+      T,
+      H,
+      detailv3::functor_storage<uint64_t, H>,
+      E,
+      detailv3::functor_storage<bool, E>,
+      A,
+      typename std::allocator_traits<A>::template rebind_alloc<
+          detailv3::sherwood_v3_entry<T>>>;
+
+ public:
+  using key_type = T;
+
+  using Table::Table;
+  flat_hash_set() = default;
+
+  template <typename... Args>
+  std::pair<typename Table::iterator, bool> emplace(Args&&... args) {
+    return Table::emplace(T(std::forward<Args>(args)...));
+  }
+  std::pair<typename Table::iterator, bool> emplace(const key_type& arg) {
+    return Table::emplace(arg);
+  }
+  std::pair<typename Table::iterator, bool> emplace(key_type& arg) {
+    return Table::emplace(arg);
+  }
+  std::pair<typename Table::iterator, bool> emplace(const key_type&& arg) {
+    return Table::emplace(std::move(arg));
+  }
+  std::pair<typename Table::iterator, bool> emplace(key_type&& arg) {
+    return Table::emplace(std::move(arg));
+  }
+
+  friend bool operator==(const flat_hash_set& lhs, const flat_hash_set& rhs) {
+    if (lhs.size() != rhs.size())
+      return false;
+    for (const T& value : lhs) {
+      if (rhs.find(value) == rhs.end())
+        return false;
+    }
+    return true;
+  }
+  friend bool operator!=(const flat_hash_set& lhs, const flat_hash_set& rhs) {
+    return !(lhs == rhs);
+  }
+};
+
+template <typename T>
+struct power_of_two_std_hash : std::hash<T> {
+  typedef ska_ordered::power_of_two_hash_policy hash_policy;
+};
+
+} // namespace ska_ordered
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/overflows.h b/lib/python3.11/site-packages/torch/include/c10/util/overflows.h
new file mode 100644
index 0000000000000000000000000000000000000000..183a2f62a32218555d73e9130ae8ae4d62af2b9d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/overflows.h
@@ -0,0 +1,100 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/complex.h>
+
+#include <cmath>
+#include <limits>
+#include <type_traits>
+
+namespace c10 {
+// In some versions of MSVC, there will be a compiler error when building.
+// C4146: unary minus operator applied to unsigned type, result still unsigned
+// C4804: unsafe use of type 'bool' in operation
+// It can be addressed by disabling the following warning.
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4146)
+#pragma warning(disable : 4804)
+#pragma warning(disable : 4018)
+#endif
+
+// The overflow checks may involve float to int conversion which may
+// trigger precision loss warning. Re-enable the warning once the code
+// is fixed. See T58053069.
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+
+// bool can be converted to any type.
+// Without specializing on bool, in pytorch_linux_trusty_py2_7_9_build:
+// `error: comparison of constant '255' with boolean expression is always false`
+// for `f > limit::max()` below
+template <typename To, typename From>
+std::enable_if_t<std::is_same_v<From, bool>, bool> overflows(
+    From /*f*/,
+    bool strict_unsigned [[maybe_unused]] = false) {
+  return false;
+}
+
+// skip isnan and isinf check for integral types
+template <typename To, typename From>
+std::enable_if_t<std::is_integral_v<From> && !std::is_same_v<From, bool>, bool>
+overflows(From f, bool strict_unsigned = false) {
+  using limit = std::numeric_limits<typename scalar_value_type<To>::type>;
+  if constexpr (!limit::is_signed && std::numeric_limits<From>::is_signed) {
+    // allow for negative numbers to wrap using two's complement arithmetic.
+    // For example, with uint8, this allows for `a - b` to be treated as
+    // `a + 255 * b`.
+    if (!strict_unsigned) {
+      return greater_than_max<To>(f) ||
+          (c10::is_negative(f) &&
+           -static_cast<uint64_t>(f) > static_cast<uint64_t>(limit::max()));
+    }
+  }
+  return c10::less_than_lowest<To>(f) || greater_than_max<To>(f);
+}
+
+template <typename To, typename From>
+std::enable_if_t<std::is_floating_point_v<From>, bool> overflows(
+    From f,
+    bool strict_unsigned [[maybe_unused]] = false) {
+  using limit = std::numeric_limits<typename scalar_value_type<To>::type>;
+  if (limit::has_infinity && std::isinf(static_cast<double>(f))) {
+    return false;
+  }
+  if (!limit::has_quiet_NaN && (f != f)) {
+    return true;
+  }
+  return f < limit::lowest() || f > limit::max();
+}
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+template <typename To, typename From>
+std::enable_if_t<is_complex<From>::value, bool> overflows(
+    From f,
+    bool strict_unsigned = false) {
+  // casts from complex to real are considered to overflow if the
+  // imaginary component is non-zero
+  if (!is_complex<To>::value && f.imag() != 0) {
+    return true;
+  }
+  // Check for overflow componentwise
+  // (Technically, the imag overflow check is guaranteed to be false
+  // when !is_complex<To>, but any optimizer worth its salt will be
+  // able to figure it out.)
+  return overflows<
+             typename scalar_value_type<To>::type,
+             typename From::value_type>(f.real(), strict_unsigned) ||
+      overflows<
+             typename scalar_value_type<To>::type,
+             typename From::value_type>(f.imag(), strict_unsigned);
+}
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/overloaded.h b/lib/python3.11/site-packages/torch/include/c10/util/overloaded.h
new file mode 100644
index 0000000000000000000000000000000000000000..34ae9d63509c2c94fb546815f73fefd2d20f295b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/overloaded.h
@@ -0,0 +1,31 @@
+#pragma once
+
+#include <memory>
+namespace c10 {
+namespace detail {
+
+template <class... Ts>
+struct overloaded_t {};
+
+template <class T0>
+struct overloaded_t<T0> : T0 {
+  using T0::operator();
+  overloaded_t(T0 t0) : T0(std::move(t0)) {}
+};
+template <class T0, class... Ts>
+struct overloaded_t<T0, Ts...> : T0, overloaded_t<Ts...> {
+  using T0::operator();
+  using overloaded_t<Ts...>::operator();
+  overloaded_t(T0 t0, Ts... ts)
+      : T0(std::move(t0)), overloaded_t<Ts...>(std::move(ts)...) {}
+};
+
+} // namespace detail
+
+// Construct an overloaded callable combining multiple callables, e.g. lambdas
+template <class... Ts>
+detail::overloaded_t<Ts...> overloaded(Ts... ts) {
+  return {std::move(ts)...};
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/python_stub.h b/lib/python3.11/site-packages/torch/include/c10/util/python_stub.h
new file mode 100644
index 0000000000000000000000000000000000000000..336c530d2b1faa35d7c87399846bb834a84ad569
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/python_stub.h
@@ -0,0 +1,4 @@
+#pragma once
+
+struct _object;
+using PyObject = _object;
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/qint32.h b/lib/python3.11/site-packages/torch/include/c10/util/qint32.h
new file mode 100644
index 0000000000000000000000000000000000000000..d0a4867d62bbb115f6ce0a1d4521774146de97f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/qint32.h
@@ -0,0 +1,18 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * qint32 is for signed 32 bit quantized Tensors
+ */
+struct alignas(4) qint32 {
+  using underlying = int32_t;
+  int32_t val_;
+  qint32() = default;
+  C10_HOST_DEVICE explicit qint32(int32_t val) : val_(val) {}
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/qint8.h b/lib/python3.11/site-packages/torch/include/c10/util/qint8.h
new file mode 100644
index 0000000000000000000000000000000000000000..439aeec8aafb8ed989709d50e497466077c7fb46
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/qint8.h
@@ -0,0 +1,20 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * This is the data type for quantized Tensors. Right now we only have
+ * qint8 which is for 8 bit Tensors, and qint32 for 32 bit int Tensors,
+ * we might have 4 bit, 2 bit or 1 bit data types in the future.
+ */
+struct alignas(1) qint8 {
+  using underlying = int8_t;
+  int8_t val_;
+  qint8() = default;
+  C10_HOST_DEVICE explicit qint8(int8_t val) : val_(val) {}
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/quint2x4.h b/lib/python3.11/site-packages/torch/include/c10/util/quint2x4.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ebd97cf65550bdd708e68b064f2a8b09f7b7bf3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/quint2x4.h
@@ -0,0 +1,19 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * quint2x4 is for un-signed 2 bit quantized Tensors that are packed to byte
+ * boundary.
+ */
+struct alignas(1) quint2x4 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  quint2x4() = default;
+  C10_HOST_DEVICE explicit quint2x4(uint8_t val) : val_(val) {}
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/quint4x2.h b/lib/python3.11/site-packages/torch/include/c10/util/quint4x2.h
new file mode 100644
index 0000000000000000000000000000000000000000..0b792b3686711904bf29c33f563d42fcea498ddd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/quint4x2.h
@@ -0,0 +1,19 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * quint4x2 is for un-signed 4 bit quantized Tensors that are packed to byte
+ * boundary.
+ */
+struct alignas(1) quint4x2 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  quint4x2() = default;
+  C10_HOST_DEVICE explicit quint4x2(uint8_t val) : val_(val) {}
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/quint8.h b/lib/python3.11/site-packages/torch/include/c10/util/quint8.h
new file mode 100644
index 0000000000000000000000000000000000000000..3aeb09b6397c02e057c208bc8d1ac1add9271e31
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/quint8.h
@@ -0,0 +1,18 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * quint8 is for unsigned 8 bit quantized Tensors
+ */
+struct alignas(1) quint8 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  quint8() = default;
+  C10_HOST_DEVICE explicit quint8(uint8_t val) : val_(val) {}
+};
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/safe_numerics.h b/lib/python3.11/site-packages/torch/include/c10/util/safe_numerics.h
new file mode 100644
index 0000000000000000000000000000000000000000..e242cfaf8449a36cfd6df0f710e4f507705c0063
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/safe_numerics.h
@@ -0,0 +1,90 @@
+#pragma once
+#include <c10/macros/Macros.h>
+
+#include <cstdint>
+
+// GCC has __builtin_mul_overflow from before it supported __has_builtin
+#ifdef _MSC_VER
+#define C10_HAS_BUILTIN_OVERFLOW() (0)
+#include <c10/util/llvmMathExtras.h>
+#include <intrin.h>
+#else
+#define C10_HAS_BUILTIN_OVERFLOW() (1)
+#endif
+
+namespace c10 {
+
+C10_ALWAYS_INLINE bool add_overflows(uint64_t a, uint64_t b, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_add_overflow(a, b, out);
+#else
+  unsigned long long tmp;
+#if defined(_M_IX86) || defined(_M_X64)
+  auto carry = _addcarry_u64(0, a, b, &tmp);
+#else
+  tmp = a + b;
+  unsigned long long vector = (a & b) ^ ((a ^ b) & ~tmp);
+  auto carry = vector >> 63;
+#endif
+  *out = tmp;
+  return carry;
+#endif
+}
+
+C10_ALWAYS_INLINE bool mul_overflows(uint64_t a, uint64_t b, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_mul_overflow(a, b, out);
+#else
+  *out = a * b;
+  // This test isnt exact, but avoids doing integer division
+  return (
+      (c10::llvm::countLeadingZeros(a) + c10::llvm::countLeadingZeros(b)) < 64);
+#endif
+}
+
+C10_ALWAYS_INLINE bool mul_overflows(int64_t a, int64_t b, int64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  return __builtin_mul_overflow(a, b, out);
+#else
+  volatile int64_t tmp = a * b;
+  *out = tmp;
+  if (a == 0 || b == 0) {
+    return false;
+  }
+  return !(a == tmp / b);
+#endif
+}
+
+template <typename It>
+bool safe_multiplies_u64(It first, It last, uint64_t* out) {
+#if C10_HAS_BUILTIN_OVERFLOW()
+  uint64_t prod = 1;
+  bool overflow = false;
+  for (; first != last; ++first) {
+    overflow |= c10::mul_overflows(prod, *first, &prod);
+  }
+  *out = prod;
+  return overflow;
+#else
+  uint64_t prod = 1;
+  uint64_t prod_log2 = 0;
+  bool is_zero = false;
+  for (; first != last; ++first) {
+    auto x = static_cast<uint64_t>(*first);
+    prod *= x;
+    // log2(0) isn't valid, so need to track it specially
+    is_zero |= (x == 0);
+    prod_log2 += c10::llvm::Log2_64_Ceil(x);
+  }
+  *out = prod;
+  // This test isnt exact, but avoids doing integer division
+  return !is_zero && (prod_log2 >= 64);
+#endif
+}
+
+template <typename Container>
+bool safe_multiplies_u64(const Container& c, uint64_t* out) {
+  return safe_multiplies_u64(c.begin(), c.end(), out);
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/signal_handler.h b/lib/python3.11/site-packages/torch/include/c10/util/signal_handler.h
new file mode 100644
index 0000000000000000000000000000000000000000..09f587d3923a4dd6aeee6cad9ac551dabd6e9244
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/signal_handler.h
@@ -0,0 +1,119 @@
+#pragma once
+
+#include <atomic>
+#include <condition_variable>
+#include <csignal>
+#include <cstdint>
+#include <mutex>
+
+#include <c10/macros/Export.h>
+
+#if defined(__APPLE__)
+#define C10_SUPPORTS_SIGNAL_HANDLER
+#elif defined(__linux__) && !defined(C10_DISABLE_SIGNAL_HANDLERS)
+#define C10_SUPPORTS_FATAL_SIGNAL_HANDLERS
+#define C10_SUPPORTS_SIGNAL_HANDLER
+#endif
+
+#if defined(C10_SUPPORTS_FATAL_SIGNAL_HANDLERS)
+#include <pthread.h>
+#endif
+
+namespace c10 {
+
+class C10_API SignalHandler {
+ public:
+  enum class Action { NONE, STOP };
+
+  // Constructor. Specify what action to take when a signal is received.
+  SignalHandler(Action SIGINT_action, Action SIGHUP_action);
+
+  SignalHandler(const SignalHandler&) = delete;
+  SignalHandler(SignalHandler&&) = delete;
+  SignalHandler& operator=(const SignalHandler&) = delete;
+  SignalHandler& operator=(SignalHandler&&) = delete;
+  ~SignalHandler();
+
+  Action CheckForSignals();
+
+  bool GotSIGINT();
+  bool GotSIGHUP();
+
+  Action SIGINT_action_;
+  Action SIGHUP_action_;
+  std::atomic<uint64_t> my_sigint_count_;
+  std::atomic<uint64_t> my_sighup_count_;
+};
+
+#if defined(C10_SUPPORTS_FATAL_SIGNAL_HANDLERS)
+class C10_API FatalSignalHandler {
+  // This works by setting up certain fatal signal handlers. Previous fatal
+  // signal handlers will still be called when the signal is raised. Defaults
+  // to being off.
+ public:
+  C10_API void setPrintStackTracesOnFatalSignal(bool print);
+  C10_API bool printStackTracesOnFatalSignal();
+  static FatalSignalHandler& getInstance();
+  FatalSignalHandler(const FatalSignalHandler&) = delete;
+  FatalSignalHandler(FatalSignalHandler&&) = delete;
+  FatalSignalHandler& operator=(const FatalSignalHandler&) = delete;
+  FatalSignalHandler& operator=(FatalSignalHandler&&) = delete;
+  virtual ~FatalSignalHandler() = default;
+
+ protected:
+  explicit FatalSignalHandler();
+
+ private:
+  void installFatalSignalHandlers();
+  void uninstallFatalSignalHandlers();
+  static void fatalSignalHandlerStatic(int signum);
+  void fatalSignalHandler(int signum);
+  virtual void fatalSignalHandlerPostProcess();
+  struct sigaction* getPreviousSigaction(int signum);
+  const char* getSignalName(int signum);
+  void callPreviousSignalHandler(
+      struct sigaction* action,
+      int signum,
+      siginfo_t* info,
+      void* ctx);
+  void stacktraceSignalHandler(bool needsLock);
+  static void stacktraceSignalHandlerStatic(
+      int signum,
+      siginfo_t* info,
+      void* ctx);
+  void stacktraceSignalHandler(int signum, siginfo_t* info, void* ctx);
+
+  // The mutex protects the bool.
+  std::mutex fatalSignalHandlersInstallationMutex;
+  bool fatalSignalHandlersInstalled;
+  // We need to hold a reference to call the previous SIGUSR2 handler in case
+  // we didn't signal it
+  struct sigaction previousSigusr2 {};
+  // Flag dictating whether the SIGUSR2 handler falls back to previous handlers
+  // or is intercepted in order to print a stack trace.
+  std::atomic<bool> fatalSignalReceived;
+  // Global state set when a fatal signal is received so that backtracing
+  // threads know why they're printing a stacktrace.
+  const char* fatalSignalName;
+  int fatalSignum = -1;
+  // This wait condition is used to wait for other threads to finish writing
+  // their stack trace when in fatal sig handler (we can't use pthread_join
+  // because there's no way to convert from a tid to a pthread_t).
+  std::condition_variable writingCond;
+  std::mutex writingMutex;
+  // used to indicate if the other thread responded to the signal
+  bool signalReceived;
+
+  struct signal_handler {
+    const char* name;
+    int signum;
+    struct sigaction previous;
+  };
+
+  // NOLINTNEXTLINE(*c-arrays*)
+  static signal_handler kSignalHandlers[];
+};
+
+#endif // defined(C10_SUPPORTS_SIGNAL_HANDLER)
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/sparse_bitset.h b/lib/python3.11/site-packages/torch/include/c10/util/sparse_bitset.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8eb0df47f6aee7dccf632a36869d98fa7aa0663
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/sparse_bitset.h
@@ -0,0 +1,893 @@
+//===- llvm/ADT/SparseBitVector.h - Efficient Sparse BitVector --*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file defines the SparseBitVector class.  See the doxygen comment for
+// SparseBitVector for more details on the algorithm used.
+//
+//===----------------------------------------------------------------------===//
+
+#pragma once
+#include <c10/macros/Macros.h>
+#include <c10/util/llvmMathExtras.h>
+#include <array>
+#include <cassert>
+#include <climits>
+#include <iterator>
+#include <list>
+#include <ostream>
+
+namespace c10 {
+
+/// SparseBitVector is an implementation of a bitvector that is sparse by only
+/// storing the elements that have non-zero bits set.  In order to make this
+/// fast for the most common cases, SparseBitVector is implemented as a linked
+/// list of SparseBitVectorElements.  We maintain a pointer to the last
+/// SparseBitVectorElement accessed (in the form of a list iterator), in order
+/// to make multiple in-order test/set constant time after the first one is
+/// executed.  Note that using vectors to store SparseBitVectorElement's does
+/// not work out very well because it causes insertion in the middle to take
+/// enormous amounts of time with a large amount of bits.  Other structures that
+/// have better worst cases for insertion in the middle (various balanced trees,
+/// etc) do not perform as well in practice as a linked list with this iterator
+/// kept up to date.  They are also significantly more memory intensive.
+
+template <unsigned ElementSize = 128>
+struct SparseBitVectorElement {
+ public:
+  using BitWord = unsigned long;
+  using size_type = unsigned;
+  enum {
+    BITWORD_SIZE = sizeof(BitWord) * CHAR_BIT,
+    BITWORDS_PER_ELEMENT = (ElementSize + BITWORD_SIZE - 1) / BITWORD_SIZE,
+    BITS_PER_ELEMENT = ElementSize
+  };
+
+ private:
+  // Index of Element in terms of where first bit starts.
+  unsigned ElementIndex;
+  std::array<BitWord, BITWORDS_PER_ELEMENT> Bits{};
+
+  SparseBitVectorElement() : ElementIndex(~0U) {}
+
+ public:
+  explicit SparseBitVectorElement(unsigned Idx) : ElementIndex(Idx) {}
+
+  // Comparison.
+  bool operator==(const SparseBitVectorElement& RHS) const {
+    if (ElementIndex != RHS.ElementIndex)
+      return false;
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i)
+      if (Bits[i] != RHS.Bits[i])
+        return false;
+    return true;
+  }
+
+  bool operator!=(const SparseBitVectorElement& RHS) const {
+    return !(*this == RHS);
+  }
+
+  // Return the bits that make up word Idx in our element.
+  BitWord word(unsigned Idx) const {
+    assert(Idx < BITWORDS_PER_ELEMENT);
+    return Bits[Idx];
+  }
+
+  unsigned index() const {
+    return ElementIndex;
+  }
+
+  bool empty() const {
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i)
+      if (Bits[i])
+        return false;
+    return true;
+  }
+
+  void set(unsigned Idx) {
+    Bits[Idx / BITWORD_SIZE] |= 1L << (Idx % BITWORD_SIZE);
+  }
+
+  bool test_and_set(unsigned Idx) {
+    bool old = test(Idx);
+    if (!old) {
+      set(Idx);
+      return true;
+    }
+    return false;
+  }
+
+  void reset(unsigned Idx) {
+    Bits[Idx / BITWORD_SIZE] &= ~(1L << (Idx % BITWORD_SIZE));
+  }
+
+  bool test(unsigned Idx) const {
+    return Bits[Idx / BITWORD_SIZE] & (1L << (Idx % BITWORD_SIZE));
+  }
+
+  size_type count() const {
+    unsigned NumBits = 0;
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i)
+      NumBits += llvm::countPopulation(Bits[i]);
+    return NumBits;
+  }
+
+  /// find_first - Returns the index of the first set bit.
+  int find_first() const {
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i)
+      if (Bits[i] != 0)
+        return i * BITWORD_SIZE + llvm::countTrailingZeros(Bits[i]);
+    throw std::runtime_error("Illegal empty element");
+  }
+
+  /// find_last - Returns the index of the last set bit.
+  int find_last() const {
+    for (unsigned I = 0; I < BITWORDS_PER_ELEMENT; ++I) {
+      unsigned Idx = BITWORDS_PER_ELEMENT - I - 1;
+      if (Bits[Idx] != 0)
+        return Idx * BITWORD_SIZE + BITWORD_SIZE -
+            llvm::countLeadingZeros(Bits[Idx]);
+    }
+    throw std::runtime_error("Illegal empty element");
+  }
+
+  /// find_next - Returns the index of the next set bit starting from the
+  /// "Curr" bit. Returns -1 if the next set bit is not found.
+  int find_next(unsigned Curr) const {
+    if (Curr >= BITS_PER_ELEMENT)
+      return -1;
+
+    unsigned WordPos = Curr / BITWORD_SIZE;
+    unsigned BitPos = Curr % BITWORD_SIZE;
+    BitWord Copy = Bits[WordPos];
+    assert(
+        WordPos <= BITWORDS_PER_ELEMENT && "Word Position outside of element");
+
+    // Mask off previous bits.
+    Copy &= ~0UL << BitPos;
+
+    if (Copy != 0)
+      return WordPos * BITWORD_SIZE + llvm::countTrailingZeros(Copy);
+
+    // Check subsequent words.
+    for (unsigned i = WordPos + 1; i < BITWORDS_PER_ELEMENT; ++i)
+      if (Bits[i] != 0)
+        return i * BITWORD_SIZE + llvm::countTrailingZeros(Bits[i]);
+    return -1;
+  }
+
+  // Union this element with RHS and return true if this one changed.
+  bool unionWith(const SparseBitVectorElement& RHS) {
+    bool changed = false;
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i) {
+      BitWord old = changed ? 0 : Bits[i];
+
+      Bits[i] |= RHS.Bits[i];
+      if (!changed && old != Bits[i])
+        changed = true;
+    }
+    return changed;
+  }
+
+  // Return true if we have any bits in common with RHS
+  bool intersects(const SparseBitVectorElement& RHS) const {
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i) {
+      if (RHS.Bits[i] & Bits[i])
+        return true;
+    }
+    return false;
+  }
+
+  // Intersect this Element with RHS and return true if this one changed.
+  // BecameZero is set to true if this element became all-zero bits.
+  bool intersectWith(const SparseBitVectorElement& RHS, bool& BecameZero) {
+    bool changed = false;
+    bool allzero = true;
+
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i) {
+      BitWord old = changed ? 0 : Bits[i];
+
+      Bits[i] &= RHS.Bits[i];
+      if (Bits[i] != 0)
+        allzero = false;
+
+      if (!changed && old != Bits[i])
+        changed = true;
+    }
+    BecameZero = allzero;
+    return changed;
+  }
+
+  // Intersect this Element with the complement of RHS and return true if this
+  // one changed.  BecameZero is set to true if this element became all-zero
+  // bits.
+  bool intersectWithComplement(
+      const SparseBitVectorElement& RHS,
+      bool& BecameZero) {
+    bool changed = false;
+    bool allzero = true;
+
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i) {
+      BitWord old = changed ? 0 : Bits[i];
+
+      Bits[i] &= ~RHS.Bits[i];
+      if (Bits[i] != 0)
+        allzero = false;
+
+      if (!changed && old != Bits[i])
+        changed = true;
+    }
+    BecameZero = allzero;
+    return changed;
+  }
+
+  // Three argument version of intersectWithComplement that intersects
+  // RHS1 & ~RHS2 into this element
+  void intersectWithComplement(
+      const SparseBitVectorElement& RHS1,
+      const SparseBitVectorElement& RHS2,
+      bool& BecameZero) {
+    bool allzero = true;
+
+    for (unsigned i = 0; i < BITWORDS_PER_ELEMENT; ++i) {
+      Bits[i] = RHS1.Bits[i] & ~RHS2.Bits[i];
+      if (Bits[i] != 0)
+        allzero = false;
+    }
+    BecameZero = allzero;
+  }
+};
+
+template <unsigned ElementSize = 128>
+class SparseBitVector {
+  using ElementList = std::list<SparseBitVectorElement<ElementSize>>;
+  using ElementListIter = typename ElementList::iterator;
+  using ElementListConstIter = typename ElementList::const_iterator;
+  enum { BITWORD_SIZE = SparseBitVectorElement<ElementSize>::BITWORD_SIZE };
+
+  ElementList Elements;
+  // Pointer to our current Element. This has no visible effect on the external
+  // state of a SparseBitVector, it's just used to improve performance in the
+  // common case of testing/modifying bits with similar indices.
+  mutable ElementListIter CurrElementIter;
+
+  // This is like std::lower_bound, except we do linear searching from the
+  // current position.
+  ElementListIter FindLowerBoundImpl(unsigned ElementIndex) const {
+    // We cache a non-const iterator so we're forced to resort to const_cast to
+    // get the begin/end in the case where 'this' is const. To avoid duplication
+    // of code with the only difference being whether the const cast is present
+    // 'this' is always const in this particular function and we sort out the
+    // difference in FindLowerBound and FindLowerBoundConst.
+    ElementListIter Begin =
+        // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+        const_cast<SparseBitVector<ElementSize>*>(this)->Elements.begin();
+    ElementListIter End =
+        // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+        const_cast<SparseBitVector<ElementSize>*>(this)->Elements.end();
+
+    if (Elements.empty()) {
+      CurrElementIter = Begin;
+      return CurrElementIter;
+    }
+
+    // Make sure our current iterator is valid.
+    if (CurrElementIter == End)
+      --CurrElementIter;
+
+    // Search from our current iterator, either backwards or forwards,
+    // depending on what element we are looking for.
+    ElementListIter ElementIter = CurrElementIter;
+    if (CurrElementIter->index() == ElementIndex) {
+      return ElementIter;
+    } else if (CurrElementIter->index() > ElementIndex) {
+      while (ElementIter != Begin && ElementIter->index() > ElementIndex)
+        --ElementIter;
+    } else {
+      while (ElementIter != End && ElementIter->index() < ElementIndex)
+        ++ElementIter;
+    }
+    CurrElementIter = ElementIter;
+    return ElementIter;
+  }
+  ElementListConstIter FindLowerBoundConst(unsigned ElementIndex) const {
+    return FindLowerBoundImpl(ElementIndex);
+  }
+  ElementListIter FindLowerBound(unsigned ElementIndex) {
+    return FindLowerBoundImpl(ElementIndex);
+  }
+
+  // Iterator to walk set bits in the bitmap.  This iterator is a lot uglier
+  // than it would be, in order to be efficient.
+  class SparseBitVectorIterator {
+   private:
+    bool AtEnd{false};
+
+    const SparseBitVector<ElementSize>* BitVector = nullptr;
+
+    // Current element inside of bitmap.
+    ElementListConstIter Iter;
+
+    // Current bit number inside of our bitmap.
+    unsigned BitNumber{0};
+
+    // Current word number inside of our element.
+    unsigned WordNumber{0};
+
+    // Current bits from the element.
+    typename SparseBitVectorElement<ElementSize>::BitWord Bits{0};
+
+    // Move our iterator to the first non-zero bit in the bitmap.
+    void AdvanceToFirstNonZero() {
+      if (AtEnd)
+        return;
+      if (BitVector->Elements.empty()) {
+        AtEnd = true;
+        return;
+      }
+      Iter = BitVector->Elements.begin();
+      BitNumber = Iter->index() * ElementSize;
+      unsigned BitPos = Iter->find_first();
+      BitNumber += BitPos;
+      WordNumber = (BitNumber % ElementSize) / BITWORD_SIZE;
+      Bits = Iter->word(WordNumber);
+      Bits >>= BitPos % BITWORD_SIZE;
+    }
+
+    // Move our iterator to the next non-zero bit.
+    void AdvanceToNextNonZero() {
+      if (AtEnd)
+        return;
+
+      while (Bits && !(Bits & 1)) {
+        Bits >>= 1;
+        BitNumber += 1;
+      }
+
+      // See if we ran out of Bits in this word.
+      if (!Bits) {
+        int NextSetBitNumber = Iter->find_next(BitNumber % ElementSize);
+        // If we ran out of set bits in this element, move to next element.
+        if (NextSetBitNumber == -1 || (BitNumber % ElementSize == 0)) {
+          ++Iter;
+          WordNumber = 0;
+
+          // We may run out of elements in the bitmap.
+          if (Iter == BitVector->Elements.end()) {
+            AtEnd = true;
+            return;
+          }
+          // Set up for next non-zero word in bitmap.
+          BitNumber = Iter->index() * ElementSize;
+          NextSetBitNumber = Iter->find_first();
+          BitNumber += NextSetBitNumber;
+          WordNumber = (BitNumber % ElementSize) / BITWORD_SIZE;
+          Bits = Iter->word(WordNumber);
+          Bits >>= NextSetBitNumber % BITWORD_SIZE;
+        } else {
+          WordNumber = (NextSetBitNumber % ElementSize) / BITWORD_SIZE;
+          Bits = Iter->word(WordNumber);
+          Bits >>= NextSetBitNumber % BITWORD_SIZE;
+          BitNumber = Iter->index() * ElementSize;
+          BitNumber += NextSetBitNumber;
+        }
+      }
+    }
+
+   public:
+    SparseBitVectorIterator() = default;
+
+    SparseBitVectorIterator(
+        const SparseBitVector<ElementSize>* RHS,
+        bool end = false)
+        : AtEnd(end),
+          BitVector(RHS),
+          Iter(BitVector->Elements.begin()),
+          WordNumber(~0) {
+      AdvanceToFirstNonZero();
+    }
+
+    // Preincrement.
+    inline SparseBitVectorIterator& operator++() {
+      ++BitNumber;
+      Bits >>= 1;
+      AdvanceToNextNonZero();
+      return *this;
+    }
+
+    // Postincrement.
+    inline SparseBitVectorIterator operator++(int) {
+      SparseBitVectorIterator tmp = *this;
+      ++*this;
+      return tmp;
+    }
+
+    // Return the current set bit number.
+    unsigned operator*() const {
+      return BitNumber;
+    }
+
+    bool operator==(const SparseBitVectorIterator& RHS) const {
+      // If they are both at the end, ignore the rest of the fields.
+      if (AtEnd && RHS.AtEnd)
+        return true;
+      // Otherwise they are the same if they have the same bit number and
+      // bitmap.
+      return AtEnd == RHS.AtEnd && RHS.BitNumber == BitNumber;
+    }
+
+    bool operator!=(const SparseBitVectorIterator& RHS) const {
+      return !(*this == RHS);
+    }
+  };
+
+ public:
+  using iterator = SparseBitVectorIterator;
+
+  SparseBitVector() : Elements(), CurrElementIter(Elements.begin()) {}
+
+  SparseBitVector(const SparseBitVector& RHS)
+      : Elements(RHS.Elements), CurrElementIter(Elements.begin()) {}
+  SparseBitVector(SparseBitVector&& RHS) noexcept
+      : Elements(std::move(RHS.Elements)), CurrElementIter(Elements.begin()) {}
+  ~SparseBitVector() = default;
+
+  // Clear.
+  void clear() {
+    Elements.clear();
+  }
+
+  // Assignment
+  SparseBitVector& operator=(const SparseBitVector& RHS) {
+    if (this == &RHS)
+      return *this;
+
+    Elements = RHS.Elements;
+    CurrElementIter = Elements.begin();
+    return *this;
+  }
+  SparseBitVector& operator=(SparseBitVector&& RHS) noexcept {
+    Elements = std::move(RHS.Elements);
+    CurrElementIter = Elements.begin();
+    return *this;
+  }
+
+  // Test, Reset, and Set a bit in the bitmap.
+  bool test(unsigned Idx) const {
+    if (Elements.empty())
+      return false;
+
+    unsigned ElementIndex = Idx / ElementSize;
+    ElementListConstIter ElementIter = FindLowerBoundConst(ElementIndex);
+
+    // If we can't find an element that is supposed to contain this bit, there
+    // is nothing more to do.
+    if (ElementIter == Elements.end() || ElementIter->index() != ElementIndex)
+      return false;
+    return ElementIter->test(Idx % ElementSize);
+  }
+
+  void reset(unsigned Idx) {
+    if (Elements.empty())
+      return;
+
+    unsigned ElementIndex = Idx / ElementSize;
+    ElementListIter ElementIter = FindLowerBound(ElementIndex);
+
+    // If we can't find an element that is supposed to contain this bit, there
+    // is nothing more to do.
+    if (ElementIter == Elements.end() || ElementIter->index() != ElementIndex)
+      return;
+    ElementIter->reset(Idx % ElementSize);
+
+    // When the element is zeroed out, delete it.
+    if (ElementIter->empty()) {
+      ++CurrElementIter;
+      Elements.erase(ElementIter);
+    }
+  }
+
+  void set(unsigned Idx) {
+    unsigned ElementIndex = Idx / ElementSize;
+    ElementListIter ElementIter;
+    if (Elements.empty()) {
+      ElementIter = Elements.emplace(Elements.end(), ElementIndex);
+    } else {
+      ElementIter = FindLowerBound(ElementIndex);
+
+      if (ElementIter == Elements.end() ||
+          ElementIter->index() != ElementIndex) {
+        // We may have hit the beginning of our SparseBitVector, in which case,
+        // we may need to insert right after this element, which requires moving
+        // the current iterator forward one, because insert does insert before.
+        if (ElementIter != Elements.end() &&
+            ElementIter->index() < ElementIndex)
+          ++ElementIter;
+        ElementIter = Elements.emplace(ElementIter, ElementIndex);
+      }
+    }
+    CurrElementIter = ElementIter;
+
+    ElementIter->set(Idx % ElementSize);
+  }
+
+  bool test_and_set(unsigned Idx) {
+    bool old = test(Idx);
+    if (!old) {
+      set(Idx);
+      return true;
+    }
+    return false;
+  }
+
+  bool operator!=(const SparseBitVector& RHS) const {
+    return !(*this == RHS);
+  }
+
+  bool operator==(const SparseBitVector& RHS) const {
+    ElementListConstIter Iter1 = Elements.begin();
+    ElementListConstIter Iter2 = RHS.Elements.begin();
+
+    for (; Iter1 != Elements.end() && Iter2 != RHS.Elements.end();
+         ++Iter1, ++Iter2) {
+      if (*Iter1 != *Iter2)
+        return false;
+    }
+    return Iter1 == Elements.end() && Iter2 == RHS.Elements.end();
+  }
+
+  // Union our bitmap with the RHS and return true if we changed.
+  bool operator|=(const SparseBitVector& RHS) {
+    if (this == &RHS)
+      return false;
+
+    if (empty()) {
+      *this = RHS;
+      return true;
+    }
+
+    bool changed = false;
+    ElementListIter Iter1 = Elements.begin();
+    ElementListConstIter Iter2 = RHS.Elements.begin();
+
+    // If RHS is empty, we are done
+    if (RHS.Elements.empty())
+      return false;
+
+    while (Iter2 != RHS.Elements.end()) {
+      if (Iter1 == Elements.end() || Iter1->index() > Iter2->index()) {
+        Elements.insert(Iter1, *Iter2);
+        ++Iter2;
+        changed = true;
+      } else if (Iter1->index() == Iter2->index()) {
+        changed |= Iter1->unionWith(*Iter2);
+        ++Iter1;
+        ++Iter2;
+      } else {
+        ++Iter1;
+      }
+    }
+    CurrElementIter = Elements.begin();
+    return changed;
+  }
+
+  // Intersect our bitmap with the RHS and return true if ours changed.
+  bool operator-=(const SparseBitVector& RHS) {
+    return intersectWithComplement(RHS);
+  }
+
+  // Intersect our bitmap with the RHS and return true if ours changed.
+  bool operator&=(const SparseBitVector& RHS) {
+    if (this == &RHS)
+      return false;
+
+    bool changed = false;
+    ElementListIter Iter1 = Elements.begin();
+    ElementListConstIter Iter2 = RHS.Elements.begin();
+
+    // Check if both bitmaps are empty.
+    if (Elements.empty() && RHS.Elements.empty())
+      return false;
+
+    // Loop through, intersecting as we go, erasing elements when necessary.
+    while (Iter2 != RHS.Elements.end()) {
+      if (Iter1 == Elements.end()) {
+        CurrElementIter = Elements.begin();
+        return changed;
+      }
+
+      if (Iter1->index() > Iter2->index()) {
+        ++Iter2;
+      } else if (Iter1->index() == Iter2->index()) {
+        bool BecameZero = false;
+        changed |= Iter1->intersectWith(*Iter2, BecameZero);
+        if (BecameZero) {
+          ElementListIter IterTmp = Iter1;
+          ++Iter1;
+          Elements.erase(IterTmp);
+        } else {
+          ++Iter1;
+        }
+        ++Iter2;
+      } else {
+        ElementListIter IterTmp = Iter1;
+        ++Iter1;
+        Elements.erase(IterTmp);
+        changed = true;
+      }
+    }
+    if (Iter1 != Elements.end()) {
+      Elements.erase(Iter1, Elements.end());
+      changed = true;
+    }
+    CurrElementIter = Elements.begin();
+    return changed;
+  }
+
+  // Intersect our bitmap with the complement of the RHS and return true
+  // if ours changed.
+  bool intersectWithComplement(const SparseBitVector& RHS) {
+    if (this == &RHS) {
+      if (!empty()) {
+        clear();
+        return true;
+      }
+      return false;
+    }
+
+    bool changed = false;
+    ElementListIter Iter1 = Elements.begin();
+    ElementListConstIter Iter2 = RHS.Elements.begin();
+
+    // If either our bitmap or RHS is empty, we are done
+    if (Elements.empty() || RHS.Elements.empty())
+      return false;
+
+    // Loop through, intersecting as we go, erasing elements when necessary.
+    while (Iter2 != RHS.Elements.end()) {
+      if (Iter1 == Elements.end()) {
+        CurrElementIter = Elements.begin();
+        return changed;
+      }
+
+      if (Iter1->index() > Iter2->index()) {
+        ++Iter2;
+      } else if (Iter1->index() == Iter2->index()) {
+        bool BecameZero = false;
+        changed |= Iter1->intersectWithComplement(*Iter2, BecameZero);
+        if (BecameZero) {
+          ElementListIter IterTmp = Iter1;
+          ++Iter1;
+          Elements.erase(IterTmp);
+        } else {
+          ++Iter1;
+        }
+        ++Iter2;
+      } else {
+        ++Iter1;
+      }
+    }
+    CurrElementIter = Elements.begin();
+    return changed;
+  }
+
+  bool intersectWithComplement(const SparseBitVector<ElementSize>* RHS) const {
+    return intersectWithComplement(*RHS);
+  }
+
+  //  Three argument version of intersectWithComplement.
+  //  Result of RHS1 & ~RHS2 is stored into this bitmap.
+  void intersectWithComplement(
+      const SparseBitVector<ElementSize>& RHS1,
+      const SparseBitVector<ElementSize>& RHS2) {
+    if (this == &RHS1) {
+      intersectWithComplement(RHS2);
+      return;
+    } else if (this == &RHS2) {
+      SparseBitVector RHS2Copy(RHS2);
+      intersectWithComplement(RHS1, RHS2Copy);
+      return;
+    }
+
+    Elements.clear();
+    CurrElementIter = Elements.begin();
+    ElementListConstIter Iter1 = RHS1.Elements.begin();
+    ElementListConstIter Iter2 = RHS2.Elements.begin();
+
+    // If RHS1 is empty, we are done
+    // If RHS2 is empty, we still have to copy RHS1
+    if (RHS1.Elements.empty())
+      return;
+
+    // Loop through, intersecting as we go, erasing elements when necessary.
+    while (Iter2 != RHS2.Elements.end()) {
+      if (Iter1 == RHS1.Elements.end())
+        return;
+
+      if (Iter1->index() > Iter2->index()) {
+        ++Iter2;
+      } else if (Iter1->index() == Iter2->index()) {
+        bool BecameZero = false;
+        Elements.emplace_back(Iter1->index());
+        Elements.back().intersectWithComplement(*Iter1, *Iter2, BecameZero);
+        if (BecameZero)
+          Elements.pop_back();
+        ++Iter1;
+        ++Iter2;
+      } else {
+        Elements.push_back(*Iter1++);
+      }
+    }
+
+    // copy the remaining elements
+    std::copy(Iter1, RHS1.Elements.end(), std::back_inserter(Elements));
+  }
+
+  void intersectWithComplement(
+      const SparseBitVector<ElementSize>* RHS1,
+      const SparseBitVector<ElementSize>* RHS2) {
+    intersectWithComplement(*RHS1, *RHS2);
+  }
+
+  bool intersects(const SparseBitVector<ElementSize>* RHS) const {
+    return intersects(*RHS);
+  }
+
+  // Return true if we share any bits in common with RHS
+  bool intersects(const SparseBitVector<ElementSize>& RHS) const {
+    ElementListConstIter Iter1 = Elements.begin();
+    ElementListConstIter Iter2 = RHS.Elements.begin();
+
+    // Check if both bitmaps are empty.
+    if (Elements.empty() && RHS.Elements.empty())
+      return false;
+
+    // Loop through, intersecting stopping when we hit bits in common.
+    while (Iter2 != RHS.Elements.end()) {
+      if (Iter1 == Elements.end())
+        return false;
+
+      if (Iter1->index() > Iter2->index()) {
+        ++Iter2;
+      } else if (Iter1->index() == Iter2->index()) {
+        if (Iter1->intersects(*Iter2))
+          return true;
+        ++Iter1;
+        ++Iter2;
+      } else {
+        ++Iter1;
+      }
+    }
+    return false;
+  }
+
+  // Return true iff all bits set in this SparseBitVector are
+  // also set in RHS.
+  bool contains(const SparseBitVector<ElementSize>& RHS) const {
+    SparseBitVector<ElementSize> Result(*this);
+    Result &= RHS;
+    return (Result == RHS);
+  }
+
+  // Return the first set bit in the bitmap.  Return -1 if no bits are set.
+  int find_first() const {
+    if (Elements.empty())
+      return -1;
+    const SparseBitVectorElement<ElementSize>& First = *(Elements.begin());
+    return (First.index() * ElementSize) + First.find_first();
+  }
+
+  // Return the last set bit in the bitmap.  Return -1 if no bits are set.
+  int find_last() const {
+    if (Elements.empty())
+      return -1;
+    const SparseBitVectorElement<ElementSize>& Last = *(Elements.rbegin());
+    return (Last.index() * ElementSize) + Last.find_last();
+  }
+
+  // Return true if the SparseBitVector is empty
+  bool empty() const {
+    return Elements.empty();
+  }
+
+  unsigned count() const {
+    unsigned BitCount = 0;
+    for (ElementListConstIter Iter = Elements.begin(); Iter != Elements.end();
+         ++Iter)
+      BitCount += Iter->count();
+
+    return BitCount;
+  }
+
+  iterator begin() const {
+    return iterator(this);
+  }
+
+  iterator end() const {
+    return iterator(this, true);
+  }
+};
+
+// Convenience functions to allow Or and And without dereferencing in the user
+// code.
+
+template <unsigned ElementSize>
+inline bool operator|=(
+    SparseBitVector<ElementSize>& LHS,
+    const SparseBitVector<ElementSize>* RHS) {
+  return LHS |= *RHS;
+}
+
+template <unsigned ElementSize>
+inline bool operator|=(
+    SparseBitVector<ElementSize>* LHS,
+    const SparseBitVector<ElementSize>& RHS) {
+  return LHS->operator|=(RHS);
+}
+
+template <unsigned ElementSize>
+inline bool operator&=(
+    SparseBitVector<ElementSize>* LHS,
+    const SparseBitVector<ElementSize>& RHS) {
+  return LHS->operator&=(RHS);
+}
+
+template <unsigned ElementSize>
+inline bool operator&=(
+    SparseBitVector<ElementSize>& LHS,
+    const SparseBitVector<ElementSize>* RHS) {
+  return LHS &= *RHS;
+}
+
+// Convenience functions for infix union, intersection, difference operators.
+
+template <unsigned ElementSize>
+inline SparseBitVector<ElementSize> operator|(
+    const SparseBitVector<ElementSize>& LHS,
+    const SparseBitVector<ElementSize>& RHS) {
+  SparseBitVector<ElementSize> Result(LHS);
+  Result |= RHS;
+  return Result;
+}
+
+template <unsigned ElementSize>
+inline SparseBitVector<ElementSize> operator&(
+    const SparseBitVector<ElementSize>& LHS,
+    const SparseBitVector<ElementSize>& RHS) {
+  SparseBitVector<ElementSize> Result(LHS);
+  Result &= RHS;
+  return Result;
+}
+
+template <unsigned ElementSize>
+inline SparseBitVector<ElementSize> operator-(
+    const SparseBitVector<ElementSize>& LHS,
+    const SparseBitVector<ElementSize>& RHS) {
+  SparseBitVector<ElementSize> Result;
+  Result.intersectWithComplement(LHS, RHS);
+  return Result;
+}
+
+template <unsigned ElementSize>
+std::ostream& operator<<(
+    std::ostream& stream,
+    const SparseBitVector<ElementSize>& vec) {
+  bool first = true;
+  stream << "{";
+  for (auto el : vec) {
+    if (first) {
+      first = false;
+    } else {
+      stream << ", ";
+    }
+    stream << el;
+  }
+  stream << "}";
+  return stream;
+}
+
+} // end namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/ssize.h b/lib/python3.11/site-packages/torch/include/c10/util/ssize.h
new file mode 100644
index 0000000000000000000000000000000000000000..0067f813bbec6adccb8e7839f51755907110de67
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/ssize.h
@@ -0,0 +1,46 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <c10/util/TypeSafeSignMath.h>
+
+#include <cstddef>
+#include <type_traits>
+
+namespace c10 {
+
+// Implementations of std::ssize() from C++ 20.
+//
+// This is useful in particular for avoiding -Werror=sign-compare
+// issues.
+//
+// Use this with argument-dependent lookup, e.g.:
+// use c10::ssize;
+// auto size = ssize(container);
+//
+// As with the standard library version, containers are permitted to
+// specialize this with a free function defined in the same namespace.
+//
+// See https://en.cppreference.com/w/cpp/iterator/size for more
+// information as well as the source of our implementations.
+//
+// We augment the implementation by adding an assert() if an overflow
+// would occur.
+
+template <typename C>
+constexpr auto ssize(const C& c) -> std::
+    common_type_t<std::ptrdiff_t, std::make_signed_t<decltype(c.size())>> {
+  using R = std::
+      common_type_t<std::ptrdiff_t, std::make_signed_t<decltype(c.size())>>;
+  // We expect this to be exceedingly rare to fire and don't wish to
+  // pay a performance hit in release mode.
+  TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!greater_than_max<R>(c.size()));
+  return static_cast<R>(c.size());
+}
+
+template <typename T, std::ptrdiff_t N>
+// NOLINTNEXTLINE(*-c-arrays)
+constexpr auto ssize(const T (&array)[N]) noexcept -> std::ptrdiff_t {
+  return N;
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint.h b/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dee97306d14326ba99072feb08b80badda77352
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint.h
@@ -0,0 +1,34 @@
+#pragma once
+
+#if defined(__ELF__) && (defined(__x86_64__) || defined(__i386__)) && \
+    !(defined(TORCH_DISABLE_SDT) && TORCH_DISABLE_SDT)
+
+#define TORCH_HAVE_SDT 1
+
+#include <c10/util/static_tracepoint_elfx86.h>
+
+#define TORCH_SDT(name, ...) \
+  TORCH_SDT_PROBE_N(         \
+      pytorch, name, 0, TORCH_SDT_NARG(0, ##__VA_ARGS__), ##__VA_ARGS__)
+// Use TORCH_SDT_DEFINE_SEMAPHORE(name) to define the semaphore
+// as global variable before using the TORCH_SDT_WITH_SEMAPHORE macro
+#define TORCH_SDT_WITH_SEMAPHORE(name, ...) \
+  TORCH_SDT_PROBE_N(                        \
+      pytorch, name, 1, TORCH_SDT_NARG(0, ##__VA_ARGS__), ##__VA_ARGS__)
+#define TORCH_SDT_IS_ENABLED(name) (TORCH_SDT_SEMAPHORE(pytorch, name) > 0)
+
+#else
+
+#define TORCH_HAVE_SDT 0
+
+#define TORCH_SDT(name, ...) \
+  do {                       \
+  } while (0)
+#define TORCH_SDT_WITH_SEMAPHORE(name, ...) \
+  do {                                      \
+  } while (0)
+#define TORCH_SDT_IS_ENABLED(name) (false)
+#define TORCH_SDT_DEFINE_SEMAPHORE(name)
+#define TORCH_SDT_DECLARE_SEMAPHORE(name)
+
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint_elfx86.h b/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint_elfx86.h
new file mode 100644
index 0000000000000000000000000000000000000000..b84ea5fe5cfdc3b079ee95f1d122e1e8790719f0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/static_tracepoint_elfx86.h
@@ -0,0 +1,144 @@
+#pragma once
+
+// clang-format off
+
+// Default constraint for the probe arguments as operands.
+#ifndef TORCH_SDT_ARG_CONSTRAINT
+#define TORCH_SDT_ARG_CONSTRAINT      "nor"
+#endif
+
+// Instruction to emit for the probe.
+#define TORCH_SDT_NOP                 nop
+
+// Note section properties.
+#define TORCH_SDT_NOTE_NAME           "stapsdt"
+#define TORCH_SDT_NOTE_TYPE           3
+
+// Semaphore variables are put in this section
+#define TORCH_SDT_SEMAPHORE_SECTION   ".probes"
+
+// Size of address depending on platform.
+#ifdef __LP64__
+#define TORCH_SDT_ASM_ADDR            .8byte
+#else
+#define TORCH_SDT_ASM_ADDR            .4byte
+#endif
+
+// Assembler helper Macros.
+#define TORCH_SDT_S(x)                #x
+#define TORCH_SDT_ASM_1(x)            TORCH_SDT_S(x) "\n"
+#define TORCH_SDT_ASM_2(a, b)         TORCH_SDT_S(a) "," TORCH_SDT_S(b) "\n"
+#define TORCH_SDT_ASM_3(a, b, c)      TORCH_SDT_S(a) "," TORCH_SDT_S(b) ","    \
+                                      TORCH_SDT_S(c) "\n"
+#define TORCH_SDT_ASM_STRING(x)       TORCH_SDT_ASM_1(.asciz TORCH_SDT_S(x))
+
+// Helper to determine the size of an argument.
+#define TORCH_SDT_IS_ARRAY_POINTER(x)  ((__builtin_classify_type(x) == 14) ||  \
+                                        (__builtin_classify_type(x) == 5))
+#define TORCH_SDT_ARGSIZE(x)  (TORCH_SDT_IS_ARRAY_POINTER(x)                   \
+                               ? sizeof(void*)                                 \
+                               : sizeof(x))
+
+// Format of each probe arguments as operand.
+// Size of the argument tagged with TORCH_SDT_Sn, with "n" constraint.
+// Value of the argument tagged with TORCH_SDT_An, with configured constraint.
+#define TORCH_SDT_ARG(n, x)                                                    \
+  [TORCH_SDT_S##n] "n"                ((size_t)TORCH_SDT_ARGSIZE(x)),          \
+  [TORCH_SDT_A##n] TORCH_SDT_ARG_CONSTRAINT (x)
+
+// Templates to append arguments as operands.
+#define TORCH_SDT_OPERANDS_0()        [__sdt_dummy] "g" (0)
+#define TORCH_SDT_OPERANDS_1(_1)      TORCH_SDT_ARG(1, _1)
+#define TORCH_SDT_OPERANDS_2(_1, _2)                                           \
+  TORCH_SDT_OPERANDS_1(_1), TORCH_SDT_ARG(2, _2)
+#define TORCH_SDT_OPERANDS_3(_1, _2, _3)                                       \
+  TORCH_SDT_OPERANDS_2(_1, _2), TORCH_SDT_ARG(3, _3)
+#define TORCH_SDT_OPERANDS_4(_1, _2, _3, _4)                                   \
+  TORCH_SDT_OPERANDS_3(_1, _2, _3), TORCH_SDT_ARG(4, _4)
+#define TORCH_SDT_OPERANDS_5(_1, _2, _3, _4, _5)                               \
+  TORCH_SDT_OPERANDS_4(_1, _2, _3, _4), TORCH_SDT_ARG(5, _5)
+#define TORCH_SDT_OPERANDS_6(_1, _2, _3, _4, _5, _6)                           \
+  TORCH_SDT_OPERANDS_5(_1, _2, _3, _4, _5), TORCH_SDT_ARG(6, _6)
+#define TORCH_SDT_OPERANDS_7(_1, _2, _3, _4, _5, _6, _7)                       \
+  TORCH_SDT_OPERANDS_6(_1, _2, _3, _4, _5, _6), TORCH_SDT_ARG(7, _7)
+#define TORCH_SDT_OPERANDS_8(_1, _2, _3, _4, _5, _6, _7, _8)                   \
+  TORCH_SDT_OPERANDS_7(_1, _2, _3, _4, _5, _6, _7), TORCH_SDT_ARG(8, _8)
+#define TORCH_SDT_OPERANDS_9(_1, _2, _3, _4, _5, _6, _7, _8, _9)               \
+  TORCH_SDT_OPERANDS_8(_1, _2, _3, _4, _5, _6, _7, _8), TORCH_SDT_ARG(9, _9)
+
+// Templates to reference the arguments from operands in note section.
+#define TORCH_SDT_ARGFMT(no)        %n[TORCH_SDT_S##no]@%[TORCH_SDT_A##no]
+#define TORCH_SDT_ARG_TEMPLATE_0    /*No arguments*/
+#define TORCH_SDT_ARG_TEMPLATE_1    TORCH_SDT_ARGFMT(1)
+#define TORCH_SDT_ARG_TEMPLATE_2    TORCH_SDT_ARG_TEMPLATE_1 TORCH_SDT_ARGFMT(2)
+#define TORCH_SDT_ARG_TEMPLATE_3    TORCH_SDT_ARG_TEMPLATE_2 TORCH_SDT_ARGFMT(3)
+#define TORCH_SDT_ARG_TEMPLATE_4    TORCH_SDT_ARG_TEMPLATE_3 TORCH_SDT_ARGFMT(4)
+#define TORCH_SDT_ARG_TEMPLATE_5    TORCH_SDT_ARG_TEMPLATE_4 TORCH_SDT_ARGFMT(5)
+#define TORCH_SDT_ARG_TEMPLATE_6    TORCH_SDT_ARG_TEMPLATE_5 TORCH_SDT_ARGFMT(6)
+#define TORCH_SDT_ARG_TEMPLATE_7    TORCH_SDT_ARG_TEMPLATE_6 TORCH_SDT_ARGFMT(7)
+#define TORCH_SDT_ARG_TEMPLATE_8    TORCH_SDT_ARG_TEMPLATE_7 TORCH_SDT_ARGFMT(8)
+#define TORCH_SDT_ARG_TEMPLATE_9    TORCH_SDT_ARG_TEMPLATE_8 TORCH_SDT_ARGFMT(9)
+
+// Resolvable by name macros
+// An attribute that marks a function or variable as needing to be resolvable
+// by name. This generally is needed if inline assembly refers to the variable
+// by string name.
+#ifdef __roar__
+#define TORCH_NAME_RESOLVABLE __attribute__((roar_resolvable_by_name))
+#else
+#define TORCH_NAME_RESOLVABLE
+#endif
+
+// Semaphore define, declare and probe note format
+
+#define TORCH_SDT_SEMAPHORE(provider, name)                                    \
+  torch_sdt_semaphore_##provider##_##name
+
+#define TORCH_SDT_DEFINE_SEMAPHORE(name)                                       \
+  extern "C" {                                                                 \
+    TORCH_NAME_RESOLVABLE                                                      \
+    volatile unsigned short TORCH_SDT_SEMAPHORE(pytorch, name)                 \
+    __attribute__((section(TORCH_SDT_SEMAPHORE_SECTION), used)) = 0;           \
+  }
+
+#define TORCH_SDT_DECLARE_SEMAPHORE(name)                                      \
+  extern "C" TORCH_NAME_RESOLVABLE volatile unsigned short                     \
+    TORCH_SDT_SEMAPHORE(pytorch, name)
+
+#define TORCH_SDT_SEMAPHORE_NOTE_0(provider, name)                             \
+  TORCH_SDT_ASM_1(     TORCH_SDT_ASM_ADDR 0) /*No Semaphore*/                  \
+
+#define TORCH_SDT_SEMAPHORE_NOTE_1(provider, name)                             \
+  TORCH_SDT_ASM_1(TORCH_SDT_ASM_ADDR TORCH_SDT_SEMAPHORE(provider, name))
+
+// Structure of note section for the probe.
+#define TORCH_SDT_NOTE_CONTENT(provider, name, has_semaphore, arg_template)    \
+  TORCH_SDT_ASM_1(990: TORCH_SDT_NOP)                                          \
+  TORCH_SDT_ASM_3(     .pushsection .note.stapsdt,"","note")                   \
+  TORCH_SDT_ASM_1(     .balign 4)                                              \
+  TORCH_SDT_ASM_3(     .4byte 992f-991f, 994f-993f, TORCH_SDT_NOTE_TYPE)       \
+  TORCH_SDT_ASM_1(991: .asciz TORCH_SDT_NOTE_NAME)                             \
+  TORCH_SDT_ASM_1(992: .balign 4)                                              \
+  TORCH_SDT_ASM_1(993: TORCH_SDT_ASM_ADDR 990b)                                \
+  TORCH_SDT_ASM_1(     TORCH_SDT_ASM_ADDR 0) /*Reserved for Base Address*/     \
+  TORCH_SDT_SEMAPHORE_NOTE_##has_semaphore(provider, name)                     \
+  TORCH_SDT_ASM_STRING(provider)                                               \
+  TORCH_SDT_ASM_STRING(name)                                                   \
+  TORCH_SDT_ASM_STRING(arg_template)                                           \
+  TORCH_SDT_ASM_1(994: .balign 4)                                              \
+  TORCH_SDT_ASM_1(     .popsection)
+
+// Main probe Macro.
+#define TORCH_SDT_PROBE(provider, name, has_semaphore, n, arglist)             \
+    __asm__ __volatile__ (                                                     \
+      TORCH_SDT_NOTE_CONTENT(                                                  \
+        provider, name, has_semaphore, TORCH_SDT_ARG_TEMPLATE_##n)             \
+      :: TORCH_SDT_OPERANDS_##n arglist                                        \
+    )                                                                          \
+
+// Helper Macros to handle variadic arguments.
+#define TORCH_SDT_NARG_(_0, _1, _2, _3, _4, _5, _6, _7, _8, _9, N, ...) N
+#define TORCH_SDT_NARG(...)                                                    \
+  TORCH_SDT_NARG_(__VA_ARGS__, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)
+#define TORCH_SDT_PROBE_N(provider, name, has_semaphore, N, ...)               \
+  TORCH_SDT_PROBE(provider, name, has_semaphore, N, (__VA_ARGS__))
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/strides.h b/lib/python3.11/site-packages/torch/include/c10/util/strides.h
new file mode 100644
index 0000000000000000000000000000000000000000..d3d38fd7d011383988e773a5423340b1d32c115f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/strides.h
@@ -0,0 +1,24 @@
+#pragma once
+#include <c10/util/ArrayRef.h>
+#include <c10/util/DimVector.h>
+#include <algorithm>
+
+namespace c10 {
+
+// Computes the contiguous strides of a tensor, given its sizes.
+inline DimVector contiguous_strides(const IntArrayRef sizes) {
+  using Int = IntArrayRef::value_type;
+  const Int dims = static_cast<Int>(sizes.size());
+
+  // With this initialisation we get the case dim == 0 or 1 right
+  DimVector strides(dims, 1);
+
+  for (auto i = dims - 2; i >= 0; --i) {
+    // Strides can't be 0 even if sizes are 0.
+    strides[i] = strides[i + 1] * std::max(sizes[i + 1], Int{1});
+  }
+
+  return strides;
+}
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/string_utils.h b/lib/python3.11/site-packages/torch/include/c10/util/string_utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..61b5df3801559dcdfe7fc9b3d83c863bb2684ac5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/string_utils.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include <string>
+
+#if !defined(FBCODE_CAFFE2) && !defined(C10_NO_DEPRECATED)
+
+namespace c10 {
+
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::stod;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::stoi;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::stoll;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::stoull;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::to_string;
+
+} // namespace c10
+
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/string_view.h b/lib/python3.11/site-packages/torch/include/c10/util/string_view.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae38c55656eacacaf3bb02e0ab67c4c394ad6b29
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/string_view.h
@@ -0,0 +1,647 @@
+#pragma once
+
+#include <algorithm>
+#include <cstddef>
+#include <cstring>
+#include <functional>
+#include <iterator>
+#include <limits>
+#include <ostream>
+#include <stdexcept>
+#include <string>
+#include <string_view>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * Port of std::string_view with methods from C++20.
+ * Implemented following the interface definition in
+ * https://en.cppreference.com/w/cpp/string/basic_string_view
+ * See there for the API documentation.
+ *
+ * Difference: We don't have a Traits template parameter because
+ * std::char_traits isn't constexpr and we'd have to reimplement
+ * std::char_traits if we wanted to use it with our constexpr basic_string_view.
+ */
+template <class CharT>
+// NOLINTNEXTLINE(cppcoreguidelines-special-member-functions)
+class basic_string_view final {
+ public:
+  using value_type = CharT;
+  using pointer = CharT*;
+  using const_pointer = const CharT*;
+  using reference = CharT&;
+  using const_reference = const CharT&;
+  using const_iterator = const CharT*;
+  using iterator = const_iterator;
+  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+  using reverse_iterator = const_reverse_iterator;
+  using size_type = std::size_t;
+  using difference_type = std::ptrdiff_t;
+
+  static constexpr size_type npos = size_type(-1);
+
+  constexpr basic_string_view() noexcept : begin_(nullptr) {}
+
+  explicit constexpr basic_string_view(const_pointer str, size_type count)
+      : begin_(str), size_(count) {}
+
+  /* implicit */ constexpr basic_string_view(const_pointer str)
+      : basic_string_view(str, strlen_(str)) {}
+
+  /* implicit */ basic_string_view(const ::std::basic_string<CharT>& str)
+      : basic_string_view(str.data(), str.size()) {}
+
+  /* implicit */ constexpr basic_string_view(
+      const ::std::basic_string_view<CharT>& str)
+      : basic_string_view(str.data(), str.size()) {}
+
+  constexpr basic_string_view(const basic_string_view&) noexcept = default;
+
+  constexpr basic_string_view& operator=(
+      const basic_string_view& rhs) noexcept {
+    begin_ = rhs.begin_;
+    size_ = rhs.size_;
+    return *this;
+  }
+
+  constexpr operator ::std::basic_string_view<CharT>() const {
+    return ::std::basic_string_view<CharT>(data(), size());
+  }
+
+  explicit operator ::std::basic_string<CharT>() const {
+    return ::std::basic_string<CharT>(data(), size());
+  }
+
+  constexpr const_iterator begin() const noexcept {
+    return cbegin();
+  }
+
+  constexpr const_iterator cbegin() const noexcept {
+    return begin_;
+  }
+
+  constexpr const_iterator end() const noexcept {
+    return cend();
+  }
+
+  constexpr const_iterator cend() const noexcept {
+    return begin_ + size_;
+  }
+
+  constexpr const_reverse_iterator rbegin() const noexcept {
+    return crbegin();
+  }
+
+  constexpr const_reverse_iterator crbegin() const noexcept {
+    return const_reverse_iterator(this->end());
+  }
+
+  constexpr const_reverse_iterator rend() const noexcept {
+    return crend();
+  }
+
+  constexpr const_reverse_iterator crend() const noexcept {
+    return const_reverse_iterator(this->begin());
+  }
+
+  friend constexpr const_iterator begin(basic_string_view sv) noexcept {
+    return sv.begin();
+  }
+
+  friend constexpr const_iterator end(basic_string_view sv) noexcept {
+    return sv.end();
+  }
+
+  constexpr const_reference operator[](size_type pos) const {
+    // TODO: split out
+    return at_(pos);
+  }
+
+  constexpr const_reference at(size_type pos) const {
+#if !defined( \
+    __CUDA_ARCH__) // CUDA doesn't like std::out_of_range in device code
+    return C10_UNLIKELY(pos >= size_)
+        ? (throw std::out_of_range(
+               "string_view::operator[] or string_view::at() out of range. Index: " +
+               std::to_string(pos) + ", size: " + std::to_string(size())),
+           at_(0))
+        : at_(pos);
+#else
+    return at_(pos);
+#endif
+  }
+
+  constexpr const_reference front() const {
+    return *begin_;
+  }
+
+  constexpr const_reference back() const {
+    return *(begin_ + size_ - 1);
+  }
+
+  constexpr const_pointer data() const noexcept {
+    return begin_;
+  }
+
+  constexpr size_type size() const noexcept {
+    return size_;
+  }
+
+  constexpr size_type length() const noexcept {
+    return size();
+  }
+
+  constexpr size_type max_size() const noexcept {
+    return std::numeric_limits<difference_type>::max();
+  }
+
+  [[nodiscard]] constexpr bool empty() const noexcept {
+    return size() == 0;
+  }
+
+  constexpr void remove_prefix(size_type n) {
+    if (n > size()) {
+      throw std::out_of_range(
+          "basic_string_view::remove_prefix: out of range. PrefixLength: " +
+          std::to_string(n) + ", size: " + std::to_string(size()));
+    }
+    begin_ += n;
+    size_ -= n;
+  }
+
+  constexpr void remove_suffix(size_type n) {
+    if (n > size()) {
+      throw std::out_of_range(
+          "basic_string_view::remove_suffix: out of range. SuffixLength: " +
+          std::to_string(n) + ", size: " + std::to_string(size()));
+    }
+    size_ -= n;
+  }
+
+  constexpr void swap(basic_string_view& sv) noexcept {
+    auto tmp = *this;
+    *this = sv;
+    sv = tmp;
+  }
+
+  size_type copy(pointer dest, size_type count, size_type pos = 0) const {
+    if (pos > size_) {
+      throw std::out_of_range(
+          "basic_string_view::copy: out of range. Index: " +
+          std::to_string(pos) + ", size: " + std::to_string(size()));
+    }
+    size_type copy_length = std::min(count, size_ - pos);
+    for (auto iter = begin() + pos, end = iter + copy_length; iter != end;) {
+      *(dest++) = *(iter++);
+    }
+    return copy_length;
+  }
+
+  constexpr basic_string_view substr(size_type pos = 0, size_type count = npos)
+      const {
+#if !defined( \
+    __CUDA_ARCH__) // CUDA doesn't like std::out_of_range in device code
+    return (pos > size_)
+        ? (throw std::out_of_range(
+               "basic_string_view::substr parameter out of bounds. Index: " +
+               std::to_string(pos) + ", size: " + std::to_string(size())),
+           substr_())
+        : substr_(pos, count);
+#else
+    return substr_(pos, count);
+#endif
+  }
+
+  constexpr int compare(basic_string_view rhs) const noexcept {
+    // Write it iteratively. This is faster.
+    for (size_t i = 0, end = std::min(size(), rhs.size()); i < end; ++i) {
+      if (at_(i) < rhs.at_(i)) {
+        return -1;
+      } else if (at_(i) > rhs.at_(i)) {
+        return 1;
+      }
+    }
+    if (size() < rhs.size()) {
+      return -1;
+    } else if (size() > rhs.size()) {
+      return 1;
+    }
+    return 0;
+  }
+
+  constexpr int compare(size_type pos1, size_type count1, basic_string_view v)
+      const {
+    return substr(pos1, count1).compare(v);
+  }
+
+  constexpr int compare(
+      size_type pos1,
+      size_type count1,
+      basic_string_view v,
+      size_type pos2,
+      size_type count2) const {
+    return substr(pos1, count1).compare(v.substr(pos2, count2));
+  }
+
+  constexpr int compare(const_pointer s) const {
+    return compare(basic_string_view(s));
+  }
+
+  constexpr int compare(size_type pos1, size_type count1, const_pointer s)
+      const {
+    return substr(pos1, count1).compare(basic_string_view(s));
+  }
+
+  constexpr int compare(
+      size_type pos1,
+      size_type count1,
+      const_pointer s,
+      size_type count2) const {
+    return substr(pos1, count1).compare(basic_string_view(s, count2));
+  }
+
+  friend constexpr bool operator==(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return lhs.equals_(rhs);
+  }
+
+  friend constexpr bool operator!=(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return !(lhs == rhs);
+  }
+
+  friend constexpr bool operator<(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return lhs.compare(rhs) < 0;
+  }
+
+  friend constexpr bool operator>=(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return !(lhs < rhs);
+  }
+
+  friend constexpr bool operator>(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return rhs < lhs;
+  }
+
+  friend constexpr bool operator<=(
+      basic_string_view lhs,
+      basic_string_view rhs) noexcept {
+    return !(lhs > rhs);
+  }
+
+  constexpr bool starts_with(basic_string_view prefix) const noexcept {
+    return (prefix.size() > size()) ? false
+                                    : prefix.equals_(substr_(0, prefix.size()));
+  }
+
+  constexpr bool starts_with(CharT prefix) const noexcept {
+    return !empty() && prefix == front();
+  }
+
+  constexpr bool starts_with(const_pointer prefix) const {
+    return starts_with(basic_string_view(prefix));
+  }
+
+  constexpr bool ends_with(basic_string_view suffix) const noexcept {
+    return (suffix.size() > size())
+        ? false
+        : suffix.equals_(substr_(size() - suffix.size(), suffix.size()));
+  }
+
+  constexpr bool ends_with(CharT suffix) const noexcept {
+    return !empty() && suffix == back();
+  }
+
+  constexpr bool ends_with(const_pointer suffix) const {
+    return ends_with(basic_string_view(suffix));
+  }
+
+  constexpr size_type find(basic_string_view v, size_type pos = 0)
+      const noexcept {
+    if (v.size() == 0) {
+      return pos <= size() ? pos : npos;
+    }
+
+    if (pos + v.size() <= size()) {
+      for (size_type cur = pos, end = size() - v.size(); cur <= end; ++cur) {
+        if (v.at_(0) == at_(cur) &&
+            v.substr_(1).equals_(substr_(cur + 1, v.size() - 1))) {
+          return cur;
+        }
+      }
+    }
+    return npos;
+  }
+
+  constexpr size_type find(CharT ch, size_type pos = 0) const noexcept {
+    return find_first_if_(pos, charIsEqual_{ch});
+  }
+
+  constexpr size_type find(const_pointer s, size_type pos, size_type count)
+      const {
+    return find(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type find(const_pointer s, size_type pos = 0) const {
+    return find(basic_string_view(s), pos);
+  }
+
+  constexpr size_type rfind(basic_string_view v, size_type pos = npos)
+      const noexcept {
+    // Write it iteratively. This is faster.
+    if (v.size() == 0) {
+      return pos <= size() ? pos : size();
+    }
+
+    if (v.size() <= size()) {
+      pos = std::min(size() - v.size(), pos);
+      do {
+        if (v.at_(0) == at_(pos) &&
+            v.substr_(1).equals_(substr_(pos + 1, v.size() - 1))) {
+          return pos;
+        }
+      } while (pos-- > 0);
+    }
+    return npos;
+  }
+
+  constexpr size_type rfind(CharT ch, size_type pos = npos) const noexcept {
+    return find_last_if_(pos, charIsEqual_{ch});
+  }
+
+  constexpr size_type rfind(const_pointer s, size_type pos, size_type count)
+      const {
+    return rfind(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type rfind(const_pointer s, size_type pos = npos) const {
+    return rfind(basic_string_view(s), pos);
+  }
+
+  constexpr size_type find_first_of(basic_string_view v, size_type pos = 0)
+      const noexcept {
+    return find_first_if_(pos, stringViewContainsChar_{v});
+  }
+
+  constexpr size_type find_first_of(CharT ch, size_type pos = 0)
+      const noexcept {
+    return find_first_if_(pos, charIsEqual_{ch});
+  }
+
+  constexpr size_type find_first_of(
+      const_pointer s,
+      size_type pos,
+      size_type count) const {
+    return find_first_of(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type find_first_of(const_pointer s, size_type pos = 0) const {
+    return find_first_of(basic_string_view(s), pos);
+  }
+
+  constexpr size_type find_last_of(basic_string_view v, size_type pos = npos)
+      const noexcept {
+    return find_last_if_(pos, stringViewContainsChar_{v});
+  }
+
+  constexpr size_type find_last_of(CharT ch, size_type pos = npos)
+      const noexcept {
+    return find_last_if_(pos, charIsEqual_{ch});
+  }
+
+  constexpr size_type find_last_of(
+      const_pointer s,
+      size_type pos,
+      size_type count) const {
+    return find_last_of(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type find_last_of(const_pointer s, size_type pos = npos)
+      const {
+    return find_last_of(basic_string_view(s), pos);
+  }
+
+  constexpr size_type find_first_not_of(basic_string_view v, size_type pos = 0)
+      const noexcept {
+    return find_first_if_(pos, stringViewDoesNotContainChar_{v});
+  }
+
+  constexpr size_type find_first_not_of(CharT ch, size_type pos = 0)
+      const noexcept {
+    return find_first_if_(pos, charIsNotEqual_{ch});
+  }
+
+  constexpr size_type find_first_not_of(
+      const_pointer s,
+      size_type pos,
+      size_type count) const {
+    return find_first_not_of(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type find_first_not_of(const_pointer s, size_type pos = 0)
+      const {
+    return find_first_not_of(basic_string_view(s), pos);
+  }
+
+  constexpr size_type find_last_not_of(
+      basic_string_view v,
+      size_type pos = npos) const noexcept {
+    return find_last_if_(pos, stringViewDoesNotContainChar_{v});
+  }
+
+  constexpr size_type find_last_not_of(CharT ch, size_type pos = npos)
+      const noexcept {
+    return find_last_if_(pos, charIsNotEqual_{ch});
+  }
+
+  constexpr size_type find_last_not_of(
+      const_pointer s,
+      size_type pos,
+      size_type count) const {
+    return find_last_not_of(basic_string_view(s, count), pos);
+  }
+
+  constexpr size_type find_last_not_of(const_pointer s, size_type pos = npos)
+      const {
+    return find_last_not_of(basic_string_view(s), pos);
+  }
+
+ private:
+  static constexpr size_type strlen_(const_pointer str) noexcept {
+    const_pointer current = str;
+    while (*current != '\0') {
+      ++current;
+    }
+    return current - str;
+  }
+
+  constexpr const_reference at_(size_type pos) const noexcept {
+    return *(begin_ + pos);
+  }
+
+  constexpr basic_string_view substr_(size_type pos = 0, size_type count = npos)
+      const {
+    return basic_string_view{begin_ + pos, std::min(count, size() - pos)};
+  }
+
+  template <class Condition>
+  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+  constexpr size_type find_first_if_(size_type pos, Condition&& condition)
+      const noexcept {
+    if (pos + 1 <= size()) {
+      for (size_type cur = pos; cur < size(); ++cur) {
+        if (condition(at_(cur))) {
+          return cur;
+        }
+      }
+    }
+    return npos;
+  }
+
+  template <class Condition>
+  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+  constexpr size_type find_last_if_(size_type pos, Condition&& condition)
+      const noexcept {
+    // Write it iteratively. This is faster.
+    if (size() > 0) {
+      pos = std::min(size() - 1, pos);
+      do {
+        if (condition(at_(pos))) {
+          return pos;
+        }
+      } while (pos-- > 0);
+    }
+    return npos;
+  }
+
+  constexpr bool equals_(basic_string_view rhs) const {
+    // We don't use string_view::compare() here but implement it manually
+    // because only looking at equality allows for more optimized code.
+#if defined(__GNUC__) && !defined(__CUDACC__)
+    return size() == rhs.size() &&
+        0 == __builtin_memcmp(data(), rhs.data(), size());
+#else
+    if (size() != rhs.size()) {
+      return false;
+    }
+    // Yes, memcmp would be laster than this loop, but memcmp isn't constexpr
+    // and I didn't feel like implementing a constexpr memcmp variant.
+    // TODO At some point this should probably be done, including tricks
+    // like comparing one machine word instead of a byte per iteration.
+    for (typename basic_string_view<CharT>::size_type pos = 0; pos < size();
+         ++pos) {
+      if (at_(pos) != rhs.at_(pos)) {
+        return false;
+      }
+    }
+    return true;
+#endif
+  }
+
+  struct charIsEqual_ final {
+    CharT expected;
+    constexpr bool operator()(CharT actual) const noexcept {
+      return expected == actual;
+    }
+  };
+
+  struct charIsNotEqual_ final {
+    CharT expected;
+    constexpr bool operator()(CharT actual) const noexcept {
+      return expected != actual;
+    }
+  };
+
+  struct stringViewContainsChar_ final {
+    basic_string_view expected;
+    constexpr bool operator()(CharT ch) const noexcept {
+      return npos != expected.find(ch);
+    }
+  };
+
+  struct stringViewDoesNotContainChar_ final {
+    basic_string_view expected;
+    constexpr bool operator()(CharT ch) const noexcept {
+      return npos == expected.find(ch);
+    }
+  };
+
+  const_pointer begin_;
+  size_type size_{};
+};
+
+template <class CharT>
+inline std::basic_ostream<CharT>& operator<<(
+    std::basic_ostream<CharT>& stream,
+    basic_string_view<CharT> sv) {
+  // The rules for operator<< are quite complex, so lets defer to the
+  // STL implementation.
+  using std_string_type = ::std::basic_string_view<CharT>;
+  return stream << std_string_type(sv.data(), sv.size());
+}
+
+template <class CharT>
+constexpr inline void swap(
+    basic_string_view<CharT>& lhs,
+    basic_string_view<CharT>& rhs) noexcept {
+  lhs.swap(rhs);
+}
+using string_view = std::string_view;
+using c10_string_view = basic_string_view<char>;
+
+// NOTE: In C++20, this function should be replaced by string_view.starts_with
+constexpr bool starts_with(
+    const std::string_view s,
+    const std::string_view prefix) noexcept {
+  return (prefix.size() > s.size()) ? false
+                                    : prefix == s.substr(0, prefix.size());
+}
+
+// NOTE: In C++20, this function should be replaced by string_view.starts_with
+constexpr bool starts_with(
+    const std::string_view s,
+    const char prefix) noexcept {
+  return !s.empty() && prefix == s.front();
+}
+
+// NOTE: In C++20, this function should be replaced by string_view.ends_with
+constexpr bool ends_with(
+    const std::string_view s,
+    const std::string_view suffix) noexcept {
+  return (suffix.size() > s.size())
+      ? false
+      : suffix == s.substr(s.size() - suffix.size(), suffix.size());
+}
+
+// NOTE: In C++20, this function should be replaced by string_view.ends_with
+constexpr bool ends_with(const std::string_view s, const char prefix) noexcept {
+  return !s.empty() && prefix == s.back();
+}
+
+} // namespace c10
+
+namespace std {
+template <class CharT>
+struct hash<::c10::basic_string_view<CharT>> {
+  size_t operator()(::c10::basic_string_view<CharT> x) const {
+    // The standard says that std::string_view hashing must do the same as
+    // std::string hashing but leaves the details of std::string hashing
+    // up to the implementer. So, to be conformant, we need to re-use and
+    // existing STL type's hash function. The std::string fallback is probably
+    // slow but the only way to be conformant.
+
+    using std_string_type = ::std::basic_string_view<CharT>;
+    return ::std::hash<std_string_type>{}(std_string_type(x.data(), x.size()));
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/strong_type.h b/lib/python3.11/site-packages/torch/include/c10/util/strong_type.h
new file mode 100644
index 0000000000000000000000000000000000000000..1399c27c7d186525f1c550f3a01b0d8c45357e40
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/strong_type.h
@@ -0,0 +1,1670 @@
+/*
+ * strong_type C++14/17/20 strong typedef library
+ *
+ * Copyright (C) Björn Fahller
+ *
+ *  Use, modification and distribution is subject to the
+ *  Boost Software License, Version 1.0. (See accompanying
+ *  file LICENSE_1_0.txt or copy at
+ *  http://www.boost.org/LICENSE_1_0.txt)
+ *
+ * Project home: https://github.com/rollbear/strong_type
+ */
+
+#ifndef ROLLBEAR_STRONG_TYPE_HPP_INCLUDED
+#define ROLLBEAR_STRONG_TYPE_HPP_INCLUDED
+
+#include <functional>
+#include <istream>
+#include <ostream>
+#include <type_traits>
+#include <utility>
+
+#ifndef STRONG_HAS_STD_FORMAT
+#define STRONG_HAS_STD_FORMAT 0
+#endif
+
+#ifndef STRONG_HAS_FMT_FORMAT
+#define STRONG_HAS_FMT_FORMAT 0
+#endif
+
+#if STRONG_HAS_STD_FORMAT
+#include <format>
+#if !defined(__cpp_lib_format) || __cpp_lib_format < 201907
+#undef STRONG_HAS_STD_FORMAT
+#define STRONG_HAS_STD_FORMAT 0
+#endif
+#endif
+
+#if STRONG_HAS_FMT_FORMAT
+#include <fmt/format.h>
+#endif
+
+namespace strong
+{
+
+namespace impl
+{
+  template <typename T, typename ... V>
+  using WhenConstructible = std::enable_if_t<std::is_constructible_v<T, V...>>;
+}
+
+template <typename M, typename T>
+using modifier = typename M::template modifier<T>;
+
+struct uninitialized_t {};
+static constexpr uninitialized_t uninitialized{};
+
+struct default_constructible
+{
+  template <typename T>
+  class modifier
+  {
+  };
+};
+
+namespace impl {
+  template <typename T>
+  constexpr bool supports_default_construction(const ::strong::default_constructible::modifier<T>*)
+  {
+    return true;
+  }
+}
+
+template <typename T, typename Tag, typename ... M>
+class type : public modifier<M, type<T, Tag, M...>>...
+{
+public:
+  template <typename TT = T, typename = std::enable_if_t<std::is_trivially_constructible<TT>{}>>
+  explicit type(uninitialized_t)
+    noexcept
+  {
+  }
+  template <typename type_ = type,
+            bool = impl::supports_default_construction(static_cast<type_*>(nullptr))>
+  constexpr
+  type()
+    noexcept(noexcept(T{}))
+  : val{}
+  {
+  }
+
+  template <typename U,
+    typename = impl::WhenConstructible<T, std::initializer_list<U>>>
+  constexpr
+  explicit
+  type(
+    std::initializer_list<U> us
+  )
+    noexcept(noexcept(T{us}))
+  : val{us}
+  {
+  }
+  template <typename ... U,
+            typename = std::enable_if_t<std::is_constructible_v<T, U&&...> && (sizeof...(U) > 0)>>
+  constexpr
+  explicit
+  type(
+    U&& ... u)
+  noexcept(std::is_nothrow_constructible_v<T, U...>)
+  : val(std::forward<U>(u)...)
+  {}
+
+  friend constexpr void swap(type& a, type& b) noexcept(
+                                                        std::is_nothrow_move_constructible_v<T> &&
+                                                        std::is_nothrow_move_assignable_v<T>
+                                                      )
+  {
+    using std::swap;
+    swap(a.val, b.val);
+  }
+
+  [[nodiscard]]
+  constexpr T& value_of() & noexcept { return val;}
+  [[nodiscard]]
+  constexpr const T& value_of() const & noexcept { return val;}
+  [[nodiscard]]
+  constexpr T&& value_of() && noexcept { return std::move(val);}
+
+  [[nodiscard]]
+  friend constexpr T& value_of(type& t) noexcept { return t.val;}
+  [[nodiscard]]
+  friend constexpr const T& value_of(const type& t) noexcept { return t.val;}
+  [[nodiscard]]
+  friend constexpr T&& value_of(type&& t) noexcept { return std::move(t).val;}
+private:
+  T val;
+};
+
+namespace impl {
+  template <typename T, typename Tag, typename ... Ms>
+  constexpr bool is_strong_type_func(const strong::type<T, Tag, Ms...>*) { return true;}
+  constexpr bool is_strong_type_func(...) { return false;}
+  template <typename T, typename Tag, typename ... Ms>
+  constexpr T underlying_type(strong::type<T, Tag, Ms...>*);
+
+}
+
+template <typename T>
+struct is_strong_type : std::integral_constant<bool, impl::is_strong_type_func(static_cast<T *>(nullptr))> {};
+
+namespace impl {
+  template <typename T>
+  using WhenStrongType = std::enable_if_t<is_strong_type<std::decay_t<T>>::value>;
+  template <typename T>
+  using WhenNotStrongType = std::enable_if_t<!is_strong_type<std::decay_t<T>>::value>;
+}
+
+template <typename T, bool = is_strong_type<T>::value>
+struct underlying_type
+{
+  using type = decltype(impl::underlying_type(static_cast<T*>(nullptr)));
+};
+
+template <typename T>
+struct underlying_type<T, false>
+{
+  using type = T;
+};
+
+template <typename T>
+using underlying_type_t = typename underlying_type<T>::type;
+
+
+namespace impl {
+  template<
+    typename T,
+    typename = impl::WhenNotStrongType<T>>
+  constexpr
+  T &&
+  access(T &&t)
+  noexcept {
+    return std::forward<T>(t);
+  }
+  template <
+    typename T,
+    typename = impl::WhenStrongType<T>>
+  [[nodiscard]]
+  constexpr
+  auto
+  access(T&& t)
+  noexcept
+  -> decltype(value_of(std::forward<T>(t)))
+  {
+    return value_of(std::forward<T>(t));
+  }
+
+}
+struct equality
+{
+  template <typename T>
+  class modifier;
+};
+
+
+template <typename T, typename Tag, typename ... M>
+class equality::modifier<::strong::type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+public:
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator==(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() == std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() == std::declval<const T&>())
+  {
+    return value_of(lh) == value_of(rh);
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator!=(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() != std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() != std::declval<const T&>())
+  {
+    return value_of(lh) != value_of(rh);
+  }
+};
+
+namespace impl
+{
+  template <typename T, typename Other>
+  class typed_equality
+  {
+  private:
+    using TT = underlying_type_t<T>;
+    using OT = underlying_type_t<Other>;
+  public:
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator==(const T& lh, const Other& rh)
+    noexcept(noexcept(std::declval<const TT&>() == std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() == std::declval<const OT&>())
+    {
+      return value_of(lh) == impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator==(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() == std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() == std::declval<const TT&>())
+    {
+      return impl::access(lh) == value_of(rh) ;
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator!=(const T& lh, const Other rh)
+    noexcept(noexcept(std::declval<const TT&>() != std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() != std::declval<const OT&>())
+    {
+      return value_of(lh) != impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator!=(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() != std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() != std::declval<const TT&>())
+    {
+      return impl::access(lh) != value_of(rh) ;
+    }
+  };
+}
+template <typename ... Ts>
+struct equality_with
+{
+  template <typename T>
+  class modifier : public impl::typed_equality<T, Ts>...
+  {
+  };
+};
+
+namespace impl
+{
+  template <typename T, typename Other>
+  class typed_ordering
+  {
+  private:
+    using TT = underlying_type_t<T>;
+    using OT = underlying_type_t<Other>;
+  public:
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator<(const T& lh, const Other& rh)
+    noexcept(noexcept(std::declval<const TT&>() < std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() < std::declval<const OT&>())
+    {
+      return value_of(lh) < impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator<(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() < std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() < std::declval<const TT&>())
+    {
+      return impl::access(lh) < value_of(rh) ;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator<=(const T& lh, const Other& rh)
+    noexcept(noexcept(std::declval<const TT&>() <= std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() <= std::declval<const OT&>())
+    {
+      return value_of(lh) <= impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator<=(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() <= std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() <= std::declval<const TT&>())
+    {
+      return impl::access(lh) <= value_of(rh) ;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator>(const T& lh, const Other& rh)
+    noexcept(noexcept(std::declval<const TT&>() > std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() > std::declval<const OT&>())
+    {
+      return value_of(lh) > impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator>(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() > std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() > std::declval<const TT&>())
+    {
+      return impl::access(lh) > value_of(rh) ;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator>=(const T& lh, const Other& rh)
+    noexcept(noexcept(std::declval<const TT&>() >= std::declval<const OT&>()))
+    -> decltype(std::declval<const TT&>() >= std::declval<const OT&>())
+    {
+      return value_of(lh) >= impl::access(rh);
+    }
+    [[nodiscard]]
+    friend
+    constexpr
+    auto operator>=(const Other& lh, const T& rh)
+    noexcept(noexcept(std::declval<const OT&>() >= std::declval<const TT&>()))
+    -> decltype(std::declval<const OT&>() >= std::declval<const TT&>())
+    {
+      return impl::access(lh) >= value_of(rh) ;
+    }
+  };
+}
+
+template <typename ... Ts>
+struct ordered_with
+{
+  template <typename T>
+  class modifier : public impl::typed_ordering<T, Ts>...
+  {
+  };
+};
+
+namespace impl
+{
+  template <typename T>
+  struct require_copy_constructible
+  {
+    static constexpr bool value = std::is_copy_constructible<underlying_type_t<T>>::value;
+    static_assert(value, "underlying type must be copy constructible");
+  };
+  template <typename T>
+  struct require_move_constructible
+  {
+    static constexpr bool value = std::is_move_constructible<underlying_type_t<T>>::value;
+    static_assert(value, "underlying type must be move constructible");
+  };
+  template <typename T>
+  struct require_copy_assignable
+  {
+    static constexpr bool value = std::is_copy_assignable<underlying_type_t<T>>::value;
+    static_assert(value, "underlying type must be copy assignable");
+  };
+  template <typename T>
+  struct require_move_assignable
+  {
+    static constexpr bool value = std::is_move_assignable<underlying_type_t<T>>::value;
+    static_assert(value, "underlying type must be move assignable");
+  };
+
+  template <bool> struct valid_type;
+  template <>
+  struct valid_type<true> {};
+
+  template <typename T>
+  struct require_semiregular
+    : valid_type<require_copy_constructible<T>::value &&
+                 require_move_constructible<T>::value &&
+                 require_copy_assignable<T>::value &&
+                 require_move_assignable<T>::value>
+  {
+  };
+
+}
+struct semiregular
+{
+  template <typename>
+  class modifier;
+};
+
+template <typename T, typename Tag, typename ... M>
+class semiregular::modifier<::strong::type<T, Tag, M...>>
+  : public default_constructible::modifier<T>
+  , private impl::require_semiregular<T>
+{
+};
+
+struct regular
+{
+  template <typename T>
+  class modifier
+    : public semiregular::modifier<T>
+    , public equality::modifier<T>
+  {
+  };
+};
+
+struct unique
+{
+  template <typename T>
+  class modifier
+    : private impl::valid_type<
+      impl::require_move_constructible<T>::value &&
+      impl::require_move_assignable<T>::value
+    >
+  {
+  public:
+    constexpr modifier() = default;
+    modifier(const modifier&) = delete;
+    constexpr modifier(modifier&&) = default;
+    modifier& operator=(const modifier&) = delete;
+    constexpr modifier& operator=(modifier&&) = default;
+  };
+};
+struct ordered
+{
+  template <typename T>
+  class modifier;
+};
+
+
+template <typename T, typename Tag, typename ... M>
+class ordered::modifier<::strong::type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+public:
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator<(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() < std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() < std::declval<const T&>())
+  {
+    return value_of(lh) < value_of(rh);
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator<=(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() <= std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() <= std::declval<const T&>())
+  {
+    return value_of(lh) <= value_of(rh);
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator>(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() > std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() > std::declval<const T&>())
+  {
+    return value_of(lh) > value_of(rh);
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+
+  auto
+  operator>=(
+    const type& lh,
+    const type& rh)
+  noexcept(noexcept(std::declval<const T&>() >= std::declval<const T&>()))
+  -> decltype(std::declval<const T&>() >= std::declval<const T&>())
+  {
+    return value_of(lh) >= value_of(rh);
+  }
+};
+
+struct ostreamable
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    friend
+    std::ostream&
+    operator<<(
+      std::ostream &os,
+      const T &t)
+    {
+      return os << value_of(t);
+    }
+  };
+};
+
+struct istreamable
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    friend
+    std::istream&
+    operator>>(
+      std::istream &is,
+      T &t)
+    {
+      return is >> value_of(t);
+    }
+  };
+};
+
+struct iostreamable
+{
+  template <typename T>
+  class modifier
+    : public ostreamable::modifier<T>
+    , public istreamable::modifier<T>
+  {
+  };
+};
+
+struct incrementable
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    friend
+    constexpr
+    T&
+    operator++(T& t)
+    noexcept(noexcept(++std::declval<T&>().value_of()))
+    {
+      ++value_of(t);
+      return t;
+    }
+
+    friend
+    constexpr
+    T
+    operator++(T& t, int)
+    {
+      auto copy = t;
+      ++t;
+      return copy;
+    }
+  };
+};
+
+struct decrementable
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    friend
+    constexpr
+    T&
+    operator--(T& t)
+    noexcept(noexcept(--std::declval<T&>().value_of()))
+    {
+      --value_of(t);
+      return t;
+    }
+
+    friend
+    constexpr
+    T
+    operator--(T& t, int)
+    {
+      auto copy = t;
+      --t;
+      return copy;
+    }
+  };
+};
+
+struct bicrementable
+{
+  template <typename T>
+  class modifier
+    : public incrementable::modifier<T>
+    , public decrementable::modifier<T>
+  {
+  };
+};
+
+struct boolean
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    explicit constexpr operator bool() const
+    noexcept(noexcept(static_cast<bool>(value_of(std::declval<const T&>()))))
+    {
+      const auto& self = static_cast<const T&>(*this);
+      return static_cast<bool>(value_of(self));
+    }
+  };
+};
+
+struct hashable
+{
+  template <typename T>
+  class modifier{};
+};
+
+struct difference
+{
+  template <typename T>
+  class modifier;
+};
+
+template <typename T, typename Tag, typename ... M>
+class difference::modifier<::strong::type<T, Tag, M...>>
+: public ordered::modifier<::strong::type<T, Tag, M...>>
+, public equality::modifier<::strong::type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+public:
+  friend
+  constexpr
+  type& operator+=(type& lh, const type& rh)
+  noexcept(noexcept(value_of(lh) += value_of(rh)))
+  {
+    value_of(lh) += value_of(rh);
+    return lh;
+  }
+
+  friend
+  constexpr
+  type& operator-=(type& lh, const type& rh)
+    noexcept(noexcept(value_of(lh) -= value_of(rh)))
+  {
+    value_of(lh) -= value_of(rh);
+    return lh;
+  }
+
+  friend
+  constexpr
+  type& operator*=(type& lh, const T& rh)
+  noexcept(noexcept(value_of(lh) *= rh))
+  {
+    value_of(lh) *= rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  type& operator/=(type& lh, const T& rh)
+    noexcept(noexcept(value_of(lh) /= rh))
+  {
+    value_of(lh) /= rh;
+    return lh;
+  }
+
+  template <typename TT = T, typename = decltype(std::declval<TT&>()%= std::declval<const TT&>())>
+  friend
+  constexpr
+  type& operator%=(type& lh, const T& rh)
+    noexcept(noexcept(value_of(lh) %= rh))
+  {
+    value_of(lh)%= rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  type operator+(type lh, const type& rh)
+  {
+    lh += rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  type operator-(type lh, const type& rh)
+  {
+    lh -= rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  type operator*(type lh, const T& rh)
+  {
+    lh *= rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  type operator*(const T& lh, type rh)
+  {
+    rh *= lh;
+    return rh;
+  }
+
+  friend
+  constexpr
+  type operator/(type lh, const T& rh)
+  {
+    lh /= rh;
+    return lh;
+  }
+
+  friend
+  constexpr
+  T operator/(const type& lh, const type& rh)
+  {
+    return value_of(lh) / value_of(rh);
+  }
+
+  template <typename TT = T, typename = decltype(std::declval<TT&>() %= std::declval<const TT&>())>
+  friend
+  constexpr
+  type operator%(type lh, const T& rh)
+    noexcept(noexcept(lh%= rh))
+  {
+      lh %= rh;
+      return lh;
+  }
+
+  template <typename TT = T, typename = decltype(std::declval<TT>() % std::declval<TT>())>
+  friend
+  constexpr
+  T operator%(type lh, type rh)
+    noexcept(noexcept(value_of(lh) % value_of(rh)))
+  {
+      return value_of(lh) % value_of(rh);
+  }
+};
+
+template <typename D = void>
+struct affine_point
+{
+  template <typename T>
+  class modifier;
+};
+
+namespace impl
+{
+  template <typename ...>
+  using void_t = void;
+
+  template <typename T, typename = void>
+  struct subtractable : std::false_type {};
+
+  template <typename T>
+  struct subtractable<T, void_t<decltype(std::declval<const T&>() - std::declval<const T&>())>>
+  : std::true_type {};
+}
+
+
+template <typename D>
+template <typename T, typename Tag, typename ... M>
+class affine_point<D>::modifier<::strong::type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+  static_assert(impl::subtractable<T>::value, "it must be possible to subtract instances of your underlying type");
+  using base_diff_type = decltype(std::declval<const T&>() - std::declval<const T&>());
+public:
+  using difference = std::conditional_t<std::is_same<D, void>{}, strong::type<base_diff_type, Tag, strong::difference>, D>;
+  static_assert(std::is_constructible_v<difference, base_diff_type>, "");
+  [[nodiscard]]
+  friend
+  constexpr
+  difference
+  operator-(
+    const type& lh,
+    const type& rh)
+  {
+    return difference(value_of(lh) - value_of(rh));
+  }
+
+  friend
+  constexpr
+  type&
+  operator+=(
+    type& lh,
+    const difference& d)
+  noexcept(noexcept(value_of(lh) += impl::access(d)))
+  {
+    value_of(lh) += impl::access(d);
+    return lh;
+  }
+
+  friend
+  constexpr
+  type&
+  operator-=(
+    type& lh,
+    const difference& d)
+  noexcept(noexcept(value_of(lh) -= impl::access(d)))
+  {
+    value_of(lh) -= impl::access(d);
+    return lh;
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  type
+  operator+(
+    type lh,
+    const difference& d)
+  {
+    return lh += d;
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  type
+  operator+(
+    const difference& d,
+    type rh)
+  {
+    return rh+= d;
+  }
+
+  [[nodiscard]]
+  friend
+  constexpr
+  type
+  operator-(
+    type lh,
+    const difference& d)
+  {
+    return lh -= d;
+  }
+};
+
+
+struct pointer
+{
+  template <typename T>
+  class modifier;
+};
+
+template <typename T, typename Tag, typename ... M>
+class pointer::modifier<::strong::type<T, Tag, M...>>
+{
+  using type = strong::type<T, Tag, M...>;
+public:
+  template <typename TT = T>
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator==(
+    const type& t,
+    std::nullptr_t)
+  noexcept(noexcept(std::declval<const TT&>() == nullptr))
+  -> decltype(std::declval<const TT&>() == nullptr)
+  {
+    return value_of(t) == nullptr;
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator==(
+    std::nullptr_t,
+    const type& t)
+  noexcept(noexcept(nullptr == std::declval<const TT&>()))
+  -> decltype(nullptr == std::declval<const TT&>())
+  {
+    return value_of(t) == nullptr;
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator!=(
+    const type& t,
+    std::nullptr_t)
+  noexcept(noexcept(std::declval<const TT&>() != nullptr))
+  -> decltype(std::declval<const TT&>() != nullptr)
+  {
+    return value_of(t) != nullptr;
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  friend
+  constexpr
+  auto
+  operator!=(
+    std::nullptr_t,
+    const type& t)
+  noexcept(noexcept(nullptr != std::declval<const TT&>()))
+  -> decltype(nullptr != std::declval<const TT&>())
+  {
+    return value_of(t) != nullptr;
+  }
+
+  [[nodiscard]]
+  constexpr
+  decltype(*std::declval<const T&>())
+  operator*()
+  const
+  {
+    auto& self = static_cast<const type&>(*this);
+    return *value_of(self);
+  }
+
+  [[nodiscard]]
+  constexpr
+  decltype(&(*std::declval<const T&>())) operator->() const { return &operator*();}
+};
+
+struct arithmetic
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator-(
+      const T &lh)
+    {
+      return T{-value_of(lh)};
+    }
+
+    friend
+    constexpr
+    T&
+    operator+=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) += value_of(rh)))
+    {
+      value_of(lh) += value_of(rh);
+      return lh;
+    }
+
+    friend
+    constexpr
+    T&
+    operator-=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) -= value_of(rh)))
+    {
+      value_of(lh) -= value_of(rh);
+      return lh;
+    }
+
+    friend
+    constexpr
+    T&
+    operator*=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) *= value_of(rh)))
+    {
+      value_of(lh) *= value_of(rh);
+      return lh;
+    }
+
+    friend
+    constexpr
+    T&
+    operator/=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) /= value_of(rh)))
+    {
+      value_of(lh) /= value_of(rh);
+      return lh;
+    }
+
+    template <typename TT = T, typename = decltype(value_of(std::declval<TT>()) % value_of(std::declval<TT>()))>
+    friend
+    constexpr
+    T&
+    operator%=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) %= value_of(rh)))
+    {
+      value_of(lh) %= value_of(rh);
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator+(
+      T lh,
+      const T &rh)
+    {
+      lh += rh;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator-(
+      T lh,
+      const T &rh)
+    {
+      lh -= rh;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator*(
+      T lh,
+      const T &rh)
+    {
+      lh *= rh;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator/(
+      T lh,
+      const T &rh)
+    {
+      lh /= rh;
+      return lh;
+    }
+
+    template <typename TT = T, typename = decltype(value_of(std::declval<TT>()) % value_of(std::declval<TT>()))>
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator%(
+      T lh,
+      const T &rh)
+    {
+      lh %= rh;
+      return lh;
+    }
+
+  };
+};
+
+
+struct bitarithmetic
+{
+  template <typename T>
+  class modifier
+  {
+  public:
+    friend
+    constexpr
+    T&
+    operator&=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) &= value_of(rh)))
+    {
+      value_of(lh) &= value_of(rh);
+      return lh;
+    }
+
+    friend
+    constexpr
+    T&
+    operator|=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) |= value_of(rh)))
+    {
+      value_of(lh) |= value_of(rh);
+      return lh;
+    }
+
+    friend
+    constexpr
+    T&
+    operator^=(
+      T &lh,
+      const T &rh)
+    noexcept(noexcept(value_of(lh) ^= value_of(rh)))
+    {
+      value_of(lh) ^= value_of(rh);
+      return lh;
+    }
+
+    template <typename C>
+    friend
+    constexpr
+    T&
+    operator<<=(
+      T &lh,
+      C c)
+    noexcept(noexcept(value_of(lh) <<= c))
+    {
+      value_of(lh) <<= c;
+      return lh;
+    }
+
+    template <typename C>
+    friend
+    constexpr
+    T&
+    operator>>=(
+      T &lh,
+      C c)
+    noexcept(noexcept(value_of(lh) >>= c))
+    {
+      value_of(lh) >>= c;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator~(
+      const T &lh)
+    {
+      auto v = value_of(lh);
+      v = ~v;
+      return T(v);
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator&(
+      T lh,
+      const T &rh)
+    {
+      lh &= rh;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator|(
+      T lh,
+      const T &rh)
+    {
+      lh |= rh;
+      return lh;
+    }
+
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator^(
+      T lh,
+      const T &rh)
+    {
+      lh ^= rh;
+      return lh;
+    }
+
+    template <typename C>
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator<<(
+      T lh,
+      C c)
+    {
+      lh <<= c;
+      return lh;
+    }
+
+    template <typename C>
+    [[nodiscard]]
+    friend
+    constexpr
+    T
+    operator>>(
+      T lh,
+      C c)
+    {
+      lh >>= c;
+      return lh;
+    }
+  };
+};
+template <typename I = void>
+struct indexed
+{
+  template <typename T>
+  class modifier;
+};
+
+template <>
+struct indexed<void> {
+  template<typename>
+  class modifier;
+
+  template <typename T, typename Tag, typename ... Ms>
+  class modifier<type<T, Tag, Ms...>> {
+    using ref = T&;
+    using cref = const T&;
+    using rref = T&&;
+    using type = strong::type<T, Tag, Ms...>;
+  public:
+    template<typename I>
+    [[nodiscard]]
+    auto
+    operator[](
+      const I &i)
+    const &
+    noexcept(noexcept(std::declval<cref>()[impl::access(i)]))
+    -> decltype(std::declval<cref>()[impl::access(i)]) {
+      auto& self = static_cast<const type&>(*this);
+      return value_of(self)[impl::access(i)];
+    }
+
+    template<typename I>
+    [[nodiscard]]
+    auto
+    operator[](
+      const I &i)
+    &
+    noexcept(noexcept(std::declval<ref>()[impl::access(i)]))
+    -> decltype(std::declval<ref>()[impl::access(i)]) {
+      auto& self = static_cast<type&>(*this);
+      return value_of(self)[impl::access(i)];
+    }
+
+    template<typename I>
+    [[nodiscard]]
+    auto
+    operator[](
+      const I &i)
+    &&
+    noexcept(noexcept(std::declval<rref>()[impl::access(i)]))
+    -> decltype(std::declval<rref>()[impl::access(i)]) {
+      auto& self = static_cast<type&>(*this);
+      return value_of(std::move(self))[impl::access(i)];
+    }
+
+    template<typename I, typename C = cref>
+    [[nodiscard]]
+    auto
+    at(
+      const I &i)
+    const &
+    -> decltype(std::declval<C>().at(impl::access(i))) {
+      auto& self = static_cast<const type&>(*this);
+      return value_of(self).at(impl::access(i));
+    }
+
+    template<typename I, typename R = ref>
+    [[nodiscard]]
+    auto
+    at(
+      const I &i)
+    &
+    -> decltype(std::declval<R>().at(impl::access(i))) {
+      auto& self = static_cast<type&>(*this);
+      return value_of(self).at(impl::access(i));
+    }
+
+    template<typename I, typename R = rref>
+    [[nodiscard]]
+    auto
+    at(
+      const I &i)
+    &&
+    -> decltype(std::declval<R>().at(impl::access(i))) {
+      auto& self = static_cast<type&>(*this);
+      return value_of(std::move(self)).at(impl::access(i));
+    }
+  };
+};
+
+template <typename I>
+template <typename T, typename Tag, typename ... M>
+class indexed<I>::modifier<type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+public:
+  [[nodiscard]]
+  auto
+  operator[](
+    const I& i)
+  const &
+  noexcept(noexcept(std::declval<const T&>()[impl::access(i)]))
+  -> decltype(std::declval<const T&>()[impl::access(i)])
+  {
+    auto& self = static_cast<const type&>(*this);
+    return value_of(self)[impl::access(i)];
+  }
+
+  [[nodiscard]]
+  auto
+  operator[](
+    const I& i)
+  &
+  noexcept(noexcept(std::declval<T&>()[impl::access(i)]))
+  -> decltype(std::declval<T&>()[impl::access(i)])
+  {
+    auto& self = static_cast<type&>(*this);
+    return value_of(self)[impl::access(i)];
+  }
+
+  [[nodiscard]]
+  auto
+  operator[](
+    const I& i)
+  &&
+  noexcept(noexcept(std::declval<T&&>()[impl::access(i)]))
+  -> decltype(std::declval<T&&>()[impl::access(i)])
+  {
+    auto& self = static_cast<type&>(*this);
+    return value_of(std::move(self))[impl::access(i)];
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  auto
+  at(
+    const I& i)
+  const &
+  -> decltype(std::declval<const TT&>().at(impl::access(i)))
+  {
+    auto& self = static_cast<const type&>(*this);
+    return value_of(self).at(impl::access(i));
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  auto
+  at(
+    const I& i)
+  &
+  -> decltype(std::declval<TT&>().at(impl::access(i)))
+  {
+    auto& self = static_cast<type&>(*this);
+    return value_of(self).at(impl::access(i));
+  }
+
+  template <typename TT = T>
+  [[nodiscard]]
+  auto
+  at(
+    const I& i)
+  &&
+  -> decltype(std::declval<TT&&>().at(impl::access(i)))
+  {
+    auto& self = static_cast<type&>(*this);
+    return value_of(std::move(self)).at(impl::access(i));
+  }
+};
+
+class iterator
+{
+public:
+  template <typename I, typename category = typename std::iterator_traits<underlying_type_t<I>>::iterator_category>
+  class modifier
+    : public pointer::modifier<I>
+    , public equality::modifier<I>
+    , public incrementable::modifier<I>
+  {
+  public:
+    using difference_type = typename std::iterator_traits<underlying_type_t<I>>::difference_type;
+    using value_type = typename std::iterator_traits<underlying_type_t<I>>::value_type;
+    using pointer = typename std::iterator_traits<underlying_type_t<I>>::value_type;
+    using reference = typename std::iterator_traits<underlying_type_t<I>>::reference;
+    using iterator_category = typename std::iterator_traits<underlying_type_t<I>>::iterator_category;
+  };
+
+  template <typename I>
+  class modifier<I, std::bidirectional_iterator_tag>
+    : public modifier<I, std::forward_iterator_tag>
+      , public decrementable::modifier<I>
+  {
+  };
+  template <typename I>
+  class modifier<I, std::random_access_iterator_tag>
+    : public modifier<I, std::bidirectional_iterator_tag>
+      , public affine_point<typename std::iterator_traits<underlying_type_t<I>>::difference_type>::template modifier<I>
+      , public indexed<>::modifier<I>
+      , public ordered::modifier<I>
+  {
+  };
+};
+
+class range
+{
+public:
+  template <typename R>
+  class modifier;
+};
+
+template <typename T, typename Tag, typename ... M>
+class range::modifier<type<T, Tag, M...>>
+{
+  using type = ::strong::type<T, Tag, M...>;
+  using r_iterator = decltype(std::declval<T&>().begin());
+  using r_const_iterator = decltype(std::declval<const T&>().begin());
+public:
+  using iterator = ::strong::type<r_iterator, Tag, strong::iterator>;
+  using const_iterator = ::strong::type<r_const_iterator, Tag, strong::iterator>;
+
+  iterator
+  begin()
+  noexcept(noexcept(std::declval<T&>().begin()))
+  {
+    auto& self = static_cast<type&>(*this);
+    return iterator{value_of(self).begin()};
+  }
+
+  iterator
+  end()
+  noexcept(noexcept(std::declval<T&>().end()))
+  {
+    auto& self = static_cast<type&>(*this);
+    return iterator{value_of(self).end()};
+  }
+
+  const_iterator
+  cbegin()
+    const
+  noexcept(noexcept(std::declval<const T&>().begin()))
+  {
+    auto& self = static_cast<const type&>(*this);
+    return const_iterator{value_of(self).begin()};
+  }
+
+  const_iterator
+  cend()
+    const
+  noexcept(noexcept(std::declval<const T&>().end()))
+  {
+    auto& self = static_cast<const type&>(*this);
+    return const_iterator{value_of(self).end()};
+  }
+
+  const_iterator
+  begin()
+  const
+  noexcept(noexcept(std::declval<const T&>().begin()))
+  {
+    auto& self = static_cast<const type&>(*this);
+    return const_iterator{value_of(self).begin()};
+  }
+
+  const_iterator
+  end()
+  const
+  noexcept(noexcept(std::declval<const T&>().end()))
+  {
+    auto& self = static_cast<const type&>(*this);
+    return const_iterator{value_of(self).end()};
+  }
+};
+
+namespace impl {
+
+  template<typename T, typename D>
+  struct converter
+  {
+    constexpr explicit operator D() const
+    noexcept(noexcept(static_cast<D>(std::declval<const underlying_type_t<T>&>())))
+    {
+      auto& self = static_cast<const T&>(*this);
+      return static_cast<D>(value_of(self));
+    }
+  };
+  template<typename T, typename D>
+  struct implicit_converter
+  {
+    constexpr operator D() const
+    noexcept(noexcept(static_cast<D>(std::declval<const underlying_type_t<T>&>())))
+    {
+      auto& self = static_cast<const T&>(*this);
+      return static_cast<D>(value_of(self));
+    }
+  };
+}
+template <typename ... Ts>
+struct convertible_to
+{
+  template <typename T>
+  struct modifier : impl::converter<T, Ts>...
+  {
+  };
+};
+
+template <typename ... Ts>
+struct implicitly_convertible_to
+{
+  template <typename T>
+  struct modifier : impl::implicit_converter<T, Ts>...
+  {
+  };
+
+};
+
+struct formattable
+{
+    template <typename T>
+    class modifier{};
+};
+
+}
+
+namespace std {
+template <typename T, typename Tag, typename ... M>
+struct hash<::strong::type<T, Tag, M...>>
+  : std::conditional_t<
+    std::is_base_of<
+      ::strong::hashable::modifier<
+        ::strong::type<T, Tag, M...>
+      >,
+      ::strong::type<T, Tag, M...>
+    >::value,
+    hash<T>,
+    std::false_type>
+{
+  using type = ::strong::type<T, Tag, M...>;
+  decltype(auto)
+  operator()(
+    const ::strong::hashable::modifier<type>& t)
+  const
+  noexcept(noexcept(std::declval<hash<T>>()(value_of(std::declval<const type&>()))))
+  {
+    auto& tt = static_cast<const type&>(t);
+    return hash<T>::operator()(value_of(tt));
+  }
+};
+template <typename T, typename Tag, typename ... M>
+struct is_arithmetic<::strong::type<T, Tag, M...>>
+  : is_base_of<::strong::arithmetic::modifier<::strong::type<T, Tag, M...>>,
+               ::strong::type<T, Tag, M...>>
+{
+};
+
+#if STRONG_HAS_STD_FORMAT
+template<typename T, typename Tag, typename... M, typename Char>
+struct formatter<::strong::type<T, Tag, M...>, Char,
+                 std::enable_if_t<
+                     std::is_base_of<
+                         ::strong::formattable::modifier<
+                             ::strong::type<T, Tag, M...>
+                             >,
+                         ::strong::type<T, Tag, M...>
+                         >::value
+                     >>
+    : formatter<T>
+{
+  using type = ::strong::type<T, Tag, M...>;
+  template<typename FormatContext>
+  constexpr
+  decltype(auto)
+  format(const ::strong::formattable::modifier<type>& t, FormatContext& fc)
+      noexcept(noexcept(std::declval<formatter<T, Char>>().format(value_of(std::declval<const type&>()), fc)))
+  {
+    const auto& tt = static_cast<const type&>(t);
+    return formatter<T, Char>::format(value_of(tt), fc);
+  }
+};
+#endif
+
+}
+
+#if STRONG_HAS_FMT_FORMAT
+namespace fmt
+{
+template<typename T, typename Tag, typename... M, typename Char>
+struct formatter<::strong::type<T, Tag, M...>, Char,
+                 std::enable_if_t<
+                   std::is_base_of<
+                     ::strong::formattable::modifier<
+                       ::strong::type<T, Tag, M...>
+                     >,
+                     ::strong::type<T, Tag, M...>
+                   >::value
+                 >>
+  : formatter<T>
+{
+  using type = ::strong::type<T, Tag, M...>;
+  template<typename FormatContext>
+  constexpr
+  decltype(auto)
+  format(const ::strong::formattable::modifier<type>& t, FormatContext& fc)
+      noexcept(noexcept(std::declval<formatter<T, Char>>().format(value_of(std::declval<const type&>()), fc)))
+  {
+    const auto& tt = static_cast<const type&>(t);
+    return formatter<T, Char>::format(value_of(tt), fc);
+  }
+};
+}
+#endif
+#endif //ROLLBEAR_STRONG_TYPE_HPP_INCLUDED
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/tempfile.h b/lib/python3.11/site-packages/torch/include/c10/util/tempfile.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf0a291c390078574c311bc6c7c82d5092d6dc7f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/tempfile.h
@@ -0,0 +1,89 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <optional>
+#include <string>
+#include <string_view>
+#include <utility>
+
+namespace c10 {
+struct C10_API TempFile {
+  TempFile(std::string_view name, int fd = -1) noexcept : fd(fd), name(name) {}
+  TempFile(const TempFile&) = delete;
+  TempFile(TempFile&& other) noexcept
+      : fd(other.fd), name(std::move(other.name)) {
+    other.fd = -1;
+  }
+
+  TempFile& operator=(const TempFile&) = delete;
+  TempFile& operator=(TempFile&& other) noexcept {
+    fd = other.fd;
+    name = std::move(other.name);
+    other.fd = -1;
+    return *this;
+  }
+#if defined(_WIN32)
+  bool open();
+#endif
+
+  ~TempFile();
+
+  int fd;
+
+  std::string name;
+};
+
+struct C10_API TempDir {
+  TempDir() = delete;
+  explicit TempDir(std::string_view name) noexcept : name(name) {}
+  TempDir(const TempDir&) = delete;
+  TempDir(TempDir&& other) noexcept : name(std::move(other.name)) {
+    other.name.clear();
+  }
+
+  TempDir& operator=(const TempDir&) = delete;
+  TempDir& operator=(TempDir&& other) noexcept {
+    name = std::move(other.name);
+    return *this;
+  }
+
+  ~TempDir();
+
+  std::string name;
+};
+
+/// Attempts to return a temporary file or returns `nullopt` if an error
+/// occurred.
+///
+/// The file returned follows the pattern
+/// `<tmp-dir>/<name-prefix><random-pattern>`, where `<tmp-dir>` is the value of
+/// the `"TMPDIR"`, `"TMP"`, `"TEMP"` or
+/// `"TEMPDIR"` environment variable if any is set, or otherwise `/tmp`;
+/// `<name-prefix>` is the value supplied to this function, and
+/// `<random-pattern>` is a random sequence of numbers.
+/// On Windows, `name_prefix` is ignored and `tmpnam_s` is used,
+/// and no temporary file is opened.
+C10_API std::optional<TempFile> try_make_tempfile(
+    std::string_view name_prefix = "torch-file-");
+
+/// Like `try_make_tempfile`, but throws an exception if a temporary file could
+/// not be returned.
+C10_API TempFile make_tempfile(std::string_view name_prefix = "torch-file-");
+
+/// Attempts to return a temporary directory or returns `nullopt` if an error
+/// occurred.
+///
+/// The directory returned follows the pattern
+/// `<tmp-dir>/<name-prefix><random-pattern>/`, where `<tmp-dir>` is the value
+/// of the `"TMPDIR"`, `"TMP"`, `"TEMP"` or
+/// `"TEMPDIR"` environment variable if any is set, or otherwise `/tmp`;
+/// `<name-prefix>` is the value supplied to this function, and
+/// `<random-pattern>` is a random sequence of numbers.
+/// On Windows, `name_prefix` is ignored.
+C10_API std::optional<TempDir> try_make_tempdir(
+    std::string_view name_prefix = "torch-dir-");
+
+/// Like `try_make_tempdir`, but throws an exception if a temporary directory
+/// could not be returned.
+C10_API TempDir make_tempdir(std::string_view name_prefix = "torch-dir-");
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/thread_name.h b/lib/python3.11/site-packages/torch/include/c10/util/thread_name.h
new file mode 100644
index 0000000000000000000000000000000000000000..ed849597e0ad3f0e5dcef4029435bfa9f124fb13
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/thread_name.h
@@ -0,0 +1,13 @@
+#pragma once
+
+#include <string>
+
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+C10_API void setThreadName(std::string name);
+
+C10_API std::string getThreadName();
+
+} // namespace c10
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/typeid.h b/lib/python3.11/site-packages/torch/include/c10/util/typeid.h
new file mode 100644
index 0000000000000000000000000000000000000000..b36d2eaf67f8fcb0db50888f93ccfab3cf76e924
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/typeid.h
@@ -0,0 +1,716 @@
+#pragma once
+
+#include <array>
+#include <atomic>
+#include <cstddef>
+#include <cstdint>
+#include <memory>
+#include <mutex>
+#include <ostream>
+#include <string>
+#include <type_traits>
+#include <vector>
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Half.h>
+#include <c10/util/IdWrapper.h>
+#include <c10/util/TypeIndex.h>
+#include <c10/util/TypeTraits.h>
+#include <c10/util/irange.h>
+#include <c10/util/string_view.h>
+
+#include <c10/core/ScalarType.h>
+
+/*
+ * TypeIdentifier is a small type containing an id.
+ * Types must be registered using CAFFE_DECLARE_KNOWN_TYPE() (in their header)
+ * and CAFFE_DEFINE_KNOWN_TYPE() (in their .cpp file) for them to have a type
+ * id. If a type is registered, you can also create an object containing meta
+ * data like constructor, destructor, stringified name, ... about the type by
+ * calling TypeMeta::Make<T>. This returns a TypeMeta() object, which is
+ * basically just a pointer to the type information, so it's cheap to pass
+ * around.
+ */
+
+// TODO: This file is still in the caffe2 namespace, despite living
+// in the ATen directory.  This is because the macro
+// CAFFE_KNOWN_TYPE (and CAFFE_DECLARE_KNOWN_TYPE) defines a template
+// specialization, which relies
+// on the namespace of TypeMeta matching the namespace where the macro is
+// called.  This requires us to fix all of the call-sites, which I want to do
+// later.  So the namespace is not fixed at the moment.
+
+// Make at::Half a fundamental type.
+
+namespace c10::guts {
+template <>
+struct is_fundamental<at::Half> : std::true_type {};
+} // namespace c10::guts
+
+namespace caffe2 {
+
+/**
+ * A type id is a unique id for a given C++ type.
+ * You need to register your types using CAFFE_KNOWN_TYPE(MyType) to be able to
+ * use TypeIdentifier with custom types. This is for example used to store the
+ * dtype of tensors.
+ */
+class C10_API TypeIdentifier final
+    : public at::IdWrapper<TypeIdentifier, c10::util::type_index> {
+ public:
+  friend std::ostream& operator<<(std::ostream& stream, TypeIdentifier typeId);
+  friend constexpr bool operator<(TypeIdentifier lhs, TypeIdentifier rhs);
+
+  /**
+   * Returns the unique id for the given type T. The id is unique for the type T
+   * in the sense that for any two different types, their ids are different; for
+   * the same type T, the id remains the same over different calls of the
+   * function. However, this is not guaranteed over different runs, as the id
+   * is generated during run-time. Do NOT serialize the id for storage.
+   */
+  template <typename T>
+  static constexpr TypeIdentifier Get() noexcept {
+    return TypeIdentifier(c10::util::get_type_index<T>());
+  }
+
+  static constexpr TypeIdentifier uninitialized() {
+    return TypeIdentifier(c10::util::type_index{0});
+  }
+
+ private:
+  constexpr explicit TypeIdentifier(c10::util::type_index id) : IdWrapper(id) {}
+};
+
+// Allow usage in std::map / std::set
+// TODO Disallow this and rather use std::unordered_map/set everywhere
+inline constexpr bool operator<(TypeIdentifier lhs, TypeIdentifier rhs) {
+  return lhs.underlyingId() < rhs.underlyingId();
+}
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    caffe2::TypeIdentifier typeId) {
+  return stream << typeId.underlyingId();
+}
+
+} // namespace caffe2
+
+namespace at {
+using DataType = caffe2::TypeIdentifier;
+}
+
+C10_DEFINE_HASH_FOR_IDWRAPPER(caffe2::TypeIdentifier)
+
+namespace caffe2 {
+
+namespace detail {
+
+// This struct holds the actual type information. There will be
+// one allocated per type. TypeMeta objects will then point to the struct
+// instance for the type they're configured for.
+struct TypeMetaData final {
+  using New = void*();
+  using PlacementNew = void(void*, size_t);
+  using Copy = void(const void*, void*, size_t);
+  using PlacementDelete = void(void*, size_t);
+  using Delete = void(void*);
+
+  constexpr TypeMetaData() noexcept
+      : itemsize_(0),
+        new_(nullptr),
+        placementNew_(nullptr),
+        copy_(nullptr),
+        placementDelete_(nullptr),
+        delete_(nullptr),
+        id_(TypeIdentifier::uninitialized()),
+        name_("nullptr (uninitialized)") {}
+
+  constexpr TypeMetaData(
+      size_t itemsize,
+      New* newFn,
+      PlacementNew* placementNew,
+      Copy* copy,
+      PlacementDelete* placementDelete,
+      Delete* deleteFn,
+      TypeIdentifier id,
+      c10::string_view name) noexcept
+      : itemsize_(itemsize),
+        new_(newFn),
+        placementNew_(placementNew),
+        copy_(copy),
+        placementDelete_(placementDelete),
+        delete_(deleteFn),
+        id_(id),
+        name_(name) {}
+
+  size_t itemsize_;
+  New* new_;
+  PlacementNew* placementNew_;
+  Copy* copy_;
+  PlacementDelete* placementDelete_;
+  Delete* delete_;
+  TypeIdentifier id_;
+  c10::string_view name_;
+};
+
+// Mechanism for throwing errors which can't be prevented at compile time
+// due to type erasure. E.g. somebody calling TypeMeta::copy() for
+// non-copyable type. Right now just throws exception but is implemented
+// in .cpp to manage dependencies
+[[noreturn]] C10_API void _ThrowRuntimeTypeLogicError(const std::string& msg);
+
+/**
+ * Placement new function for the type.
+ */
+template <typename T>
+inline void _PlacementNew(void* ptr, size_t n) {
+  T* typed_ptr = static_cast<T*>(ptr);
+  for (const auto i : c10::irange(n)) {
+    new (typed_ptr + i) T;
+  }
+}
+
+template <typename T>
+inline void _PlacementNewNotDefault(void* /*ptr*/, size_t /*n*/) {
+  _ThrowRuntimeTypeLogicError(
+      "Type " + std::string(c10::util::get_fully_qualified_type_name<T>()) +
+      " is not default-constructible.");
+}
+
+template <
+    typename T,
+    std::enable_if_t<std::is_default_constructible_v<T>>* = nullptr>
+inline constexpr TypeMetaData::PlacementNew* _PickPlacementNew() {
+  return (c10::guts::is_fundamental<T>::value || std::is_pointer_v<T>)
+      ? nullptr
+      : &_PlacementNew<T>;
+}
+
+template <
+    typename T,
+    std::enable_if_t<!std::is_default_constructible_v<T>>* = nullptr>
+inline constexpr TypeMetaData::PlacementNew* _PickPlacementNew() {
+  static_assert(
+      !c10::guts::is_fundamental<T>::value && !std::is_pointer_v<T>,
+      "this should have picked the other SFINAE case");
+  return &_PlacementNewNotDefault<T>;
+}
+
+template <typename T>
+inline void* _New() {
+  return new T;
+}
+
+template <typename T>
+inline void* _NewNotDefault() {
+  _ThrowRuntimeTypeLogicError(
+      "Type " + std::string(c10::util::get_fully_qualified_type_name<T>()) +
+      " is not default-constructible.");
+}
+
+template <
+    typename T,
+    std::enable_if_t<std::is_default_constructible_v<T>>* = nullptr>
+inline constexpr TypeMetaData::New* _PickNew() {
+  return &_New<T>;
+}
+
+template <
+    typename T,
+    std::enable_if_t<!std::is_default_constructible_v<T>>* = nullptr>
+inline constexpr TypeMetaData::New* _PickNew() {
+  return &_NewNotDefault<T>;
+}
+
+/**
+ * Typed copy function for classes.
+ */
+template <typename T>
+inline void _Copy(const void* src, void* dst, size_t n) {
+  const T* typed_src = static_cast<const T*>(src);
+  T* typed_dst = static_cast<T*>(dst);
+  for (const auto i : c10::irange(n)) {
+    typed_dst[i] = typed_src[i];
+  }
+}
+
+/**
+ * A placeholder function for types that do not allow assignment.
+ */
+template <typename T>
+inline void _CopyNotAllowed(const void* /*src*/, void* /*dst*/, size_t /*n*/) {
+  _ThrowRuntimeTypeLogicError(
+      "Type " + std::string(c10::util::get_fully_qualified_type_name<T>()) +
+      " does not allow assignment.");
+}
+
+template <typename T, std::enable_if_t<std::is_copy_assignable_v<T>>* = nullptr>
+inline constexpr TypeMetaData::Copy* _PickCopy() {
+  return (c10::guts::is_fundamental<T>::value || std::is_pointer_v<T>)
+      ? nullptr
+      : &_Copy<T>;
+}
+
+template <
+    typename T,
+    std::enable_if_t<!std::is_copy_assignable_v<T>>* = nullptr>
+inline constexpr TypeMetaData::Copy* _PickCopy() {
+  static_assert(
+      !c10::guts::is_fundamental<T>::value && !std::is_pointer_v<T>,
+      "this should have picked the other SFINAE case");
+  return &_CopyNotAllowed<T>;
+}
+
+/**
+ * Destructor for non-fundamental types.
+ */
+template <typename T>
+inline void _PlacementDelete(void* ptr, size_t n) {
+  T* typed_ptr = static_cast<T*>(ptr);
+  for (const auto i : c10::irange(n)) {
+    typed_ptr[i].~T();
+  }
+}
+
+template <typename T>
+inline constexpr TypeMetaData::PlacementDelete* _PickPlacementDelete() {
+  return (c10::guts::is_fundamental<T>::value || std::is_pointer_v<T>)
+      ? nullptr
+      : &_PlacementDelete<T>;
+}
+
+template <typename T>
+inline void _Delete(void* ptr) {
+  T* typed_ptr = static_cast<T*>(ptr);
+  delete typed_ptr;
+}
+
+template <class T>
+inline constexpr TypeMetaData::Delete* _PickDelete() noexcept {
+  return &_Delete<T>;
+}
+
+class _Uninitialized final {};
+
+} // namespace detail
+
+//
+// note: this is outside TypeMeta bc gcc seems to have trouble
+// with scalarTypeItemSizes as a constexpr static member used by
+// a public inline instance method
+//
+
+// item sizes for TypeMeta::itemsize() fast path
+static constexpr std::array<uint8_t, NumScalarTypes> scalarTypeItemSizes = {
+#define SCALAR_TYPE_SIZE(T, name) sizeof(T),
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SCALAR_TYPE_SIZE)
+#undef SCALAR_TYPE_SIZE
+        0, // Undefined
+};
+
+/**
+ * TypeMeta is a thin class that allows us to store the type of a container such
+ * as a blob, or the data type of a tensor, with a unique run-time id. It also
+ * stores some additional data such as the item size and the name of the type
+ * for run-time inspection.
+ */
+class C10_API TypeMeta final {
+ public:
+  using New = detail::TypeMetaData::New;
+  using PlacementNew = detail::TypeMetaData::PlacementNew;
+  using Copy = detail::TypeMetaData::Copy;
+  using PlacementDelete = detail::TypeMetaData::PlacementDelete;
+  using Delete = detail::TypeMetaData::Delete;
+
+  /** Create a dummy TypeMeta object. To create a TypeMeta object for a specific
+   * type, use TypeMeta::Make<T>().
+   */
+  TypeMeta() noexcept;
+  ~TypeMeta() = default;
+
+  /**
+   * Copy constructor.
+   */
+  TypeMeta(const TypeMeta& src) noexcept = default;
+
+  /**
+   * Assignment operators.
+   */
+  TypeMeta& operator=(const TypeMeta& src) noexcept = default;
+
+  TypeMeta& operator=(TypeMeta&& src) noexcept = default;
+  TypeMeta(TypeMeta&& rhs) noexcept = default;
+
+  inline TypeMeta& operator=(ScalarType scalar_type) noexcept {
+    index_ = static_cast<uint16_t>(scalar_type);
+    return *this;
+  }
+
+ private:
+  // TypeMeta can only be created by Make, making sure that we do not
+  // create incorrectly mixed up TypeMeta objects.
+  explicit TypeMeta(const uint16_t index) noexcept : index_(index) {}
+
+ public:
+  /**
+   * Returns the type id.
+   */
+  TypeIdentifier id() const noexcept {
+    return data().id_;
+  }
+  /**
+   * true if we represent some ScalarType type
+   */
+  inline bool isScalarType() const noexcept {
+    return index_ < NumScalarTypes;
+  }
+  /**
+   * true if we represent ScalarType scalar_type
+   */
+  inline bool isScalarType(ScalarType scalar_type) const noexcept {
+    return index_ == static_cast<uint16_t>(scalar_type);
+  }
+  /**
+   * Returns the size of the item.
+   */
+  inline size_t itemsize() const noexcept {
+    if (C10_LIKELY(isScalarType())) {
+      return scalarTypeItemSizes[index_];
+    }
+    return data().itemsize_;
+  }
+  /**
+   * Returns the new function pointer for individual items.
+   */
+  New* newFn() const noexcept {
+    return data().new_;
+  }
+  /**
+   * Returns the placement new function pointer for individual items.
+   */
+  PlacementNew* placementNew() const noexcept {
+    return data().placementNew_;
+  }
+  /**
+   * Returns the typed copy function pointer for individual iterms.
+   */
+  Copy* copy() const noexcept {
+    return data().copy_;
+  }
+  /**
+   * Returns the destructor function pointer for individual items.
+   */
+  PlacementDelete* placementDelete() const noexcept {
+    return data().placementDelete_;
+  }
+  Delete* deleteFn() const noexcept {
+    return data().delete_;
+  }
+  /**
+   * Returns a printable name for the type.
+   */
+  c10::string_view name() const noexcept {
+    return data().name_;
+  }
+
+  friend bool operator==(const TypeMeta& lhs, const TypeMeta& rhs) noexcept;
+
+  template <typename T>
+  bool Match() const noexcept {
+    return (*this == Make<T>());
+  }
+
+  // Below are static functions that can be called by passing a specific type.
+
+  template <class T>
+  static constexpr TypeIdentifier Id() noexcept {
+    return TypeIdentifier::Get<T>();
+  }
+
+  template <class T>
+  static c10::string_view TypeName() noexcept {
+    return c10::util::get_fully_qualified_type_name<T>();
+  }
+
+  template <class T>
+  static constexpr size_t ItemSize() noexcept {
+    return sizeof(T);
+  }
+
+  /**
+   * Returns a TypeMeta object that corresponds to the typename T.
+   */
+  template <typename T>
+  static TypeMeta Make() {
+    // The instance pointed to is declared here, but defined in a .cpp file.
+    // We need to silence the compiler warning about using an undefined
+    // variable template. '-Wpragmas' and '-Wunknown-warning-option' has to be
+    // disabled for compilers that don't know '-Wundefined-var-template' and
+    // would error at our attempt to disable it.
+#ifndef _MSC_VER
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wpragmas"
+#pragma GCC diagnostic ignored "-Wunknown-warning-option"
+#pragma GCC diagnostic ignored "-Wundefined-var-template"
+#endif
+    return TypeMeta(_typeMetaData<T>());
+#ifndef _MSC_VER
+#pragma GCC diagnostic pop
+#endif
+  }
+
+  /**
+   * convert ScalarType enum values to TypeMeta handles
+   */
+  static inline caffe2::TypeMeta fromScalarType(ScalarType scalar_type) {
+    const auto index = static_cast<uint16_t>(scalar_type);
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        index < NumScalarTypes,
+        "Unrecognized Scalartype ",
+        scalar_type,
+        " (please report this error)");
+    return TypeMeta(index);
+  }
+
+  /**
+   * convert TypeMeta handles to ScalarType enum values
+   */
+  inline ScalarType toScalarType() {
+    if (C10_LIKELY(isScalarType())) {
+      return static_cast<ScalarType>(index_);
+    }
+    error_unsupported_typemeta(*this);
+  }
+
+ private:
+  [[noreturn]] static void error_unsupported_typemeta(caffe2::TypeMeta dtype);
+
+  // hard limit number of registered types
+  // note: constexpr provokes Windows compilation error "member may not be
+  // initialized" static constexpr size_t MaxTypeIndex = 32;
+  //
+#if defined C10_MOBILE
+// The reason for this not to be UINT8_MAX is that the array
+// initialization takes space which is proportional to the size of the array.
+// The compiler seems to add code (or data padding) to initialize the array with
+// empty elements. Please see
+// https://github.com/pytorch/pytorch/pull/51881 for details.
+//
+#define MaxTypeIndex                                                           \
+  (NumScalarTypes + 15 /* number of CAFFE_DEFINE_KNOWN_TYPE in typeid.cpp */ + \
+   1 /* 1 more for caffe2 tensor */)
+#else
+#define MaxTypeIndex UINT8_MAX
+#endif
+
+  // Protects type metadata allocation.
+  // NOLINTNEXTLINE(facebook-hte-NonPodStaticDeclaration)
+  static std::mutex& getTypeMetaDatasLock();
+  static uint16_t nextTypeIndex;
+
+  static detail::TypeMetaData* typeMetaDatas();
+
+  static uint16_t existingMetaDataIndexForType(TypeIdentifier identifier);
+
+ public:
+#ifdef __CUDACC__
+  // NOTE [ TypeIdentifier::Get nvcc/clang discrepancy]
+  // nvcc and clang do not produce identical results for
+  // TypeIdentifier::Get, because TypeIdentifier::Get relies on
+  // __PRETTY_FUNCTION__ and they don't agree on the canonical names
+  // of types (e.g., nvcc normalizes to `short unsigned int`, but clang
+  // calls it `unsigned short`). Hide the implementation of this function
+  // from nvcc so that we always use clang (or whatever host C++ compiler)
+  // for TypeIdentifier::Get.
+  template <class T>
+  C10_EXPORT static uint16_t addTypeMetaData();
+#else
+  template <class T>
+  C10_EXPORT static uint16_t addTypeMetaData() {
+    const auto identifier = TypeIdentifier::Get<T>();
+    // Need to hold this for the rest of the function, protecting:
+    // 1) existingMetaDataIndexForType()
+    // 2) nextTypeIndex++
+    // 3) the write into typeMetaDatas()
+    std::lock_guard<std::mutex> lock(getTypeMetaDatasLock());
+    // It may exist already if added in a different dynamic shared library.
+    const uint16_t existing_index = existingMetaDataIndexForType(identifier);
+    if (existing_index != MaxTypeIndex) {
+      return existing_index;
+    }
+    const uint16_t index = nextTypeIndex++;
+    TORCH_CHECK(
+        index <= MaxTypeIndex,
+        "Maximum number of CAFFE_KNOWN_TYPE declarations has been exceeded. ",
+        "Please report this issue.");
+    typeMetaDatas()[index] = detail::TypeMetaData{
+        sizeof(T),
+        detail::_PickNew<T>(),
+        detail::_PickPlacementNew<T>(),
+        detail::_PickCopy<T>(),
+        detail::_PickPlacementDelete<T>(),
+        detail::_PickDelete<T>(),
+        identifier,
+        c10::util::get_fully_qualified_type_name<T>()};
+    return index;
+  }
+#endif
+
+ private:
+  // specializations return indexes into typeMetaDataInstances()
+  template <class T>
+  C10_API static uint16_t _typeMetaData() noexcept;
+
+  //
+  // TypeMeta just wraps this index
+  //
+
+  uint16_t index_;
+
+  inline const detail::TypeMetaData& data() const {
+    return typeMetaDatas()[index_];
+  }
+};
+
+// specializations of TypeMeta::_typeMetaData for ScalarType types
+
+#define DEFINE_SCALAR_METADATA_INSTANCE(T, name)             \
+  template <>                                                \
+  constexpr uint16_t TypeMeta::_typeMetaData<T>() noexcept { \
+    return static_cast<uint16_t>(ScalarType::name);          \
+  }
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_SCALAR_METADATA_INSTANCE)
+#undef DEFINE_SCALAR_METADATA_INSTANCE
+
+template <>
+C10_EXPORT constexpr uint16_t TypeMeta::_typeMetaData<
+    detail::_Uninitialized>() noexcept {
+  return static_cast<uint16_t>(ScalarType::Undefined);
+}
+
+inline TypeMeta::TypeMeta() noexcept
+    : index_(_typeMetaData<detail::_Uninitialized>()) {}
+
+inline bool operator==(const TypeMeta& lhs, const TypeMeta& rhs) noexcept {
+  return (lhs.index_ == rhs.index_);
+}
+inline bool operator!=(const TypeMeta& lhs, const TypeMeta& rhs) noexcept {
+  return !operator==(lhs, rhs);
+}
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    caffe2::TypeMeta typeMeta) {
+  return stream << typeMeta.name();
+}
+
+/**
+ * Register unique id for a type so it can be used in TypeMeta context, e.g. be
+ * used as a type for Blob or for Tensor elements.
+ *
+ * CAFFE_KNOWN_TYPE is deprecated; prefer CAFFE_DECLARE_KNOWN_TYPE and
+ * CAFFE_DEFINE_KNOWN_TYPE.
+ *
+ * CAFFE_KNOWN_TYPE does explicit instantiation of TypeIdentifier::Get<T>
+ * template function and thus needs to be put in a single translation unit (.cpp
+ * file) for a given type T. Other translation units that use type T as a type
+ * of the caffe2::Blob or element type of caffe2::Tensor need to depend on the
+ * translation unit that contains CAFFE_KNOWN_TYPE declaration via regular
+ * linkage dependencies.
+ *
+ * NOTE: the macro needs to be invoked in ::caffe2 namespace
+ */
+// Implementation note: in MSVC, we will need to prepend the C10_API
+// keyword in order to get things compiled properly. in Linux, gcc seems to
+// create attribute ignored error for explicit template instantiations, see
+//   http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2017/p0537r0.html
+//   https://gcc.gnu.org/bugzilla/show_bug.cgi?id=51930
+// and as a result, we define these two macros slightly differently.
+#if defined(_MSC_VER) || defined(__clang__)
+#define EXPORT_IF_NOT_GCC C10_EXPORT
+#else
+#define EXPORT_IF_NOT_GCC
+#endif
+
+// CAFFE_KNOWN_TYPE is deprecated! Use CAFFE_DECLARE_KNOWN_TYPE and
+// CAFFE_DEFINE_KNOWN_TYPE instead.
+#define CAFFE_KNOWN_TYPE(T)                                          \
+  template uint16_t TypeMeta::addTypeMetaData<T>();                  \
+  template <>                                                        \
+  EXPORT_IF_NOT_GCC uint16_t TypeMeta::_typeMetaData<T>() noexcept { \
+    static const uint16_t index = addTypeMetaData<T>();              \
+    return index;                                                    \
+  }
+
+#define CAFFE_DEFINE_KNOWN_TYPE(T, ident)                   \
+  template uint16_t TypeMeta::addTypeMetaData<T>();         \
+  namespace detail {                                        \
+  EXPORT_IF_NOT_GCC const uint16_t ident##_metadata_index = \
+      TypeMeta::addTypeMetaData<T>();                       \
+  } // namespace detail
+
+// Unlike CAFFE_KNOWN_TYPE, CAFFE_DECLARE_KNOWN_TYPE avoids a function
+// call to access _typeMetaData in the common case.
+#define CAFFE_DECLARE_KNOWN_TYPE(T, ident)                 \
+  extern template uint16_t TypeMeta::addTypeMetaData<T>(); \
+  namespace detail {                                       \
+  extern C10_API const uint16_t ident##_metadata_index;    \
+  } /* namespace detail */                                 \
+  template <>                                              \
+  EXPORT_IF_NOT_GCC C10_ALWAYS_INLINE uint16_t             \
+  TypeMeta::_typeMetaData<T>() noexcept {                  \
+    return detail::ident##_metadata_index;                 \
+  }
+
+#define CAFFE_KNOWN_TYPE_NOEXPORT(T)                    \
+  template <>                                           \
+  uint16_t TypeMeta::_typeMetaData<T>() noexcept {      \
+    static const uint16_t index = addTypeMetaData<T>(); \
+    return index;                                       \
+  }
+
+CAFFE_DECLARE_KNOWN_TYPE(std::string, std_string)
+CAFFE_DECLARE_KNOWN_TYPE(char, char)
+CAFFE_DECLARE_KNOWN_TYPE(std::unique_ptr<std::mutex>, std_unique_ptr_std_mutex)
+CAFFE_DECLARE_KNOWN_TYPE(
+    std::unique_ptr<std::atomic<bool>>,
+    std_unique_ptr_std_atomic_bool)
+CAFFE_DECLARE_KNOWN_TYPE(std::vector<int32_t>, std_vector_int32_t)
+CAFFE_DECLARE_KNOWN_TYPE(std::vector<int64_t>, std_vector_int64_t)
+CAFFE_DECLARE_KNOWN_TYPE(std::vector<unsigned long>, std_vector_unsigned_long)
+CAFFE_DECLARE_KNOWN_TYPE(bool*, bool_ptr)
+CAFFE_DECLARE_KNOWN_TYPE(char*, char_ptr)
+CAFFE_DECLARE_KNOWN_TYPE(int*, int_ptr)
+
+// For some of the compilers, long is defined separately from int32_t and
+// int64_t. As a result we will need to actually define them separately.
+// It is recommended that one does NOT use long - use int32_t and int64_t
+// explicitly. Explicit long type annotation may go away in the future.
+// details: This hack works by defining a _guard_long_unique type, which is
+// long iff the compiler has a separate long type and is a dummy type otherwise.
+// we then allocate a type id to that _guard_long_unique. If the compiler has a
+// separate long type, this allocates a type id for long. Otherwise, it
+// allocates a type id for the dummy type, which doesn't matter.
+namespace detail {
+template <class T>
+class _guard_long_unique_dummy final {};
+template <class T>
+using _guard_long_unique = std::conditional_t<
+    std::is_same_v<long, int32_t> || std::is_same_v<long, int64_t>,
+    _guard_long_unique_dummy<T>,
+    T>;
+} // namespace detail
+
+CAFFE_DECLARE_KNOWN_TYPE(
+    detail::_guard_long_unique<long>,
+    detail_guard_long_unique_long)
+CAFFE_DECLARE_KNOWN_TYPE(
+    detail::_guard_long_unique<std::vector<long>>,
+    detail_guard_long_unique_std_vector_long)
+
+CAFFE_DECLARE_KNOWN_TYPE(float*, float_ptr)
+CAFFE_DECLARE_KNOWN_TYPE(at::Half*, at_Half)
+
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/c10/util/win32-headers.h b/lib/python3.11/site-packages/torch/include/c10/util/win32-headers.h
new file mode 100644
index 0000000000000000000000000000000000000000..6a3e8bc2caf42f02fba1925970dd33770a40abfa
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/util/win32-headers.h
@@ -0,0 +1,60 @@
+#pragma once
+
+#ifndef WIN32_LEAN_AND_MEAN
+#define WIN32_LEAN_AND_MEAN
+#endif
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#ifndef NOKERNEL
+#define NOKERNEL
+#endif
+#ifndef NOUSER
+#define NOUSER
+#endif
+#ifndef NOSERVICE
+#define NOSERVICE
+#endif
+#ifndef NOSOUND
+#define NOSOUND
+#endif
+#ifndef NOMCX
+#define NOMCX
+#endif
+#ifndef NOGDI
+#define NOGDI
+#endif
+#ifndef NOMSG
+#define NOMSG
+#endif
+#ifndef NOMB
+#define NOMB
+#endif
+#ifndef NOCLIPBOARD
+#define NOCLIPBOARD
+#endif
+
+// dbghelp seems to require windows.h.
+// clang-format off
+#include <windows.h>
+#include <dbghelp.h>
+// clang-format on
+
+#undef VOID
+#undef DELETE
+#undef IN
+#undef THIS
+#undef CONST
+#undef NAN
+#undef UNKNOWN
+#undef NONE
+#undef ANY
+#undef IGNORE
+#undef STRICT
+#undef GetObject
+#undef CreateSemaphore
+#undef Yield
+#undef RotateRight32
+#undef RotateLeft32
+#undef RotateRight64
+#undef RotateLeft64
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUCachingAllocator.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUCachingAllocator.h
new file mode 100644
index 0000000000000000000000000000000000000000..6cdc8c8c71a6c99e58e600bd37ec1019f7a62c2b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUCachingAllocator.h
@@ -0,0 +1,27 @@
+#pragma once
+
+#include <c10/core/CachingDeviceAllocator.h>
+#include <c10/xpu/XPUStream.h>
+
+namespace c10::xpu::XPUCachingAllocator {
+
+C10_XPU_API Allocator* get();
+
+C10_XPU_API void init(DeviceIndex device_count);
+
+C10_XPU_API void emptyCache();
+
+C10_XPU_API void resetPeakStats(DeviceIndex device);
+
+C10_XPU_API void resetAccumulatedStats(DeviceIndex device);
+
+C10_XPU_API c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+    DeviceIndex device);
+
+C10_XPU_API void* raw_alloc(size_t size);
+
+C10_XPU_API void raw_delete(void* ptr);
+
+C10_XPU_API void recordStream(const DataPtr& dataPtr, XPUStream stream);
+
+} // namespace c10::xpu::XPUCachingAllocator
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUDeviceProp.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUDeviceProp.h
new file mode 100644
index 0000000000000000000000000000000000000000..00b7969a73d4963e56856aade036a1e57eccfd1b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUDeviceProp.h
@@ -0,0 +1,201 @@
+#pragma once
+
+#include <c10/xpu/XPUMacros.h>
+#include <sycl/sycl.hpp>
+
+namespace c10::xpu {
+
+#define AT_FORALL_XPU_DEVICE_PROPERTIES(_)                                     \
+  /* the device name of this SYCL device. */                                   \
+  _(name)                                                                      \
+                                                                               \
+  /* the device type associated with the device. */                            \
+  _(device_type)                                                               \
+                                                                               \
+  /* the vendor of this SYCL device. */                                        \
+  _(vendor)                                                                    \
+                                                                               \
+  /* a backend-defined driver version as a std::string. */                     \
+  _(driver_version)                                                            \
+                                                                               \
+  /* the SYCL version as a std::string in the form <major>.<minor> */          \
+  _(version)                                                                   \
+                                                                               \
+  /* true if the SYCL device is available. Otherwise, return false. */         \
+  _(is_available)                                                              \
+                                                                               \
+  /* the maximum size in bytes of the arguments that can be passed to a        \
+   * kernel. */                                                                \
+  _(max_parameter_size)                                                        \
+                                                                               \
+  /* the number of parallel compute units available to the device. */          \
+  _(max_compute_units)                                                         \
+                                                                               \
+  /* the maximum dimensions that specify the global and local work-item IDs    \
+   * used by the data parallel execution model. */                             \
+  _(max_work_item_dimensions)                                                  \
+                                                                               \
+  /* the maximum number of workitems that are permitted in a work-group        \
+   * executing a kernel on a single compute unit. */                           \
+  _(max_work_group_size)                                                       \
+                                                                               \
+  /* the maximum number of subgroups in a work-group for any kernel executed   \
+   * on the device. */                                                         \
+  _(max_num_sub_groups)                                                        \
+                                                                               \
+  /* a std::vector of size_t containing the set of sub-group sizes  supported  \
+   * by the device. */                                                         \
+  _(sub_group_sizes)                                                           \
+                                                                               \
+  /* the maximum configured clock frequency of this SYCL device in MHz. */     \
+  _(max_clock_frequency)                                                       \
+                                                                               \
+  /* the default compute device address space size specified as an unsigned    \
+   * integer value in bits. Must return either 32 or 64. */                    \
+  _(address_bits)                                                              \
+                                                                               \
+  /* the maximum size of memory object allocation in bytes. */                 \
+  _(max_mem_alloc_size)                                                        \
+                                                                               \
+  /* the minimum value in bits of the largest supported SYCL built-in data     \
+   * type if this SYCL device is not of device type                            \
+   * sycl::info::device_type::custom. */                                       \
+  _(mem_base_addr_align)                                                       \
+                                                                               \
+  /* a std::vector of info::fp_config describing the half/single/double        \
+   * precision floating-point capability of this SYCL device. */               \
+  _(half_fp_config)                                                            \
+  _(single_fp_config)                                                          \
+  _(double_fp_config)                                                          \
+                                                                               \
+  /* the size of global device memory in bytes. */                             \
+  _(global_mem_size)                                                           \
+                                                                               \
+  /* the type of global memory cache supported. */                             \
+  _(global_mem_cache_type)                                                     \
+                                                                               \
+  /* the size of global memory cache in bytes. */                              \
+  _(global_mem_cache_size)                                                     \
+                                                                               \
+  /* the size of global memory cache line in bytes. */                         \
+  _(global_mem_cache_line_size)                                                \
+                                                                               \
+  /* the type of local memory supported. */                                    \
+  _(local_mem_type)                                                            \
+                                                                               \
+  /* the size of local memory arena in bytes. */                               \
+  _(local_mem_size)                                                            \
+                                                                               \
+  /* the maximum number of sub-devices that can be created when this device is \
+   * partitioned. */                                                           \
+  _(partition_max_sub_devices)                                                 \
+                                                                               \
+  /* the resolution of device timer in nanoseconds. */                         \
+  _(profiling_timer_resolution)                                                \
+                                                                               \
+  /* the preferred native vector width size for built-in scalar types that can \
+   * be put into vectors. */                                                   \
+  _(preferred_vector_width_char)                                               \
+  _(preferred_vector_width_short)                                              \
+  _(preferred_vector_width_int)                                                \
+  _(preferred_vector_width_long)                                               \
+  _(preferred_vector_width_float)                                              \
+  _(preferred_vector_width_double)                                             \
+  _(preferred_vector_width_half)                                               \
+                                                                               \
+  /* the native ISA vector width. The vector width is defined as the number of \
+   * scalar elements that can be stored in the vector. */                      \
+  _(native_vector_width_char)                                                  \
+  _(native_vector_width_short)                                                 \
+  _(native_vector_width_int)                                                   \
+  _(native_vector_width_long)                                                  \
+  _(native_vector_width_float)                                                 \
+  _(native_vector_width_double)                                                \
+  _(native_vector_width_half)
+
+#define AT_FORALL_XPU_EXT_DEVICE_PROPERTIES(_)           \
+  /* the number of EUs associated with the Intel GPU. */ \
+  _(gpu_eu_count, 512)                                   \
+                                                         \
+  /* the number of EUs in a subslice. */                 \
+  _(gpu_eu_count_per_subslice, 8)                        \
+                                                         \
+  /* the simd width of EU of GPU. */                     \
+  _(gpu_eu_simd_width, 8)                                \
+                                                         \
+  /* the number of hardware threads per EU of GPU. */    \
+  _(gpu_hw_threads_per_eu, 8)
+
+#define AT_FORALL_XPU_DEVICE_ASPECT(_)                  \
+  /* sycl::half is supported on device. */              \
+  _(fp16)                                               \
+                                                        \
+  /* double is supported on device. */                  \
+  _(fp64)                                               \
+                                                        \
+  /* 64-bit atomic operation is supported on device. */ \
+  _(atomic64)
+
+#define AT_FORALL_XPU_EXP_CL_ASPECT(_)                                         \
+  /* conversion between single-precision 32-bit floating-point values and      \
+   * 16-bit bfloat16 values is supported on device. */                         \
+  _(bfloat16_conversions)                                                      \
+                                                                               \
+  /* specialized hardware to compute MMA is supported on device. */            \
+  _(subgroup_matrix_multiply_accumulate)                                       \
+                                                                               \
+  /* specialized hardware to compute MMA for 32-bit floating-point is          \
+   * supported on device. */                                                   \
+  _(subgroup_matrix_multiply_accumulate_tensor_float32)                        \
+                                                                               \
+  /* block read operations for efficient matrix multiplication is supported on \
+   * device. */                                                                \
+  _(subgroup_2d_block_io)
+
+#define AT_FORALL_XPU_EXP_DEVICE_PROPERTIES(_)       \
+  /* the device architecture of this SYCL device. */ \
+  _(architecture)
+
+#define _DEFINE_SYCL_PROP(ns, property, member) \
+  ns::property::return_type member;
+
+#define DEFINE_DEVICE_PROP(property) \
+  _DEFINE_SYCL_PROP(sycl::info::device, property, property)
+
+#define DEFINE_PLATFORM_PROP(property, member) \
+  _DEFINE_SYCL_PROP(sycl::info::platform, property, member)
+
+#define DEFINE_EXT_DEVICE_PROP(property, ...) \
+  _DEFINE_SYCL_PROP(sycl::ext::intel::info::device, property, property)
+
+#define DEFINE_DEVICE_ASPECT(member) bool has_##member;
+
+#define DEFINE_EXP_DEVICE_PROP(property) \
+  _DEFINE_SYCL_PROP(                     \
+      sycl::ext::oneapi::experimental::info::device, property, property)
+
+struct C10_XPU_API DeviceProp {
+  AT_FORALL_XPU_DEVICE_PROPERTIES(DEFINE_DEVICE_PROP);
+
+  // the platform name.
+  DEFINE_PLATFORM_PROP(name, platform_name);
+
+  AT_FORALL_XPU_EXT_DEVICE_PROPERTIES(DEFINE_EXT_DEVICE_PROP);
+
+  AT_FORALL_XPU_DEVICE_ASPECT(DEFINE_DEVICE_ASPECT);
+
+  AT_FORALL_XPU_EXP_CL_ASPECT(DEFINE_DEVICE_ASPECT);
+
+#if SYCL_COMPILER_VERSION >= 20250000
+  AT_FORALL_XPU_EXP_DEVICE_PROPERTIES(DEFINE_EXP_DEVICE_PROP);
+#endif
+};
+
+#undef _DEFINE_SYCL_PROP
+#undef DEFINE_DEVICE_PROP
+#undef DEFINE_PLATFORM_PROP
+#undef DEFINE_EXT_DEVICE_PROP
+#undef DEFINE_DEVICE_ASPECT
+#undef DEFINE_EXP_DEVICE_PROP
+
+} // namespace c10::xpu
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUException.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUException.h
new file mode 100644
index 0000000000000000000000000000000000000000..9bc64ec3f39b7b6afed238a2b078a2ef4e6952f9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUException.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <sycl/sycl.hpp>
+
+namespace c10::xpu {
+
+static inline sycl::async_handler asyncHandler = [](sycl::exception_list el) {
+  if (el.size() == 0) {
+    return;
+  }
+  for (const auto& e : el) {
+    try {
+      std::rethrow_exception(e);
+    } catch (sycl::exception& e) {
+      TORCH_WARN("SYCL Exception: ", e.what());
+    }
+  }
+  throw;
+};
+
+} // namespace c10::xpu
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUFunctions.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..a205db0d5ebdaa3f2816ef96be76f1374c5beb22
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUFunctions.h
@@ -0,0 +1,45 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/xpu/XPUDeviceProp.h>
+#include <c10/xpu/XPUMacros.h>
+
+// The naming convention used here matches the naming convention of torch.xpu
+
+namespace c10::xpu {
+
+// Log a warning only once if no devices are detected.
+C10_XPU_API DeviceIndex device_count();
+
+// Throws an error if no devices are detected.
+C10_XPU_API DeviceIndex device_count_ensure_non_zero();
+
+C10_XPU_API DeviceIndex current_device();
+
+C10_XPU_API void set_device(DeviceIndex device);
+
+C10_XPU_API DeviceIndex exchange_device(DeviceIndex device);
+
+C10_XPU_API DeviceIndex maybe_exchange_device(DeviceIndex to_device);
+
+C10_XPU_API sycl::device& get_raw_device(DeviceIndex device);
+
+C10_XPU_API sycl::context& get_device_context();
+
+C10_XPU_API void get_device_properties(
+    DeviceProp* device_prop,
+    DeviceIndex device);
+
+C10_XPU_API DeviceIndex get_device_idx_from_pointer(void* ptr);
+
+static inline void check_device_index(DeviceIndex device) {
+  TORCH_CHECK(
+      device >= 0 && device < c10::xpu::device_count(),
+      "device is out of range, device is ",
+      static_cast<int>(device),
+      ", total number of device is ",
+      static_cast<int>(c10::xpu::device_count()),
+      ".");
+}
+
+} // namespace c10::xpu
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUMacros.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUMacros.h
new file mode 100644
index 0000000000000000000000000000000000000000..d51eab989d25d8e76fdd330814f4a74decc7f52d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUMacros.h
@@ -0,0 +1,33 @@
+#pragma once
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+#include <c10/xpu/impl/xpu_cmake_macros.h>
+#endif
+
+// See c10/macros/Export.h for a detailed explanation of what the function
+// of these macros are.  We need one set of macros for every separate library
+// we build.
+
+#ifdef _WIN32
+#if defined(C10_XPU_BUILD_SHARED_LIBS)
+#define C10_XPU_EXPORT __declspec(dllexport)
+#define C10_XPU_IMPORT __declspec(dllimport)
+#else
+#define C10_XPU_EXPORT
+#define C10_XPU_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_XPU_EXPORT __attribute__((__visibility__("default")))
+#else // defined(__GNUC__)
+#define C10_XPU_EXPORT
+#endif // defined(__GNUC__)
+#define C10_XPU_IMPORT C10_XPU_EXPORT
+#endif // _WIN32
+
+// This one is being used by libc10_xpu.so
+#ifdef C10_XPU_BUILD_MAIN_LIB
+#define C10_XPU_API C10_XPU_EXPORT
+#else
+#define C10_XPU_API C10_XPU_IMPORT
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/XPUStream.h b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUStream.h
new file mode 100644
index 0000000000000000000000000000000000000000..987800b1299127aea9e965402b5a37014b5b6c4b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/XPUStream.h
@@ -0,0 +1,199 @@
+#pragma once
+
+#include <c10/core/Stream.h>
+#include <c10/core/impl/GPUTrace.h>
+#include <c10/xpu/XPUFunctions.h>
+
+namespace c10::xpu {
+
+/*
+ * Note [Stream Management]
+ *
+ * An XPUStream is an abstraction of an actual SYCL queue in which SYCL kernel
+ * can execute. Currently, there are several pools per device to manage SYCL
+ * queue, and a device's pool is lazily created.
+ *
+ * There are two pools per device. The first pool contains "normal priority"
+ * queues. The second pool is the "high priority" queues. There are 32 queues in
+ * per pool per device, and when a queue is requested one of these queues is
+ * returned round-robin. That is, the first queue requested is at index 0, the
+ * second at index 1... to index 31, then index 0 again.
+ *
+ * This means that if 33 queues are requested, the first and last queues
+ * requested are actually the same queue (under the covers) and kernels enqueued
+ * on them cannot run concurrently.
+ *
+ * It is safe to enqueue a kernel on the same queue from two different
+ * threads as the SYCL specification described.
+ */
+
+static constexpr int max_compile_time_stream_priorities = 2;
+
+/*
+ * This serves as a wrapper around c10::Stream and acts as a representation for
+ * a SYCL queue, which allows asynchronous execution of XPU tasks.
+ */
+class C10_XPU_API XPUStream {
+ public:
+  enum Unchecked { UNCHECKED };
+
+  /// Construct a XPUStream from a Stream. This construction is checked, and
+  /// will raise an error if the Stream is not, in fact, a XPU stream.
+  explicit XPUStream(Stream stream) : stream_(stream) {
+    TORCH_CHECK(stream_.device_type() == DeviceType::XPU);
+  }
+
+  /// Construct a XPUStream from a Stream with no error checking.
+  explicit XPUStream(Unchecked, Stream stream) : stream_(stream) {}
+
+  bool operator==(const XPUStream& other) const noexcept {
+    return unwrap() == other.unwrap();
+  }
+
+  bool operator!=(const XPUStream& other) const noexcept {
+    return unwrap() != other.unwrap();
+  }
+
+  /// Implicit conversion to sycl::queue&.
+  operator sycl::queue&() const {
+    return queue();
+  }
+
+  /// Implicit conversion to Stream (a.k.a., forget that the stream is a
+  /// XPU stream).
+  operator Stream() const {
+    return unwrap();
+  }
+
+  /// Get the XPU device type that this stream is associated with.
+  DeviceType device_type() const {
+    return DeviceType::XPU;
+  }
+
+  /// Get the XPU device index that this stream is associated with.
+  DeviceIndex device_index() const {
+    return stream_.device_index();
+  }
+
+  /// Get the full Device that this stream is associated with. The Device is
+  /// guaranteed to be a XPU device.
+  Device device() const {
+    return Device(DeviceType::XPU, device_index());
+  }
+
+  /// Return the stream ID corresponding to this particular stream. StreamId is
+  /// a int64_t representation generated by its type and index.
+  StreamId id() const {
+    return stream_.id();
+  }
+
+  /// Return true if all enqueued tasks in this stream have been completed,
+  /// otherwise return false.
+  bool query() const {
+    return queue().ext_oneapi_empty();
+  }
+
+  /// Performs a blocking wait for the completion of all enqueued tasks in this
+  /// stream.
+  void synchronize() const {
+    queue().wait_and_throw();
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_stream_synchronization(
+          c10::kXPU, reinterpret_cast<uintptr_t>(&queue()));
+    }
+  }
+
+  /// Return the priority that this stream is associated with. Lower numbers
+  /// represent higher priority.
+  int priority() const;
+
+  /// Explicit conversion to sycl::queue&.
+  sycl::queue& queue() const;
+
+  /// Explicit conversion to Stream.
+  Stream unwrap() const {
+    return stream_;
+  }
+
+  /// Reversibly pack a XPUStream into a struct representation. The XPUStream
+  /// can be unpacked using unpack3().
+  struct c10::StreamData3 pack3() const {
+    return stream_.pack3();
+  }
+
+  /// Unpack a XPUStream from the 3 fields generated by pack3().
+  static XPUStream unpack3(
+      StreamId stream_id,
+      DeviceIndex device_index,
+      DeviceType device_type) {
+    return XPUStream(Stream::unpack3(stream_id, device_index, device_type));
+  }
+
+  /// Return the range of priority **supported by PyTorch**.
+  static std::tuple<int, int> priority_range() {
+    return std::make_tuple(0, -max_compile_time_stream_priorities + 1);
+  }
+
+ private:
+  Stream stream_;
+};
+
+/**
+ * Get a stream from the pool in a round-robin fashion.
+ *
+ * You can request a stream from the highest priority pool by setting
+ * isHighPriority to true for a specific device.
+ */
+C10_XPU_API XPUStream
+getStreamFromPool(const bool isHighPriority = false, DeviceIndex device = -1);
+
+/**
+ * Get a stream from the pool in a round-robin fashion.
+ *
+ * You can request a stream by setting a priority value for a specific device.
+ * The priority number lower, the priority higher.
+ */
+C10_XPU_API XPUStream
+getStreamFromPool(const int priority, DeviceIndex device = -1);
+
+/**
+ * Get a XPUStream from a externally allocated one.
+ *
+ * This is mainly for interoperability with different libraries where we
+ * want to operate on a non-torch allocated stream for data exchange or similar
+ * purposes
+ */
+C10_API XPUStream
+getStreamFromExternal(sycl::queue* ext_stream, DeviceIndex device_index);
+
+/**
+ * Get the current XPU stream, for the passed XPU device, or for the current
+ * device if no device index is passed.
+ */
+C10_XPU_API XPUStream getCurrentXPUStream(DeviceIndex device = -1);
+
+/**
+ * Set the current stream on the device of the passed in stream to be the passed
+ * in stream.
+ */
+C10_XPU_API void setCurrentXPUStream(XPUStream stream);
+
+C10_XPU_API std::ostream& operator<<(std::ostream& stream, const XPUStream& s);
+
+/**
+ * Block all reserved SYCL queues in the stream pools on the device, and wait
+ * for their synchronizations.
+ */
+C10_XPU_API void syncStreamsOnDevice(DeviceIndex device = -1);
+
+} // namespace c10::xpu
+
+namespace std {
+template <>
+struct hash<c10::xpu::XPUStream> {
+  size_t operator()(c10::xpu::XPUStream s) const noexcept {
+    return std::hash<c10::Stream>{}(s.unwrap());
+  }
+};
+} // namespace std
diff --git a/lib/python3.11/site-packages/torch/include/c10/xpu/impl/XPUGuardImpl.h b/lib/python3.11/site-packages/torch/include/c10/xpu/impl/XPUGuardImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..b646b21d99f1cb7decd2f9e768c33e54e4c8babc
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/c10/xpu/impl/XPUGuardImpl.h
@@ -0,0 +1,217 @@
+#pragma once
+
+#include <c10/core/DeviceGuard.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/GPUTrace.h>
+#include <c10/xpu/XPUCachingAllocator.h>
+#include <c10/xpu/XPUFunctions.h>
+#include <c10/xpu/XPUStream.h>
+
+#include <vector>
+
+namespace c10::xpu::impl {
+
+struct XPUGuardImpl final : public c10::impl::DeviceGuardImplInterface {
+  static constexpr DeviceType static_type = kXPU;
+
+  XPUGuardImpl() = default;
+
+  explicit XPUGuardImpl(DeviceType t) {
+    TORCH_INTERNAL_ASSERT(t == kXPU);
+  }
+
+  DeviceType type() const override {
+    return kXPU;
+  }
+
+  Device exchangeDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_xpu());
+    const auto old_device_index = c10::xpu::exchange_device(d.index());
+    return Device(kXPU, old_device_index);
+  }
+
+  Device getDevice() const override {
+    const auto device = c10::xpu::current_device();
+    return Device(kXPU, device);
+  }
+
+  void setDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_xpu());
+    c10::xpu::set_device(d.index());
+  }
+
+  void uncheckedSetDevice(Device d) const noexcept override {
+    c10::xpu::set_device(d.index());
+  }
+
+  Stream getStream(Device d) const noexcept override {
+    return getCurrentXPUStream(d.index()).unwrap();
+  }
+
+  Stream getNewStream(Device d, int priority = 0) const override {
+    return getStreamFromPool(priority, d.index());
+  }
+
+  Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false)
+      const override {
+    return getStreamFromPool(isHighPriority, d.index());
+  }
+
+  // NB: These do NOT set the current device
+  Stream exchangeStream(Stream s) const noexcept override {
+    const XPUStream stream(s);
+    const auto old_stream = getCurrentXPUStream(s.device().index());
+    setCurrentXPUStream(stream);
+    return old_stream.unwrap();
+  }
+
+  DeviceIndex deviceCount() const noexcept override {
+    return c10::xpu::device_count();
+  }
+
+  // Event-related functions
+  void destroyEvent(void* event, const DeviceIndex device_index)
+      const noexcept override {
+    if (!event)
+      return;
+
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_deletion(
+          c10::kXPU, reinterpret_cast<uintptr_t>(event));
+    }
+
+    delete reinterpret_cast<sycl::event*>(event);
+  }
+
+  void record(
+      void** event,
+      const Stream& stream,
+      const DeviceIndex device_index,
+      const EventFlag flag) const override {
+    TORCH_CHECK(
+        device_index == -1 || device_index == stream.device_index(),
+        "Event device index ",
+        device_index,
+        " does not match recording stream's device index ",
+        stream.device_index(),
+        ".");
+
+    auto* xpu_event = reinterpret_cast<sycl::event*>(*event);
+    const XPUStream xpu_stream{stream};
+
+    // Delete the event previously recorded.
+    if (xpu_event)
+      delete xpu_event;
+#if SYCL_COMPILER_VERSION >= 20250000
+    if (flag == EventFlag::BACKEND_DEFAULT) {
+      // Use the profiling tag to record the event to enable timing feature.
+      xpu_event =
+          new sycl::event(sycl::ext::oneapi::experimental::submit_profiling_tag(
+              xpu_stream.queue()));
+    } else {
+      xpu_event =
+          new sycl::event(xpu_stream.queue().ext_oneapi_submit_barrier());
+    }
+#else
+    xpu_event = new sycl::event(xpu_stream.queue().ext_oneapi_submit_barrier());
+#endif
+    *event = reinterpret_cast<void*>(xpu_event);
+
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_record(
+          c10::kXPU,
+          reinterpret_cast<uintptr_t>(xpu_event),
+          reinterpret_cast<uintptr_t>(&xpu_stream.queue()));
+    }
+  }
+
+  void block(void* event, const Stream& stream) const override {
+    if (!event)
+      return;
+    auto* xpu_event = reinterpret_cast<sycl::event*>(event);
+    std::vector<sycl::event> event_list{*xpu_event};
+    const XPUStream xpu_stream(stream);
+    xpu_stream.queue().ext_oneapi_submit_barrier(event_list);
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_wait(
+          c10::kXPU,
+          reinterpret_cast<uintptr_t>(xpu_event),
+          reinterpret_cast<uintptr_t>(&xpu_stream.queue()));
+    }
+  }
+
+  bool queryEvent(void* event) const override {
+    using namespace sycl::info;
+    if (!event)
+      return true;
+    auto* xpu_event = reinterpret_cast<sycl::event*>(event);
+    return xpu_event->get_info<event::command_execution_status>() ==
+        event_command_status::complete;
+  }
+
+  double elapsedTime(
+      void* start_event,
+      void* end_event,
+      const DeviceIndex device_index) const override {
+#if SYCL_COMPILER_VERSION < 20250000
+    TORCH_CHECK_NOT_IMPLEMENTED(
+        false,
+        "elapsedTime requires PyTorch to be built with SYCL compiler version 2025.0.0 or newer.");
+#endif
+    TORCH_CHECK(
+        start_event && end_event,
+        "Both events must be recorded before calculating elapsed time.");
+    auto* xpu_start_event = reinterpret_cast<sycl::event*>(start_event);
+    auto* xpu_end_event = reinterpret_cast<sycl::event*>(end_event);
+
+    using namespace sycl::info::event_profiling;
+    // Block until both of the recorded events are completed.
+    uint64_t end_time_ns = xpu_end_event->get_profiling_info<command_end>();
+    uint64_t start_time_ns = xpu_start_event->get_profiling_info<command_end>();
+    // Return the eplased time in milliseconds.
+    return 1e-6 *
+        (static_cast<double>(end_time_ns) - static_cast<double>(start_time_ns));
+  }
+
+  // Stream-related functions
+  bool queryStream(const Stream& stream) const override {
+    const XPUStream xpu_stream{stream};
+    return xpu_stream.query();
+  }
+
+  void synchronizeStream(const Stream& stream) const override {
+    const XPUStream xpu_stream{stream};
+    xpu_stream.synchronize();
+  }
+
+  void synchronizeEvent(void* event) const override {
+    if (!event)
+      return;
+    auto* xpu_event = reinterpret_cast<sycl::event*>(event);
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_synchronization(
+          c10::kXPU, reinterpret_cast<uintptr_t>(xpu_event));
+    }
+    xpu_event->wait_and_throw();
+  }
+
+  void synchronizeDevice(const c10::DeviceIndex device_index) const override {
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_device_synchronization(c10::kXPU);
+    }
+    c10::xpu::syncStreamsOnDevice(device_index);
+  }
+
+  void recordDataPtrOnStream(const c10::DataPtr& data_ptr, const Stream& stream)
+      const override {
+    const XPUStream xpu_stream{stream};
+    XPUCachingAllocator::recordStream(data_ptr, xpu_stream);
+  }
+};
+
+} // namespace c10::xpu::impl
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/crc_alt.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/crc_alt.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d1c4f1dc7ddc8997f7cc1297ef20d74de67afe0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/crc_alt.h
@@ -0,0 +1,1343 @@
+#pragma once
+
+// //////////////////////////////////////////////////////////
+// Crc32.h
+// Copyright (c) 2011-2019 Stephan Brumme. All rights reserved.
+// Slicing-by-16 contributed by Bulat Ziganshin
+// Tableless bytewise CRC contributed by Hagai Gold
+// see http://create.stephan-brumme.com/disclaimer.html
+//
+
+// if running on an embedded system, you might consider shrinking the
+// big Crc32Lookup table by undefining these lines:
+#define CRC32_USE_LOOKUP_TABLE_BYTE
+#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_4
+#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_8
+#define CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+// - crc32_bitwise  doesn't need it at all
+// - crc32_halfbyte has its own small lookup table
+// - crc32_1byte_tableless and crc32_1byte_tableless2 don't need it at all
+// - crc32_1byte    needs only Crc32Lookup[0]
+// - crc32_4bytes   needs only Crc32Lookup[0..3]
+// - crc32_8bytes   needs only Crc32Lookup[0..7]
+// - crc32_4x8bytes needs only Crc32Lookup[0..7]
+// - crc32_16bytes  needs all of Crc32Lookup
+// using the aforementioned #defines the table is automatically fitted to your needs
+
+// uint8_t, uint32_t, int32_t
+#include <stdint.h>
+// size_t
+#include <cstddef>
+
+// crc32_fast selects the fastest algorithm depending on flags (CRC32_USE_LOOKUP_...)
+/// compute CRC32 using the fastest algorithm for large datasets on modern CPUs
+uint32_t crc32_fast    (const void* data, size_t length, uint32_t previousCrc32 = 0);
+
+/// merge two CRC32 such that result = crc32(dataB, lengthB, crc32(dataA, lengthA))
+uint32_t crc32_combine (uint32_t crcA, uint32_t crcB, size_t lengthB);
+
+/// compute CRC32 (bitwise algorithm)
+uint32_t crc32_bitwise (const void* data, size_t length, uint32_t previousCrc32 = 0);
+/// compute CRC32 (half-byte algoritm)
+uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32 = 0);
+
+#ifdef CRC32_USE_LOOKUP_TABLE_BYTE
+/// compute CRC32 (standard algorithm)
+uint32_t crc32_1byte   (const void* data, size_t length, uint32_t previousCrc32 = 0);
+#endif
+
+/// compute CRC32 (byte algorithm) without lookup tables
+uint32_t crc32_1byte_tableless (const void* data, size_t length, uint32_t previousCrc32 = 0);
+/// compute CRC32 (byte algorithm) without lookup tables
+uint32_t crc32_1byte_tableless2(const void* data, size_t length, uint32_t previousCrc32 = 0);
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_4
+/// compute CRC32 (Slicing-by-4 algorithm)
+uint32_t crc32_4bytes  (const void* data, size_t length, uint32_t previousCrc32 = 0);
+#endif
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_8
+/// compute CRC32 (Slicing-by-8 algorithm)
+uint32_t crc32_8bytes  (const void* data, size_t length, uint32_t previousCrc32 = 0);
+/// compute CRC32 (Slicing-by-8 algorithm), unroll inner loop 4 times
+uint32_t crc32_4x8bytes(const void* data, size_t length, uint32_t previousCrc32 = 0);
+#endif
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+/// compute CRC32 (Slicing-by-16 algorithm)
+uint32_t crc32_16bytes (const void* data, size_t length, uint32_t previousCrc32 = 0);
+/// compute CRC32 (Slicing-by-16 algorithm, prefetch upcoming data blocks)
+uint32_t crc32_16bytes_prefetch(const void* data, size_t length, uint32_t previousCrc32 = 0, size_t prefetchAhead = 256);
+#endif
+
+// //////////////////////////////////////////////////////////
+// Crc32.cpp
+// Copyright (c) 2011-2019 Stephan Brumme. All rights reserved.
+// Slicing-by-16 contributed by Bulat Ziganshin
+// Tableless bytewise CRC contributed by Hagai Gold
+// see http://create.stephan-brumme.com/disclaimer.html
+//
+
+// if running on an embedded system, you might consider shrinking the
+// big Crc32Lookup table:
+// - crc32_bitwise  doesn't need it at all
+// - crc32_halfbyte has its own small lookup table
+// - crc32_1byte    needs only Crc32Lookup[0]
+// - crc32_4bytes   needs only Crc32Lookup[0..3]
+// - crc32_8bytes   needs only Crc32Lookup[0..7]
+// - crc32_4x8bytes needs only Crc32Lookup[0..7]
+// - crc32_16bytes  needs all of Crc32Lookup
+
+
+#ifndef __LITTLE_ENDIAN
+  #define __LITTLE_ENDIAN 1234
+#endif
+#ifndef __BIG_ENDIAN
+  #define __BIG_ENDIAN    4321
+#endif
+
+// define endianess and some integer data types
+#if defined(_MSC_VER) || defined(__MINGW32__)
+  // Windows always little endian
+  #define __BYTE_ORDER __LITTLE_ENDIAN
+
+  // intrinsics / prefetching
+  #if defined(_M_ARM64)
+    #include <intrin.h>
+  #else
+    #include <xmmintrin.h>
+  #endif
+
+  #ifdef __MINGW32__
+    #define PREFETCH(location) __builtin_prefetch(location)
+  #else
+    #if defined(_M_ARM64)
+      #define PREFETCH(location) __prefetch(location)
+    #else
+      #define PREFETCH(location) _mm_prefetch(location, _MM_HINT_T0)
+    #endif
+  #endif
+#elif defined(__APPLE__)
+  #include <TargetConditionals.h>
+    #if TARGET_IPHONE_SIMULATOR
+      #define __BYTE_ORDER __LITTLE_ENDIAN
+    #elif TARGET_OS_IPHONE
+      #define __BYTE_ORDER __LITTLE_ENDIAN
+    #elif TARGET_OS_MAC
+      #include <machine/endian.h>
+      #if defined(__BIG_ENDIAN__)
+          #define __BYTE_ORDER __BIG_ENDIAN
+      #endif
+      #if defined(__LITTLE_ENDIAN__)
+        #define __BYTE_ORDER __LITTLE_ENDIAN
+      #endif
+    #else
+      # error "Unknown Apple platform"
+    #endif
+#elif defined(__ARMEB__)
+  #define __BYTE_ORDER __BIG_ENDIAN
+#elif (defined(__BYTE_ORDER__) and !defined(__BYTE_ORDER))
+    #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+        #define __BYTE_ORDER __BIG_ENDIAN
+    #else
+        #define __BYTE_ORDER __LITTLE_ENDIAN
+    #endif
+#else
+  // defines __BYTE_ORDER as __LITTLE_ENDIAN or __BIG_ENDIAN
+  #include <sys/param.h>
+#endif
+
+// intrinsics / prefetching
+#ifdef __GNUC__
+  #define PREFETCH(location) __builtin_prefetch(location)
+#else
+#ifndef PREFETCH
+  // no prefetching
+  #define PREFETCH(location) ;
+#endif
+#endif
+
+// abort if byte order is undefined
+#ifndef __BYTE_ORDER
+#error undefined byte order, compile with -D__BYTE_ORDER=1234 (if little endian) or -D__BYTE_ORDER=4321 (big endian)
+#endif
+
+
+namespace
+{
+  /// zlib's CRC32 polynomial
+  const uint32_t Polynomial = 0xEDB88320;
+
+  /// swap endianess
+  static inline uint32_t swap(uint32_t x)
+  {
+  #if defined(__GNUC__) || defined(__clang__)
+    return __builtin_bswap32(x);
+  #else
+    return (x >> 24) |
+          ((x >>  8) & 0x0000FF00) |
+          ((x <<  8) & 0x00FF0000) |
+           (x << 24);
+  #endif
+  }
+
+  /// Slicing-By-16
+  #ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+  const size_t MaxSlice = 16;
+  #elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8)
+  const size_t MaxSlice = 8;
+  #elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4)
+  const size_t MaxSlice = 4;
+  #elif defined(CRC32_USE_LOOKUP_TABLE_BYTE)
+  const size_t MaxSlice = 1;
+  #else
+    #define NO_LUT // don't need Crc32Lookup at all
+  #endif
+
+} // anonymous namespace
+
+#ifndef NO_LUT
+/// forward declaration, table is at the end of this file
+extern const uint32_t Crc32Lookup[MaxSlice][256]; // extern is needed to keep compiler happy
+#endif
+
+
+/// compute CRC32 (bitwise algorithm)
+uint32_t crc32_bitwise(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint8_t* current = (const uint8_t*) data;
+
+  while (length-- != 0)
+  {
+    crc ^= *current++;
+
+    for (int j = 0; j < 8; j++)
+    {
+      // branch-free
+      crc = (crc >> 1) ^ (-int32_t(crc & 1) & Polynomial);
+
+      // branching, much slower:
+      //if (crc & 1)
+      //  crc = (crc >> 1) ^ Polynomial;
+      //else
+      //  crc =  crc >> 1;
+    }
+  }
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+/// compute CRC32 (half-byte algoritm)
+uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint8_t* current = (const uint8_t*) data;
+
+  /// look-up table for half-byte, same as crc32Lookup[0][16*i]
+  static const uint32_t Crc32Lookup16[16] =
+  {
+    0x00000000,0x1DB71064,0x3B6E20C8,0x26D930AC,0x76DC4190,0x6B6B51F4,0x4DB26158,0x5005713C,
+    0xEDB88320,0xF00F9344,0xD6D6A3E8,0xCB61B38C,0x9B64C2B0,0x86D3D2D4,0xA00AE278,0xBDBDF21C
+  };
+
+  while (length-- != 0)
+  {
+    crc = Crc32Lookup16[(crc ^  *current      ) & 0x0F] ^ (crc >> 4);
+    crc = Crc32Lookup16[(crc ^ (*current >> 4)) & 0x0F] ^ (crc >> 4);
+    current++;
+  }
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+#ifdef CRC32_USE_LOOKUP_TABLE_BYTE
+/// compute CRC32 (standard algorithm)
+uint32_t crc32_1byte(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint8_t* current = (const uint8_t*) data;
+
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *current++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+#endif
+
+
+/// compute CRC32 (byte algorithm) without lookup tables
+uint32_t crc32_1byte_tableless(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint8_t* current = (const uint8_t*) data;
+
+  while (length-- != 0)
+  {
+    uint8_t s = uint8_t(crc) ^ *current++;
+
+    // Hagai Gold made me aware of this table-less algorithm and send me code
+
+    // polynomial 0xEDB88320 can be written in binary as 11101101101110001000001100100000b
+    // reverse the bits (or just assume bit 0 is the first one)
+    // and we have bits set at position 0, 1, 2, 4, 5, 7, 8, 10, 11, 12, 16, 22, 23, 26
+    // => those are the shift offsets:
+    //crc = (crc >> 8) ^
+    //       t ^
+    //      (t >>  1) ^ (t >>  2) ^ (t >>  4) ^ (t >>  5) ^  // == y
+    //      (t >>  7) ^ (t >>  8) ^ (t >> 10) ^ (t >> 11) ^  // == y >> 6
+    //      (t >> 12) ^ (t >> 16) ^                          // == z
+    //      (t >> 22) ^ (t >> 26) ^                          // == z >> 10
+    //      (t >> 23);
+
+    // the fastest I can come up with:
+    uint32_t low = (s ^ (s << 6)) & 0xFF;
+    uint32_t a   = (low * ((1 << 23) + (1 << 14) + (1 << 2)));
+    crc = (crc >> 8) ^
+          (low * ((1 << 24) + (1 << 16) + (1 << 8))) ^
+           a ^
+          (a >> 1) ^
+          (low * ((1 << 20) + (1 << 12)           )) ^
+          (low << 19) ^
+          (low << 17) ^
+          (low >>  2);
+
+    // Hagai's code:
+    /*uint32_t t = (s ^ (s << 6)) << 24;
+    // some temporaries to optimize XOR
+    uint32_t x = (t >> 1) ^ (t >> 2);
+    uint32_t y = x ^ (x >> 3);
+    uint32_t z = (t >> 12) ^ (t >> 16);
+    crc = (crc >> 8) ^
+           t ^ (t >> 23) ^
+           y ^ (y >>  6) ^
+           z ^ (z >> 10);*/
+  }
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+/// compute CRC32 (byte algorithm) without lookup tables
+uint32_t crc32_1byte_tableless2(const void* data, size_t length, uint32_t previousCrc32)
+{
+  int32_t crc = ~previousCrc32; // note: signed integer, right shift distributes sign bit into lower bits
+  const uint8_t* current = (const uint8_t*) data;
+
+  while (length-- != 0)
+  {
+    crc = crc ^ *current++;
+
+    uint32_t c = (((crc << 31) >> 31) & ((Polynomial >> 7)  ^ (Polynomial >> 1))) ^
+                 (((crc << 30) >> 31) & ((Polynomial >> 6)  ^  Polynomial)) ^
+                 (((crc << 29) >> 31) &  (Polynomial >> 5)) ^
+                 (((crc << 28) >> 31) &  (Polynomial >> 4)) ^
+                 (((crc << 27) >> 31) &  (Polynomial >> 3)) ^
+                 (((crc << 26) >> 31) &  (Polynomial >> 2)) ^
+                 (((crc << 25) >> 31) &  (Polynomial >> 1)) ^
+                 (((crc << 24) >> 31) &   Polynomial);
+
+    crc = ((uint32_t)crc >> 8) ^ c; // convert to unsigned integer before right shift
+  }
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_4
+/// compute CRC32 (Slicing-by-4 algorithm)
+uint32_t crc32_4bytes(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t  crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint32_t* current = (const uint32_t*) data;
+
+  // process four bytes at once (Slicing-by-4)
+  while (length >= 4)
+  {
+#if __BYTE_ORDER == __BIG_ENDIAN
+    uint32_t one = *current++ ^ swap(crc);
+    crc = Crc32Lookup[0][ one      & 0xFF] ^
+          Crc32Lookup[1][(one>> 8) & 0xFF] ^
+          Crc32Lookup[2][(one>>16) & 0xFF] ^
+          Crc32Lookup[3][(one>>24) & 0xFF];
+#else
+    uint32_t one = *current++ ^ crc;
+    crc = Crc32Lookup[0][(one>>24) & 0xFF] ^
+          Crc32Lookup[1][(one>>16) & 0xFF] ^
+          Crc32Lookup[2][(one>> 8) & 0xFF] ^
+          Crc32Lookup[3][ one      & 0xFF];
+#endif
+
+    length -= 4;
+  }
+
+  const uint8_t* currentChar = (const uint8_t*) current;
+  // remaining 1 to 3 bytes (standard algorithm)
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+#endif
+
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_8
+/// compute CRC32 (Slicing-by-8 algorithm)
+uint32_t crc32_8bytes(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint32_t* current = (const uint32_t*) data;
+
+  // process eight bytes at once (Slicing-by-8)
+  while (length >= 8)
+  {
+#if __BYTE_ORDER == __BIG_ENDIAN
+    uint32_t one = *current++ ^ swap(crc);
+    uint32_t two = *current++;
+    crc = Crc32Lookup[0][ two      & 0xFF] ^
+          Crc32Lookup[1][(two>> 8) & 0xFF] ^
+          Crc32Lookup[2][(two>>16) & 0xFF] ^
+          Crc32Lookup[3][(two>>24) & 0xFF] ^
+          Crc32Lookup[4][ one      & 0xFF] ^
+          Crc32Lookup[5][(one>> 8) & 0xFF] ^
+          Crc32Lookup[6][(one>>16) & 0xFF] ^
+          Crc32Lookup[7][(one>>24) & 0xFF];
+#else
+    uint32_t one = *current++ ^ crc;
+    uint32_t two = *current++;
+    crc = Crc32Lookup[0][(two>>24) & 0xFF] ^
+          Crc32Lookup[1][(two>>16) & 0xFF] ^
+          Crc32Lookup[2][(two>> 8) & 0xFF] ^
+          Crc32Lookup[3][ two      & 0xFF] ^
+          Crc32Lookup[4][(one>>24) & 0xFF] ^
+          Crc32Lookup[5][(one>>16) & 0xFF] ^
+          Crc32Lookup[6][(one>> 8) & 0xFF] ^
+          Crc32Lookup[7][ one      & 0xFF];
+#endif
+
+    length -= 8;
+  }
+
+  const uint8_t* currentChar = (const uint8_t*) current;
+  // remaining 1 to 7 bytes (standard algorithm)
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+/// compute CRC32 (Slicing-by-8 algorithm), unroll inner loop 4 times
+uint32_t crc32_4x8bytes(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint32_t* current = (const uint32_t*) data;
+
+  // enabling optimization (at least -O2) automatically unrolls the inner for-loop
+  const size_t Unroll = 4;
+  const size_t BytesAtOnce = 8 * Unroll;
+
+  // process 4x eight bytes at once (Slicing-by-8)
+  while (length >= BytesAtOnce)
+  {
+    for (size_t unrolling = 0; unrolling < Unroll; unrolling++)
+    {
+#if __BYTE_ORDER == __BIG_ENDIAN
+      uint32_t one = *current++ ^ swap(crc);
+      uint32_t two = *current++;
+      crc = Crc32Lookup[0][ two      & 0xFF] ^
+            Crc32Lookup[1][(two>> 8) & 0xFF] ^
+            Crc32Lookup[2][(two>>16) & 0xFF] ^
+            Crc32Lookup[3][(two>>24) & 0xFF] ^
+            Crc32Lookup[4][ one      & 0xFF] ^
+            Crc32Lookup[5][(one>> 8) & 0xFF] ^
+            Crc32Lookup[6][(one>>16) & 0xFF] ^
+            Crc32Lookup[7][(one>>24) & 0xFF];
+#else
+      uint32_t one = *current++ ^ crc;
+      uint32_t two = *current++;
+      crc = Crc32Lookup[0][(two>>24) & 0xFF] ^
+            Crc32Lookup[1][(two>>16) & 0xFF] ^
+            Crc32Lookup[2][(two>> 8) & 0xFF] ^
+            Crc32Lookup[3][ two      & 0xFF] ^
+            Crc32Lookup[4][(one>>24) & 0xFF] ^
+            Crc32Lookup[5][(one>>16) & 0xFF] ^
+            Crc32Lookup[6][(one>> 8) & 0xFF] ^
+            Crc32Lookup[7][ one      & 0xFF];
+#endif
+
+    }
+
+    length -= BytesAtOnce;
+  }
+
+  const uint8_t* currentChar = (const uint8_t*) current;
+  // remaining 1 to 31 bytes (standard algorithm)
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_8
+
+
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+/// compute CRC32 (Slicing-by-16 algorithm)
+uint32_t crc32_16bytes(const void* data, size_t length, uint32_t previousCrc32)
+{
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint32_t* current = (const uint32_t*) data;
+
+  // enabling optimization (at least -O2) automatically unrolls the inner for-loop
+  const size_t Unroll = 4;
+  const size_t BytesAtOnce = 16 * Unroll;
+
+  while (length >= BytesAtOnce)
+  {
+    for (size_t unrolling = 0; unrolling < Unroll; unrolling++)
+    {
+#if __BYTE_ORDER == __BIG_ENDIAN
+    uint32_t one   = *current++ ^ swap(crc);
+    uint32_t two   = *current++;
+    uint32_t three = *current++;
+    uint32_t four  = *current++;
+    crc  = Crc32Lookup[ 0][ four         & 0xFF] ^
+           Crc32Lookup[ 1][(four  >>  8) & 0xFF] ^
+           Crc32Lookup[ 2][(four  >> 16) & 0xFF] ^
+           Crc32Lookup[ 3][(four  >> 24) & 0xFF] ^
+           Crc32Lookup[ 4][ three        & 0xFF] ^
+           Crc32Lookup[ 5][(three >>  8) & 0xFF] ^
+           Crc32Lookup[ 6][(three >> 16) & 0xFF] ^
+           Crc32Lookup[ 7][(three >> 24) & 0xFF] ^
+           Crc32Lookup[ 8][ two          & 0xFF] ^
+           Crc32Lookup[ 9][(two   >>  8) & 0xFF] ^
+           Crc32Lookup[10][(two   >> 16) & 0xFF] ^
+           Crc32Lookup[11][(two   >> 24) & 0xFF] ^
+           Crc32Lookup[12][ one          & 0xFF] ^
+           Crc32Lookup[13][(one   >>  8) & 0xFF] ^
+           Crc32Lookup[14][(one   >> 16) & 0xFF] ^
+           Crc32Lookup[15][(one   >> 24) & 0xFF];
+#else
+    uint32_t one   = *current++ ^ crc;
+    uint32_t two   = *current++;
+    uint32_t three = *current++;
+    uint32_t four  = *current++;
+    crc  = Crc32Lookup[ 0][(four  >> 24) & 0xFF] ^
+           Crc32Lookup[ 1][(four  >> 16) & 0xFF] ^
+           Crc32Lookup[ 2][(four  >>  8) & 0xFF] ^
+           Crc32Lookup[ 3][ four         & 0xFF] ^
+           Crc32Lookup[ 4][(three >> 24) & 0xFF] ^
+           Crc32Lookup[ 5][(three >> 16) & 0xFF] ^
+           Crc32Lookup[ 6][(three >>  8) & 0xFF] ^
+           Crc32Lookup[ 7][ three        & 0xFF] ^
+           Crc32Lookup[ 8][(two   >> 24) & 0xFF] ^
+           Crc32Lookup[ 9][(two   >> 16) & 0xFF] ^
+           Crc32Lookup[10][(two   >>  8) & 0xFF] ^
+           Crc32Lookup[11][ two          & 0xFF] ^
+           Crc32Lookup[12][(one   >> 24) & 0xFF] ^
+           Crc32Lookup[13][(one   >> 16) & 0xFF] ^
+           Crc32Lookup[14][(one   >>  8) & 0xFF] ^
+           Crc32Lookup[15][ one          & 0xFF];
+#endif
+    }
+
+    length -= BytesAtOnce;
+  }
+
+  const uint8_t* currentChar = (const uint8_t*) current;
+  // remaining 1 to 63 bytes (standard algorithm)
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+
+
+/// compute CRC32 (Slicing-by-16 algorithm, prefetch upcoming data blocks)
+uint32_t crc32_16bytes_prefetch(const void* data, size_t length, uint32_t previousCrc32, size_t prefetchAhead)
+{
+  // CRC code is identical to crc32_16bytes (including unrolling), only added prefetching
+  // 256 bytes look-ahead seems to be the sweet spot on Core i7 CPUs
+
+  uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
+  const uint32_t* current = (const uint32_t*) data;
+
+  // enabling optimization (at least -O2) automatically unrolls the for-loop
+  const size_t Unroll = 4;
+  const size_t BytesAtOnce = 16 * Unroll;
+
+  while (length >= BytesAtOnce + prefetchAhead)
+  {
+    PREFETCH(((const char*) current) + prefetchAhead);
+
+    for (size_t unrolling = 0; unrolling < Unroll; unrolling++)
+    {
+#if __BYTE_ORDER == __BIG_ENDIAN
+    uint32_t one   = *current++ ^ swap(crc);
+    uint32_t two   = *current++;
+    uint32_t three = *current++;
+    uint32_t four  = *current++;
+    crc  = Crc32Lookup[ 0][ four         & 0xFF] ^
+           Crc32Lookup[ 1][(four  >>  8) & 0xFF] ^
+           Crc32Lookup[ 2][(four  >> 16) & 0xFF] ^
+           Crc32Lookup[ 3][(four  >> 24) & 0xFF] ^
+           Crc32Lookup[ 4][ three        & 0xFF] ^
+           Crc32Lookup[ 5][(three >>  8) & 0xFF] ^
+           Crc32Lookup[ 6][(three >> 16) & 0xFF] ^
+           Crc32Lookup[ 7][(three >> 24) & 0xFF] ^
+           Crc32Lookup[ 8][ two          & 0xFF] ^
+           Crc32Lookup[ 9][(two   >>  8) & 0xFF] ^
+           Crc32Lookup[10][(two   >> 16) & 0xFF] ^
+           Crc32Lookup[11][(two   >> 24) & 0xFF] ^
+           Crc32Lookup[12][ one          & 0xFF] ^
+           Crc32Lookup[13][(one   >>  8) & 0xFF] ^
+           Crc32Lookup[14][(one   >> 16) & 0xFF] ^
+           Crc32Lookup[15][(one   >> 24) & 0xFF];
+#else
+    uint32_t one   = *current++ ^ crc;
+    uint32_t two   = *current++;
+    uint32_t three = *current++;
+    uint32_t four  = *current++;
+    crc  = Crc32Lookup[ 0][(four  >> 24) & 0xFF] ^
+           Crc32Lookup[ 1][(four  >> 16) & 0xFF] ^
+           Crc32Lookup[ 2][(four  >>  8) & 0xFF] ^
+           Crc32Lookup[ 3][ four         & 0xFF] ^
+           Crc32Lookup[ 4][(three >> 24) & 0xFF] ^
+           Crc32Lookup[ 5][(three >> 16) & 0xFF] ^
+           Crc32Lookup[ 6][(three >>  8) & 0xFF] ^
+           Crc32Lookup[ 7][ three        & 0xFF] ^
+           Crc32Lookup[ 8][(two   >> 24) & 0xFF] ^
+           Crc32Lookup[ 9][(two   >> 16) & 0xFF] ^
+           Crc32Lookup[10][(two   >>  8) & 0xFF] ^
+           Crc32Lookup[11][ two          & 0xFF] ^
+           Crc32Lookup[12][(one   >> 24) & 0xFF] ^
+           Crc32Lookup[13][(one   >> 16) & 0xFF] ^
+           Crc32Lookup[14][(one   >>  8) & 0xFF] ^
+           Crc32Lookup[15][ one          & 0xFF];
+#endif
+    }
+
+    length -= BytesAtOnce;
+  }
+
+  const uint8_t* currentChar = (const uint8_t*) current;
+  // remaining 1 to 63 bytes (standard algorithm)
+  while (length-- != 0)
+    crc = (crc >> 8) ^ Crc32Lookup[0][(crc & 0xFF) ^ *currentChar++];
+
+  return ~crc; // same as crc ^ 0xFFFFFFFF
+}
+#endif
+
+
+/// compute CRC32 using the fastest algorithm for large datasets on modern CPUs
+uint32_t crc32_fast(const void* data, size_t length, uint32_t previousCrc32)
+{
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+  return crc32_16bytes (data, length, previousCrc32);
+#elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8)
+  return crc32_8bytes  (data, length, previousCrc32);
+#elif defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4)
+  return crc32_4bytes  (data, length, previousCrc32);
+#elif defined(CRC32_USE_LOOKUP_TABLE_BYTE)
+  return crc32_1byte   (data, length, previousCrc32);
+#else
+  return crc32_halfbyte(data, length, previousCrc32);
+#endif
+}
+
+
+/// merge two CRC32 such that result = crc32(dataB, lengthB, crc32(dataA, lengthA))
+uint32_t crc32_combine(uint32_t crcA, uint32_t crcB, size_t lengthB)
+{
+  // based on Mark Adler's crc_combine from
+  // https://github.com/madler/pigz/blob/master/pigz.c
+
+  // main idea:
+  // - if you have two equally-sized blocks A and B,
+  //   then you can create a block C = A ^ B
+  //   which has the property crc(C) = crc(A) ^ crc(B)
+  // - if you append length(B) zeros to A and call it A' (think of it as AAAA000)
+  //   and   prepend length(A) zeros to B and call it B' (think of it as 0000BBB)
+  //   then exists a C' = A' ^ B'
+  // - remember: if you XOR someting with zero, it remains unchanged: X ^ 0 = X
+  // - that means C' = A concat B so that crc(A concat B) = crc(C') = crc(A') ^ crc(B')
+  // - the trick is to compute crc(A') based on crc(A)
+  //                       and crc(B') based on crc(B)
+  // - since B' starts with many zeros, the crc of those initial zeros is still zero
+  // - that means crc(B') = crc(B)
+  // - unfortunately the trailing zeros of A' change the crc, so usually crc(A') != crc(A)
+  // - the following code is a fast algorithm to compute crc(A')
+  // - starting with crc(A) and appending length(B) zeros, needing just log2(length(B)) iterations
+  // - the details are explained by the original author at
+  //   https://stackoverflow.com/questions/23122312/crc-calculation-of-a-mostly-static-data-stream/23126768
+  //
+  // notes:
+  // - I squeezed everything into one function to keep global namespace clean (original code two helper functions)
+  // - most original comments are still in place, I added comments where these helper functions where made inline code
+  // - performance-wise there isn't any differenze to the original zlib/pigz code
+
+  // degenerated case
+  if (lengthB == 0)
+    return crcA;
+
+  /// CRC32 => 32 bits
+  const uint32_t CrcBits = 32;
+
+  uint32_t odd [CrcBits]; // odd-power-of-two  zeros operator
+  uint32_t even[CrcBits]; // even-power-of-two zeros operator
+
+  // put operator for one zero bit in odd
+  odd[0] = Polynomial;    // CRC-32 polynomial
+  for (uint32_t i = 1; i < CrcBits; i++)
+    odd[i] = 1 << (i - 1);
+
+  // put operator for two zero bits in even
+  // same as gf2_matrix_square(even, odd);
+  for (uint32_t i = 0; i < CrcBits; i++)
+  {
+    uint32_t vec = odd[i];
+    even[i] = 0;
+    for (int j = 0; vec != 0; j++, vec >>= 1)
+      if (vec & 1)
+        even[i] ^= odd[j];
+  }
+  // put operator for four zero bits in odd
+  // same as gf2_matrix_square(odd, even);
+  for (uint32_t i = 0; i < CrcBits; i++)
+  {
+    uint32_t vec = even[i];
+    odd[i] = 0;
+    for (int j = 0; vec != 0; j++, vec >>= 1)
+      if (vec & 1)
+        odd[i] ^= even[j];
+  }
+
+  // the following loop becomes much shorter if I keep swapping even and odd
+  uint32_t* a = even;
+  uint32_t* b = odd;
+  // apply secondLength zeros to firstCrc32
+  for (; lengthB > 0; lengthB >>= 1)
+  {
+    // same as gf2_matrix_square(a, b);
+    for (uint32_t i = 0; i < CrcBits; i++)
+    {
+      uint32_t vec = b[i];
+      a[i] = 0;
+      for (int j = 0; vec != 0; j++, vec >>= 1)
+        if (vec & 1)
+          a[i] ^= b[j];
+    }
+
+    // apply zeros operator for this bit
+    if (lengthB & 1)
+    {
+      // same as firstCrc32 = gf2_matrix_times(a, firstCrc32);
+      uint32_t sum = 0;
+      for (int i = 0; crcA != 0; i++, crcA >>= 1)
+        if (crcA & 1)
+          sum ^= a[i];
+      crcA = sum;
+    }
+
+    // switch even and odd
+    uint32_t* t = a; a = b; b = t;
+  }
+
+  // return combined crc
+  return crcA ^ crcB;
+}
+
+
+// //////////////////////////////////////////////////////////
+// constants
+
+
+#ifndef NO_LUT
+/// look-up table, already declared above
+const uint32_t Crc32Lookup[MaxSlice][256] =
+{
+  //// same algorithm as crc32_bitwise
+  //for (int i = 0; i <= 0xFF; i++)
+  //{
+  //  uint32_t crc = i;
+  //  for (int j = 0; j < 8; j++)
+  //    crc = (crc >> 1) ^ ((crc & 1) * Polynomial);
+  //  Crc32Lookup[0][i] = crc;
+  //}
+  //// ... and the following slicing-by-8 algorithm (from Intel):
+  //// http://www.intel.com/technology/comms/perfnet/download/CRC_generators.pdf
+  //// http://sourceforge.net/projects/slicing-by-8/
+  //for (int slice = 1; slice < MaxSlice; slice++)
+  //  Crc32Lookup[slice][i] = (Crc32Lookup[slice - 1][i] >> 8) ^ Crc32Lookup[0][Crc32Lookup[slice - 1][i] & 0xFF];
+  {
+    // note: the first number of every second row corresponds to the half-byte look-up table !
+    0x00000000,0x77073096,0xEE0E612C,0x990951BA,0x076DC419,0x706AF48F,0xE963A535,0x9E6495A3,
+    0x0EDB8832,0x79DCB8A4,0xE0D5E91E,0x97D2D988,0x09B64C2B,0x7EB17CBD,0xE7B82D07,0x90BF1D91,
+    0x1DB71064,0x6AB020F2,0xF3B97148,0x84BE41DE,0x1ADAD47D,0x6DDDE4EB,0xF4D4B551,0x83D385C7,
+    0x136C9856,0x646BA8C0,0xFD62F97A,0x8A65C9EC,0x14015C4F,0x63066CD9,0xFA0F3D63,0x8D080DF5,
+    0x3B6E20C8,0x4C69105E,0xD56041E4,0xA2677172,0x3C03E4D1,0x4B04D447,0xD20D85FD,0xA50AB56B,
+    0x35B5A8FA,0x42B2986C,0xDBBBC9D6,0xACBCF940,0x32D86CE3,0x45DF5C75,0xDCD60DCF,0xABD13D59,
+    0x26D930AC,0x51DE003A,0xC8D75180,0xBFD06116,0x21B4F4B5,0x56B3C423,0xCFBA9599,0xB8BDA50F,
+    0x2802B89E,0x5F058808,0xC60CD9B2,0xB10BE924,0x2F6F7C87,0x58684C11,0xC1611DAB,0xB6662D3D,
+    0x76DC4190,0x01DB7106,0x98D220BC,0xEFD5102A,0x71B18589,0x06B6B51F,0x9FBFE4A5,0xE8B8D433,
+    0x7807C9A2,0x0F00F934,0x9609A88E,0xE10E9818,0x7F6A0DBB,0x086D3D2D,0x91646C97,0xE6635C01,
+    0x6B6B51F4,0x1C6C6162,0x856530D8,0xF262004E,0x6C0695ED,0x1B01A57B,0x8208F4C1,0xF50FC457,
+    0x65B0D9C6,0x12B7E950,0x8BBEB8EA,0xFCB9887C,0x62DD1DDF,0x15DA2D49,0x8CD37CF3,0xFBD44C65,
+    0x4DB26158,0x3AB551CE,0xA3BC0074,0xD4BB30E2,0x4ADFA541,0x3DD895D7,0xA4D1C46D,0xD3D6F4FB,
+    0x4369E96A,0x346ED9FC,0xAD678846,0xDA60B8D0,0x44042D73,0x33031DE5,0xAA0A4C5F,0xDD0D7CC9,
+    0x5005713C,0x270241AA,0xBE0B1010,0xC90C2086,0x5768B525,0x206F85B3,0xB966D409,0xCE61E49F,
+    0x5EDEF90E,0x29D9C998,0xB0D09822,0xC7D7A8B4,0x59B33D17,0x2EB40D81,0xB7BD5C3B,0xC0BA6CAD,
+    0xEDB88320,0x9ABFB3B6,0x03B6E20C,0x74B1D29A,0xEAD54739,0x9DD277AF,0x04DB2615,0x73DC1683,
+    0xE3630B12,0x94643B84,0x0D6D6A3E,0x7A6A5AA8,0xE40ECF0B,0x9309FF9D,0x0A00AE27,0x7D079EB1,
+    0xF00F9344,0x8708A3D2,0x1E01F268,0x6906C2FE,0xF762575D,0x806567CB,0x196C3671,0x6E6B06E7,
+    0xFED41B76,0x89D32BE0,0x10DA7A5A,0x67DD4ACC,0xF9B9DF6F,0x8EBEEFF9,0x17B7BE43,0x60B08ED5,
+    0xD6D6A3E8,0xA1D1937E,0x38D8C2C4,0x4FDFF252,0xD1BB67F1,0xA6BC5767,0x3FB506DD,0x48B2364B,
+    0xD80D2BDA,0xAF0A1B4C,0x36034AF6,0x41047A60,0xDF60EFC3,0xA867DF55,0x316E8EEF,0x4669BE79,
+    0xCB61B38C,0xBC66831A,0x256FD2A0,0x5268E236,0xCC0C7795,0xBB0B4703,0x220216B9,0x5505262F,
+    0xC5BA3BBE,0xB2BD0B28,0x2BB45A92,0x5CB36A04,0xC2D7FFA7,0xB5D0CF31,0x2CD99E8B,0x5BDEAE1D,
+    0x9B64C2B0,0xEC63F226,0x756AA39C,0x026D930A,0x9C0906A9,0xEB0E363F,0x72076785,0x05005713,
+    0x95BF4A82,0xE2B87A14,0x7BB12BAE,0x0CB61B38,0x92D28E9B,0xE5D5BE0D,0x7CDCEFB7,0x0BDBDF21,
+    0x86D3D2D4,0xF1D4E242,0x68DDB3F8,0x1FDA836E,0x81BE16CD,0xF6B9265B,0x6FB077E1,0x18B74777,
+    0x88085AE6,0xFF0F6A70,0x66063BCA,0x11010B5C,0x8F659EFF,0xF862AE69,0x616BFFD3,0x166CCF45,
+    0xA00AE278,0xD70DD2EE,0x4E048354,0x3903B3C2,0xA7672661,0xD06016F7,0x4969474D,0x3E6E77DB,
+    0xAED16A4A,0xD9D65ADC,0x40DF0B66,0x37D83BF0,0xA9BCAE53,0xDEBB9EC5,0x47B2CF7F,0x30B5FFE9,
+    0xBDBDF21C,0xCABAC28A,0x53B39330,0x24B4A3A6,0xBAD03605,0xCDD70693,0x54DE5729,0x23D967BF,
+    0xB3667A2E,0xC4614AB8,0x5D681B02,0x2A6F2B94,0xB40BBE37,0xC30C8EA1,0x5A05DF1B,0x2D02EF8D,
+  }
+
+#if defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16)
+  // beyond this point only relevant for Slicing-by-4, Slicing-by-8 and Slicing-by-16
+  ,{
+    0x00000000,0x191B3141,0x32366282,0x2B2D53C3,0x646CC504,0x7D77F445,0x565AA786,0x4F4196C7,
+    0xC8D98A08,0xD1C2BB49,0xFAEFE88A,0xE3F4D9CB,0xACB54F0C,0xB5AE7E4D,0x9E832D8E,0x87981CCF,
+    0x4AC21251,0x53D92310,0x78F470D3,0x61EF4192,0x2EAED755,0x37B5E614,0x1C98B5D7,0x05838496,
+    0x821B9859,0x9B00A918,0xB02DFADB,0xA936CB9A,0xE6775D5D,0xFF6C6C1C,0xD4413FDF,0xCD5A0E9E,
+    0x958424A2,0x8C9F15E3,0xA7B24620,0xBEA97761,0xF1E8E1A6,0xE8F3D0E7,0xC3DE8324,0xDAC5B265,
+    0x5D5DAEAA,0x44469FEB,0x6F6BCC28,0x7670FD69,0x39316BAE,0x202A5AEF,0x0B07092C,0x121C386D,
+    0xDF4636F3,0xC65D07B2,0xED705471,0xF46B6530,0xBB2AF3F7,0xA231C2B6,0x891C9175,0x9007A034,
+    0x179FBCFB,0x0E848DBA,0x25A9DE79,0x3CB2EF38,0x73F379FF,0x6AE848BE,0x41C51B7D,0x58DE2A3C,
+    0xF0794F05,0xE9627E44,0xC24F2D87,0xDB541CC6,0x94158A01,0x8D0EBB40,0xA623E883,0xBF38D9C2,
+    0x38A0C50D,0x21BBF44C,0x0A96A78F,0x138D96CE,0x5CCC0009,0x45D73148,0x6EFA628B,0x77E153CA,
+    0xBABB5D54,0xA3A06C15,0x888D3FD6,0x91960E97,0xDED79850,0xC7CCA911,0xECE1FAD2,0xF5FACB93,
+    0x7262D75C,0x6B79E61D,0x4054B5DE,0x594F849F,0x160E1258,0x0F152319,0x243870DA,0x3D23419B,
+    0x65FD6BA7,0x7CE65AE6,0x57CB0925,0x4ED03864,0x0191AEA3,0x188A9FE2,0x33A7CC21,0x2ABCFD60,
+    0xAD24E1AF,0xB43FD0EE,0x9F12832D,0x8609B26C,0xC94824AB,0xD05315EA,0xFB7E4629,0xE2657768,
+    0x2F3F79F6,0x362448B7,0x1D091B74,0x04122A35,0x4B53BCF2,0x52488DB3,0x7965DE70,0x607EEF31,
+    0xE7E6F3FE,0xFEFDC2BF,0xD5D0917C,0xCCCBA03D,0x838A36FA,0x9A9107BB,0xB1BC5478,0xA8A76539,
+    0x3B83984B,0x2298A90A,0x09B5FAC9,0x10AECB88,0x5FEF5D4F,0x46F46C0E,0x6DD93FCD,0x74C20E8C,
+    0xF35A1243,0xEA412302,0xC16C70C1,0xD8774180,0x9736D747,0x8E2DE606,0xA500B5C5,0xBC1B8484,
+    0x71418A1A,0x685ABB5B,0x4377E898,0x5A6CD9D9,0x152D4F1E,0x0C367E5F,0x271B2D9C,0x3E001CDD,
+    0xB9980012,0xA0833153,0x8BAE6290,0x92B553D1,0xDDF4C516,0xC4EFF457,0xEFC2A794,0xF6D996D5,
+    0xAE07BCE9,0xB71C8DA8,0x9C31DE6B,0x852AEF2A,0xCA6B79ED,0xD37048AC,0xF85D1B6F,0xE1462A2E,
+    0x66DE36E1,0x7FC507A0,0x54E85463,0x4DF36522,0x02B2F3E5,0x1BA9C2A4,0x30849167,0x299FA026,
+    0xE4C5AEB8,0xFDDE9FF9,0xD6F3CC3A,0xCFE8FD7B,0x80A96BBC,0x99B25AFD,0xB29F093E,0xAB84387F,
+    0x2C1C24B0,0x350715F1,0x1E2A4632,0x07317773,0x4870E1B4,0x516BD0F5,0x7A468336,0x635DB277,
+    0xCBFAD74E,0xD2E1E60F,0xF9CCB5CC,0xE0D7848D,0xAF96124A,0xB68D230B,0x9DA070C8,0x84BB4189,
+    0x03235D46,0x1A386C07,0x31153FC4,0x280E0E85,0x674F9842,0x7E54A903,0x5579FAC0,0x4C62CB81,
+    0x8138C51F,0x9823F45E,0xB30EA79D,0xAA1596DC,0xE554001B,0xFC4F315A,0xD7626299,0xCE7953D8,
+    0x49E14F17,0x50FA7E56,0x7BD72D95,0x62CC1CD4,0x2D8D8A13,0x3496BB52,0x1FBBE891,0x06A0D9D0,
+    0x5E7EF3EC,0x4765C2AD,0x6C48916E,0x7553A02F,0x3A1236E8,0x230907A9,0x0824546A,0x113F652B,
+    0x96A779E4,0x8FBC48A5,0xA4911B66,0xBD8A2A27,0xF2CBBCE0,0xEBD08DA1,0xC0FDDE62,0xD9E6EF23,
+    0x14BCE1BD,0x0DA7D0FC,0x268A833F,0x3F91B27E,0x70D024B9,0x69CB15F8,0x42E6463B,0x5BFD777A,
+    0xDC656BB5,0xC57E5AF4,0xEE530937,0xF7483876,0xB809AEB1,0xA1129FF0,0x8A3FCC33,0x9324FD72,
+  },
+
+  {
+    0x00000000,0x01C26A37,0x0384D46E,0x0246BE59,0x0709A8DC,0x06CBC2EB,0x048D7CB2,0x054F1685,
+    0x0E1351B8,0x0FD13B8F,0x0D9785D6,0x0C55EFE1,0x091AF964,0x08D89353,0x0A9E2D0A,0x0B5C473D,
+    0x1C26A370,0x1DE4C947,0x1FA2771E,0x1E601D29,0x1B2F0BAC,0x1AED619B,0x18ABDFC2,0x1969B5F5,
+    0x1235F2C8,0x13F798FF,0x11B126A6,0x10734C91,0x153C5A14,0x14FE3023,0x16B88E7A,0x177AE44D,
+    0x384D46E0,0x398F2CD7,0x3BC9928E,0x3A0BF8B9,0x3F44EE3C,0x3E86840B,0x3CC03A52,0x3D025065,
+    0x365E1758,0x379C7D6F,0x35DAC336,0x3418A901,0x3157BF84,0x3095D5B3,0x32D36BEA,0x331101DD,
+    0x246BE590,0x25A98FA7,0x27EF31FE,0x262D5BC9,0x23624D4C,0x22A0277B,0x20E69922,0x2124F315,
+    0x2A78B428,0x2BBADE1F,0x29FC6046,0x283E0A71,0x2D711CF4,0x2CB376C3,0x2EF5C89A,0x2F37A2AD,
+    0x709A8DC0,0x7158E7F7,0x731E59AE,0x72DC3399,0x7793251C,0x76514F2B,0x7417F172,0x75D59B45,
+    0x7E89DC78,0x7F4BB64F,0x7D0D0816,0x7CCF6221,0x798074A4,0x78421E93,0x7A04A0CA,0x7BC6CAFD,
+    0x6CBC2EB0,0x6D7E4487,0x6F38FADE,0x6EFA90E9,0x6BB5866C,0x6A77EC5B,0x68315202,0x69F33835,
+    0x62AF7F08,0x636D153F,0x612BAB66,0x60E9C151,0x65A6D7D4,0x6464BDE3,0x662203BA,0x67E0698D,
+    0x48D7CB20,0x4915A117,0x4B531F4E,0x4A917579,0x4FDE63FC,0x4E1C09CB,0x4C5AB792,0x4D98DDA5,
+    0x46C49A98,0x4706F0AF,0x45404EF6,0x448224C1,0x41CD3244,0x400F5873,0x4249E62A,0x438B8C1D,
+    0x54F16850,0x55330267,0x5775BC3E,0x56B7D609,0x53F8C08C,0x523AAABB,0x507C14E2,0x51BE7ED5,
+    0x5AE239E8,0x5B2053DF,0x5966ED86,0x58A487B1,0x5DEB9134,0x5C29FB03,0x5E6F455A,0x5FAD2F6D,
+    0xE1351B80,0xE0F771B7,0xE2B1CFEE,0xE373A5D9,0xE63CB35C,0xE7FED96B,0xE5B86732,0xE47A0D05,
+    0xEF264A38,0xEEE4200F,0xECA29E56,0xED60F461,0xE82FE2E4,0xE9ED88D3,0xEBAB368A,0xEA695CBD,
+    0xFD13B8F0,0xFCD1D2C7,0xFE976C9E,0xFF5506A9,0xFA1A102C,0xFBD87A1B,0xF99EC442,0xF85CAE75,
+    0xF300E948,0xF2C2837F,0xF0843D26,0xF1465711,0xF4094194,0xF5CB2BA3,0xF78D95FA,0xF64FFFCD,
+    0xD9785D60,0xD8BA3757,0xDAFC890E,0xDB3EE339,0xDE71F5BC,0xDFB39F8B,0xDDF521D2,0xDC374BE5,
+    0xD76B0CD8,0xD6A966EF,0xD4EFD8B6,0xD52DB281,0xD062A404,0xD1A0CE33,0xD3E6706A,0xD2241A5D,
+    0xC55EFE10,0xC49C9427,0xC6DA2A7E,0xC7184049,0xC25756CC,0xC3953CFB,0xC1D382A2,0xC011E895,
+    0xCB4DAFA8,0xCA8FC59F,0xC8C97BC6,0xC90B11F1,0xCC440774,0xCD866D43,0xCFC0D31A,0xCE02B92D,
+    0x91AF9640,0x906DFC77,0x922B422E,0x93E92819,0x96A63E9C,0x976454AB,0x9522EAF2,0x94E080C5,
+    0x9FBCC7F8,0x9E7EADCF,0x9C381396,0x9DFA79A1,0x98B56F24,0x99770513,0x9B31BB4A,0x9AF3D17D,
+    0x8D893530,0x8C4B5F07,0x8E0DE15E,0x8FCF8B69,0x8A809DEC,0x8B42F7DB,0x89044982,0x88C623B5,
+    0x839A6488,0x82580EBF,0x801EB0E6,0x81DCDAD1,0x8493CC54,0x8551A663,0x8717183A,0x86D5720D,
+    0xA9E2D0A0,0xA820BA97,0xAA6604CE,0xABA46EF9,0xAEEB787C,0xAF29124B,0xAD6FAC12,0xACADC625,
+    0xA7F18118,0xA633EB2F,0xA4755576,0xA5B73F41,0xA0F829C4,0xA13A43F3,0xA37CFDAA,0xA2BE979D,
+    0xB5C473D0,0xB40619E7,0xB640A7BE,0xB782CD89,0xB2CDDB0C,0xB30FB13B,0xB1490F62,0xB08B6555,
+    0xBBD72268,0xBA15485F,0xB853F606,0xB9919C31,0xBCDE8AB4,0xBD1CE083,0xBF5A5EDA,0xBE9834ED,
+  },
+
+  {
+    0x00000000,0xB8BC6765,0xAA09C88B,0x12B5AFEE,0x8F629757,0x37DEF032,0x256B5FDC,0x9DD738B9,
+    0xC5B428EF,0x7D084F8A,0x6FBDE064,0xD7018701,0x4AD6BFB8,0xF26AD8DD,0xE0DF7733,0x58631056,
+    0x5019579F,0xE8A530FA,0xFA109F14,0x42ACF871,0xDF7BC0C8,0x67C7A7AD,0x75720843,0xCDCE6F26,
+    0x95AD7F70,0x2D111815,0x3FA4B7FB,0x8718D09E,0x1ACFE827,0xA2738F42,0xB0C620AC,0x087A47C9,
+    0xA032AF3E,0x188EC85B,0x0A3B67B5,0xB28700D0,0x2F503869,0x97EC5F0C,0x8559F0E2,0x3DE59787,
+    0x658687D1,0xDD3AE0B4,0xCF8F4F5A,0x7733283F,0xEAE41086,0x525877E3,0x40EDD80D,0xF851BF68,
+    0xF02BF8A1,0x48979FC4,0x5A22302A,0xE29E574F,0x7F496FF6,0xC7F50893,0xD540A77D,0x6DFCC018,
+    0x359FD04E,0x8D23B72B,0x9F9618C5,0x272A7FA0,0xBAFD4719,0x0241207C,0x10F48F92,0xA848E8F7,
+    0x9B14583D,0x23A83F58,0x311D90B6,0x89A1F7D3,0x1476CF6A,0xACCAA80F,0xBE7F07E1,0x06C36084,
+    0x5EA070D2,0xE61C17B7,0xF4A9B859,0x4C15DF3C,0xD1C2E785,0x697E80E0,0x7BCB2F0E,0xC377486B,
+    0xCB0D0FA2,0x73B168C7,0x6104C729,0xD9B8A04C,0x446F98F5,0xFCD3FF90,0xEE66507E,0x56DA371B,
+    0x0EB9274D,0xB6054028,0xA4B0EFC6,0x1C0C88A3,0x81DBB01A,0x3967D77F,0x2BD27891,0x936E1FF4,
+    0x3B26F703,0x839A9066,0x912F3F88,0x299358ED,0xB4446054,0x0CF80731,0x1E4DA8DF,0xA6F1CFBA,
+    0xFE92DFEC,0x462EB889,0x549B1767,0xEC277002,0x71F048BB,0xC94C2FDE,0xDBF98030,0x6345E755,
+    0x6B3FA09C,0xD383C7F9,0xC1366817,0x798A0F72,0xE45D37CB,0x5CE150AE,0x4E54FF40,0xF6E89825,
+    0xAE8B8873,0x1637EF16,0x048240F8,0xBC3E279D,0x21E91F24,0x99557841,0x8BE0D7AF,0x335CB0CA,
+    0xED59B63B,0x55E5D15E,0x47507EB0,0xFFEC19D5,0x623B216C,0xDA874609,0xC832E9E7,0x708E8E82,
+    0x28ED9ED4,0x9051F9B1,0x82E4565F,0x3A58313A,0xA78F0983,0x1F336EE6,0x0D86C108,0xB53AA66D,
+    0xBD40E1A4,0x05FC86C1,0x1749292F,0xAFF54E4A,0x322276F3,0x8A9E1196,0x982BBE78,0x2097D91D,
+    0x78F4C94B,0xC048AE2E,0xD2FD01C0,0x6A4166A5,0xF7965E1C,0x4F2A3979,0x5D9F9697,0xE523F1F2,
+    0x4D6B1905,0xF5D77E60,0xE762D18E,0x5FDEB6EB,0xC2098E52,0x7AB5E937,0x680046D9,0xD0BC21BC,
+    0x88DF31EA,0x3063568F,0x22D6F961,0x9A6A9E04,0x07BDA6BD,0xBF01C1D8,0xADB46E36,0x15080953,
+    0x1D724E9A,0xA5CE29FF,0xB77B8611,0x0FC7E174,0x9210D9CD,0x2AACBEA8,0x38191146,0x80A57623,
+    0xD8C66675,0x607A0110,0x72CFAEFE,0xCA73C99B,0x57A4F122,0xEF189647,0xFDAD39A9,0x45115ECC,
+    0x764DEE06,0xCEF18963,0xDC44268D,0x64F841E8,0xF92F7951,0x41931E34,0x5326B1DA,0xEB9AD6BF,
+    0xB3F9C6E9,0x0B45A18C,0x19F00E62,0xA14C6907,0x3C9B51BE,0x842736DB,0x96929935,0x2E2EFE50,
+    0x2654B999,0x9EE8DEFC,0x8C5D7112,0x34E11677,0xA9362ECE,0x118A49AB,0x033FE645,0xBB838120,
+    0xE3E09176,0x5B5CF613,0x49E959FD,0xF1553E98,0x6C820621,0xD43E6144,0xC68BCEAA,0x7E37A9CF,
+    0xD67F4138,0x6EC3265D,0x7C7689B3,0xC4CAEED6,0x591DD66F,0xE1A1B10A,0xF3141EE4,0x4BA87981,
+    0x13CB69D7,0xAB770EB2,0xB9C2A15C,0x017EC639,0x9CA9FE80,0x241599E5,0x36A0360B,0x8E1C516E,
+    0x866616A7,0x3EDA71C2,0x2C6FDE2C,0x94D3B949,0x090481F0,0xB1B8E695,0xA30D497B,0x1BB12E1E,
+    0x43D23E48,0xFB6E592D,0xE9DBF6C3,0x516791A6,0xCCB0A91F,0x740CCE7A,0x66B96194,0xDE0506F1,
+  }
+#endif // defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_4) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16)
+#if defined (CRC32_USE_LOOKUP_TABLE_SLICING_BY_8) || defined(CRC32_USE_LOOKUP_TABLE_SLICING_BY_16)
+  // beyond this point only relevant for Slicing-by-8 and Slicing-by-16
+  ,{
+    0x00000000,0x3D6029B0,0x7AC05360,0x47A07AD0,0xF580A6C0,0xC8E08F70,0x8F40F5A0,0xB220DC10,
+    0x30704BC1,0x0D106271,0x4AB018A1,0x77D03111,0xC5F0ED01,0xF890C4B1,0xBF30BE61,0x825097D1,
+    0x60E09782,0x5D80BE32,0x1A20C4E2,0x2740ED52,0x95603142,0xA80018F2,0xEFA06222,0xD2C04B92,
+    0x5090DC43,0x6DF0F5F3,0x2A508F23,0x1730A693,0xA5107A83,0x98705333,0xDFD029E3,0xE2B00053,
+    0xC1C12F04,0xFCA106B4,0xBB017C64,0x866155D4,0x344189C4,0x0921A074,0x4E81DAA4,0x73E1F314,
+    0xF1B164C5,0xCCD14D75,0x8B7137A5,0xB6111E15,0x0431C205,0x3951EBB5,0x7EF19165,0x4391B8D5,
+    0xA121B886,0x9C419136,0xDBE1EBE6,0xE681C256,0x54A11E46,0x69C137F6,0x2E614D26,0x13016496,
+    0x9151F347,0xAC31DAF7,0xEB91A027,0xD6F18997,0x64D15587,0x59B17C37,0x1E1106E7,0x23712F57,
+    0x58F35849,0x659371F9,0x22330B29,0x1F532299,0xAD73FE89,0x9013D739,0xD7B3ADE9,0xEAD38459,
+    0x68831388,0x55E33A38,0x124340E8,0x2F236958,0x9D03B548,0xA0639CF8,0xE7C3E628,0xDAA3CF98,
+    0x3813CFCB,0x0573E67B,0x42D39CAB,0x7FB3B51B,0xCD93690B,0xF0F340BB,0xB7533A6B,0x8A3313DB,
+    0x0863840A,0x3503ADBA,0x72A3D76A,0x4FC3FEDA,0xFDE322CA,0xC0830B7A,0x872371AA,0xBA43581A,
+    0x9932774D,0xA4525EFD,0xE3F2242D,0xDE920D9D,0x6CB2D18D,0x51D2F83D,0x167282ED,0x2B12AB5D,
+    0xA9423C8C,0x9422153C,0xD3826FEC,0xEEE2465C,0x5CC29A4C,0x61A2B3FC,0x2602C92C,0x1B62E09C,
+    0xF9D2E0CF,0xC4B2C97F,0x8312B3AF,0xBE729A1F,0x0C52460F,0x31326FBF,0x7692156F,0x4BF23CDF,
+    0xC9A2AB0E,0xF4C282BE,0xB362F86E,0x8E02D1DE,0x3C220DCE,0x0142247E,0x46E25EAE,0x7B82771E,
+    0xB1E6B092,0x8C869922,0xCB26E3F2,0xF646CA42,0x44661652,0x79063FE2,0x3EA64532,0x03C66C82,
+    0x8196FB53,0xBCF6D2E3,0xFB56A833,0xC6368183,0x74165D93,0x49767423,0x0ED60EF3,0x33B62743,
+    0xD1062710,0xEC660EA0,0xABC67470,0x96A65DC0,0x248681D0,0x19E6A860,0x5E46D2B0,0x6326FB00,
+    0xE1766CD1,0xDC164561,0x9BB63FB1,0xA6D61601,0x14F6CA11,0x2996E3A1,0x6E369971,0x5356B0C1,
+    0x70279F96,0x4D47B626,0x0AE7CCF6,0x3787E546,0x85A73956,0xB8C710E6,0xFF676A36,0xC2074386,
+    0x4057D457,0x7D37FDE7,0x3A978737,0x07F7AE87,0xB5D77297,0x88B75B27,0xCF1721F7,0xF2770847,
+    0x10C70814,0x2DA721A4,0x6A075B74,0x576772C4,0xE547AED4,0xD8278764,0x9F87FDB4,0xA2E7D404,
+    0x20B743D5,0x1DD76A65,0x5A7710B5,0x67173905,0xD537E515,0xE857CCA5,0xAFF7B675,0x92979FC5,
+    0xE915E8DB,0xD475C16B,0x93D5BBBB,0xAEB5920B,0x1C954E1B,0x21F567AB,0x66551D7B,0x5B3534CB,
+    0xD965A31A,0xE4058AAA,0xA3A5F07A,0x9EC5D9CA,0x2CE505DA,0x11852C6A,0x562556BA,0x6B457F0A,
+    0x89F57F59,0xB49556E9,0xF3352C39,0xCE550589,0x7C75D999,0x4115F029,0x06B58AF9,0x3BD5A349,
+    0xB9853498,0x84E51D28,0xC34567F8,0xFE254E48,0x4C059258,0x7165BBE8,0x36C5C138,0x0BA5E888,
+    0x28D4C7DF,0x15B4EE6F,0x521494BF,0x6F74BD0F,0xDD54611F,0xE03448AF,0xA794327F,0x9AF41BCF,
+    0x18A48C1E,0x25C4A5AE,0x6264DF7E,0x5F04F6CE,0xED242ADE,0xD044036E,0x97E479BE,0xAA84500E,
+    0x4834505D,0x755479ED,0x32F4033D,0x0F942A8D,0xBDB4F69D,0x80D4DF2D,0xC774A5FD,0xFA148C4D,
+    0x78441B9C,0x4524322C,0x028448FC,0x3FE4614C,0x8DC4BD5C,0xB0A494EC,0xF704EE3C,0xCA64C78C,
+  },
+
+  {
+    0x00000000,0xCB5CD3A5,0x4DC8A10B,0x869472AE,0x9B914216,0x50CD91B3,0xD659E31D,0x1D0530B8,
+    0xEC53826D,0x270F51C8,0xA19B2366,0x6AC7F0C3,0x77C2C07B,0xBC9E13DE,0x3A0A6170,0xF156B2D5,
+    0x03D6029B,0xC88AD13E,0x4E1EA390,0x85427035,0x9847408D,0x531B9328,0xD58FE186,0x1ED33223,
+    0xEF8580F6,0x24D95353,0xA24D21FD,0x6911F258,0x7414C2E0,0xBF481145,0x39DC63EB,0xF280B04E,
+    0x07AC0536,0xCCF0D693,0x4A64A43D,0x81387798,0x9C3D4720,0x57619485,0xD1F5E62B,0x1AA9358E,
+    0xEBFF875B,0x20A354FE,0xA6372650,0x6D6BF5F5,0x706EC54D,0xBB3216E8,0x3DA66446,0xF6FAB7E3,
+    0x047A07AD,0xCF26D408,0x49B2A6A6,0x82EE7503,0x9FEB45BB,0x54B7961E,0xD223E4B0,0x197F3715,
+    0xE82985C0,0x23755665,0xA5E124CB,0x6EBDF76E,0x73B8C7D6,0xB8E41473,0x3E7066DD,0xF52CB578,
+    0x0F580A6C,0xC404D9C9,0x4290AB67,0x89CC78C2,0x94C9487A,0x5F959BDF,0xD901E971,0x125D3AD4,
+    0xE30B8801,0x28575BA4,0xAEC3290A,0x659FFAAF,0x789ACA17,0xB3C619B2,0x35526B1C,0xFE0EB8B9,
+    0x0C8E08F7,0xC7D2DB52,0x4146A9FC,0x8A1A7A59,0x971F4AE1,0x5C439944,0xDAD7EBEA,0x118B384F,
+    0xE0DD8A9A,0x2B81593F,0xAD152B91,0x6649F834,0x7B4CC88C,0xB0101B29,0x36846987,0xFDD8BA22,
+    0x08F40F5A,0xC3A8DCFF,0x453CAE51,0x8E607DF4,0x93654D4C,0x58399EE9,0xDEADEC47,0x15F13FE2,
+    0xE4A78D37,0x2FFB5E92,0xA96F2C3C,0x6233FF99,0x7F36CF21,0xB46A1C84,0x32FE6E2A,0xF9A2BD8F,
+    0x0B220DC1,0xC07EDE64,0x46EAACCA,0x8DB67F6F,0x90B34FD7,0x5BEF9C72,0xDD7BEEDC,0x16273D79,
+    0xE7718FAC,0x2C2D5C09,0xAAB92EA7,0x61E5FD02,0x7CE0CDBA,0xB7BC1E1F,0x31286CB1,0xFA74BF14,
+    0x1EB014D8,0xD5ECC77D,0x5378B5D3,0x98246676,0x852156CE,0x4E7D856B,0xC8E9F7C5,0x03B52460,
+    0xF2E396B5,0x39BF4510,0xBF2B37BE,0x7477E41B,0x6972D4A3,0xA22E0706,0x24BA75A8,0xEFE6A60D,
+    0x1D661643,0xD63AC5E6,0x50AEB748,0x9BF264ED,0x86F75455,0x4DAB87F0,0xCB3FF55E,0x006326FB,
+    0xF135942E,0x3A69478B,0xBCFD3525,0x77A1E680,0x6AA4D638,0xA1F8059D,0x276C7733,0xEC30A496,
+    0x191C11EE,0xD240C24B,0x54D4B0E5,0x9F886340,0x828D53F8,0x49D1805D,0xCF45F2F3,0x04192156,
+    0xF54F9383,0x3E134026,0xB8873288,0x73DBE12D,0x6EDED195,0xA5820230,0x2316709E,0xE84AA33B,
+    0x1ACA1375,0xD196C0D0,0x5702B27E,0x9C5E61DB,0x815B5163,0x4A0782C6,0xCC93F068,0x07CF23CD,
+    0xF6999118,0x3DC542BD,0xBB513013,0x700DE3B6,0x6D08D30E,0xA65400AB,0x20C07205,0xEB9CA1A0,
+    0x11E81EB4,0xDAB4CD11,0x5C20BFBF,0x977C6C1A,0x8A795CA2,0x41258F07,0xC7B1FDA9,0x0CED2E0C,
+    0xFDBB9CD9,0x36E74F7C,0xB0733DD2,0x7B2FEE77,0x662ADECF,0xAD760D6A,0x2BE27FC4,0xE0BEAC61,
+    0x123E1C2F,0xD962CF8A,0x5FF6BD24,0x94AA6E81,0x89AF5E39,0x42F38D9C,0xC467FF32,0x0F3B2C97,
+    0xFE6D9E42,0x35314DE7,0xB3A53F49,0x78F9ECEC,0x65FCDC54,0xAEA00FF1,0x28347D5F,0xE368AEFA,
+    0x16441B82,0xDD18C827,0x5B8CBA89,0x90D0692C,0x8DD55994,0x46898A31,0xC01DF89F,0x0B412B3A,
+    0xFA1799EF,0x314B4A4A,0xB7DF38E4,0x7C83EB41,0x6186DBF9,0xAADA085C,0x2C4E7AF2,0xE712A957,
+    0x15921919,0xDECECABC,0x585AB812,0x93066BB7,0x8E035B0F,0x455F88AA,0xC3CBFA04,0x089729A1,
+    0xF9C19B74,0x329D48D1,0xB4093A7F,0x7F55E9DA,0x6250D962,0xA90C0AC7,0x2F987869,0xE4C4ABCC,
+  },
+
+  {
+    0x00000000,0xA6770BB4,0x979F1129,0x31E81A9D,0xF44F2413,0x52382FA7,0x63D0353A,0xC5A73E8E,
+    0x33EF4E67,0x959845D3,0xA4705F4E,0x020754FA,0xC7A06A74,0x61D761C0,0x503F7B5D,0xF64870E9,
+    0x67DE9CCE,0xC1A9977A,0xF0418DE7,0x56368653,0x9391B8DD,0x35E6B369,0x040EA9F4,0xA279A240,
+    0x5431D2A9,0xF246D91D,0xC3AEC380,0x65D9C834,0xA07EF6BA,0x0609FD0E,0x37E1E793,0x9196EC27,
+    0xCFBD399C,0x69CA3228,0x582228B5,0xFE552301,0x3BF21D8F,0x9D85163B,0xAC6D0CA6,0x0A1A0712,
+    0xFC5277FB,0x5A257C4F,0x6BCD66D2,0xCDBA6D66,0x081D53E8,0xAE6A585C,0x9F8242C1,0x39F54975,
+    0xA863A552,0x0E14AEE6,0x3FFCB47B,0x998BBFCF,0x5C2C8141,0xFA5B8AF5,0xCBB39068,0x6DC49BDC,
+    0x9B8CEB35,0x3DFBE081,0x0C13FA1C,0xAA64F1A8,0x6FC3CF26,0xC9B4C492,0xF85CDE0F,0x5E2BD5BB,
+    0x440B7579,0xE27C7ECD,0xD3946450,0x75E36FE4,0xB044516A,0x16335ADE,0x27DB4043,0x81AC4BF7,
+    0x77E43B1E,0xD19330AA,0xE07B2A37,0x460C2183,0x83AB1F0D,0x25DC14B9,0x14340E24,0xB2430590,
+    0x23D5E9B7,0x85A2E203,0xB44AF89E,0x123DF32A,0xD79ACDA4,0x71EDC610,0x4005DC8D,0xE672D739,
+    0x103AA7D0,0xB64DAC64,0x87A5B6F9,0x21D2BD4D,0xE47583C3,0x42028877,0x73EA92EA,0xD59D995E,
+    0x8BB64CE5,0x2DC14751,0x1C295DCC,0xBA5E5678,0x7FF968F6,0xD98E6342,0xE86679DF,0x4E11726B,
+    0xB8590282,0x1E2E0936,0x2FC613AB,0x89B1181F,0x4C162691,0xEA612D25,0xDB8937B8,0x7DFE3C0C,
+    0xEC68D02B,0x4A1FDB9F,0x7BF7C102,0xDD80CAB6,0x1827F438,0xBE50FF8C,0x8FB8E511,0x29CFEEA5,
+    0xDF879E4C,0x79F095F8,0x48188F65,0xEE6F84D1,0x2BC8BA5F,0x8DBFB1EB,0xBC57AB76,0x1A20A0C2,
+    0x8816EAF2,0x2E61E146,0x1F89FBDB,0xB9FEF06F,0x7C59CEE1,0xDA2EC555,0xEBC6DFC8,0x4DB1D47C,
+    0xBBF9A495,0x1D8EAF21,0x2C66B5BC,0x8A11BE08,0x4FB68086,0xE9C18B32,0xD82991AF,0x7E5E9A1B,
+    0xEFC8763C,0x49BF7D88,0x78576715,0xDE206CA1,0x1B87522F,0xBDF0599B,0x8C184306,0x2A6F48B2,
+    0xDC27385B,0x7A5033EF,0x4BB82972,0xEDCF22C6,0x28681C48,0x8E1F17FC,0xBFF70D61,0x198006D5,
+    0x47ABD36E,0xE1DCD8DA,0xD034C247,0x7643C9F3,0xB3E4F77D,0x1593FCC9,0x247BE654,0x820CEDE0,
+    0x74449D09,0xD23396BD,0xE3DB8C20,0x45AC8794,0x800BB91A,0x267CB2AE,0x1794A833,0xB1E3A387,
+    0x20754FA0,0x86024414,0xB7EA5E89,0x119D553D,0xD43A6BB3,0x724D6007,0x43A57A9A,0xE5D2712E,
+    0x139A01C7,0xB5ED0A73,0x840510EE,0x22721B5A,0xE7D525D4,0x41A22E60,0x704A34FD,0xD63D3F49,
+    0xCC1D9F8B,0x6A6A943F,0x5B828EA2,0xFDF58516,0x3852BB98,0x9E25B02C,0xAFCDAAB1,0x09BAA105,
+    0xFFF2D1EC,0x5985DA58,0x686DC0C5,0xCE1ACB71,0x0BBDF5FF,0xADCAFE4B,0x9C22E4D6,0x3A55EF62,
+    0xABC30345,0x0DB408F1,0x3C5C126C,0x9A2B19D8,0x5F8C2756,0xF9FB2CE2,0xC813367F,0x6E643DCB,
+    0x982C4D22,0x3E5B4696,0x0FB35C0B,0xA9C457BF,0x6C636931,0xCA146285,0xFBFC7818,0x5D8B73AC,
+    0x03A0A617,0xA5D7ADA3,0x943FB73E,0x3248BC8A,0xF7EF8204,0x519889B0,0x6070932D,0xC6079899,
+    0x304FE870,0x9638E3C4,0xA7D0F959,0x01A7F2ED,0xC400CC63,0x6277C7D7,0x539FDD4A,0xF5E8D6FE,
+    0x647E3AD9,0xC209316D,0xF3E12BF0,0x55962044,0x90311ECA,0x3646157E,0x07AE0FE3,0xA1D90457,
+    0x579174BE,0xF1E67F0A,0xC00E6597,0x66796E23,0xA3DE50AD,0x05A95B19,0x34414184,0x92364A30,
+  },
+
+  {
+    0x00000000,0xCCAA009E,0x4225077D,0x8E8F07E3,0x844A0EFA,0x48E00E64,0xC66F0987,0x0AC50919,
+    0xD3E51BB5,0x1F4F1B2B,0x91C01CC8,0x5D6A1C56,0x57AF154F,0x9B0515D1,0x158A1232,0xD92012AC,
+    0x7CBB312B,0xB01131B5,0x3E9E3656,0xF23436C8,0xF8F13FD1,0x345B3F4F,0xBAD438AC,0x767E3832,
+    0xAF5E2A9E,0x63F42A00,0xED7B2DE3,0x21D12D7D,0x2B142464,0xE7BE24FA,0x69312319,0xA59B2387,
+    0xF9766256,0x35DC62C8,0xBB53652B,0x77F965B5,0x7D3C6CAC,0xB1966C32,0x3F196BD1,0xF3B36B4F,
+    0x2A9379E3,0xE639797D,0x68B67E9E,0xA41C7E00,0xAED97719,0x62737787,0xECFC7064,0x205670FA,
+    0x85CD537D,0x496753E3,0xC7E85400,0x0B42549E,0x01875D87,0xCD2D5D19,0x43A25AFA,0x8F085A64,
+    0x562848C8,0x9A824856,0x140D4FB5,0xD8A74F2B,0xD2624632,0x1EC846AC,0x9047414F,0x5CED41D1,
+    0x299DC2ED,0xE537C273,0x6BB8C590,0xA712C50E,0xADD7CC17,0x617DCC89,0xEFF2CB6A,0x2358CBF4,
+    0xFA78D958,0x36D2D9C6,0xB85DDE25,0x74F7DEBB,0x7E32D7A2,0xB298D73C,0x3C17D0DF,0xF0BDD041,
+    0x5526F3C6,0x998CF358,0x1703F4BB,0xDBA9F425,0xD16CFD3C,0x1DC6FDA2,0x9349FA41,0x5FE3FADF,
+    0x86C3E873,0x4A69E8ED,0xC4E6EF0E,0x084CEF90,0x0289E689,0xCE23E617,0x40ACE1F4,0x8C06E16A,
+    0xD0EBA0BB,0x1C41A025,0x92CEA7C6,0x5E64A758,0x54A1AE41,0x980BAEDF,0x1684A93C,0xDA2EA9A2,
+    0x030EBB0E,0xCFA4BB90,0x412BBC73,0x8D81BCED,0x8744B5F4,0x4BEEB56A,0xC561B289,0x09CBB217,
+    0xAC509190,0x60FA910E,0xEE7596ED,0x22DF9673,0x281A9F6A,0xE4B09FF4,0x6A3F9817,0xA6959889,
+    0x7FB58A25,0xB31F8ABB,0x3D908D58,0xF13A8DC6,0xFBFF84DF,0x37558441,0xB9DA83A2,0x7570833C,
+    0x533B85DA,0x9F918544,0x111E82A7,0xDDB48239,0xD7718B20,0x1BDB8BBE,0x95548C5D,0x59FE8CC3,
+    0x80DE9E6F,0x4C749EF1,0xC2FB9912,0x0E51998C,0x04949095,0xC83E900B,0x46B197E8,0x8A1B9776,
+    0x2F80B4F1,0xE32AB46F,0x6DA5B38C,0xA10FB312,0xABCABA0B,0x6760BA95,0xE9EFBD76,0x2545BDE8,
+    0xFC65AF44,0x30CFAFDA,0xBE40A839,0x72EAA8A7,0x782FA1BE,0xB485A120,0x3A0AA6C3,0xF6A0A65D,
+    0xAA4DE78C,0x66E7E712,0xE868E0F1,0x24C2E06F,0x2E07E976,0xE2ADE9E8,0x6C22EE0B,0xA088EE95,
+    0x79A8FC39,0xB502FCA7,0x3B8DFB44,0xF727FBDA,0xFDE2F2C3,0x3148F25D,0xBFC7F5BE,0x736DF520,
+    0xD6F6D6A7,0x1A5CD639,0x94D3D1DA,0x5879D144,0x52BCD85D,0x9E16D8C3,0x1099DF20,0xDC33DFBE,
+    0x0513CD12,0xC9B9CD8C,0x4736CA6F,0x8B9CCAF1,0x8159C3E8,0x4DF3C376,0xC37CC495,0x0FD6C40B,
+    0x7AA64737,0xB60C47A9,0x3883404A,0xF42940D4,0xFEEC49CD,0x32464953,0xBCC94EB0,0x70634E2E,
+    0xA9435C82,0x65E95C1C,0xEB665BFF,0x27CC5B61,0x2D095278,0xE1A352E6,0x6F2C5505,0xA386559B,
+    0x061D761C,0xCAB77682,0x44387161,0x889271FF,0x825778E6,0x4EFD7878,0xC0727F9B,0x0CD87F05,
+    0xD5F86DA9,0x19526D37,0x97DD6AD4,0x5B776A4A,0x51B26353,0x9D1863CD,0x1397642E,0xDF3D64B0,
+    0x83D02561,0x4F7A25FF,0xC1F5221C,0x0D5F2282,0x079A2B9B,0xCB302B05,0x45BF2CE6,0x89152C78,
+    0x50353ED4,0x9C9F3E4A,0x121039A9,0xDEBA3937,0xD47F302E,0x18D530B0,0x965A3753,0x5AF037CD,
+    0xFF6B144A,0x33C114D4,0xBD4E1337,0x71E413A9,0x7B211AB0,0xB78B1A2E,0x39041DCD,0xF5AE1D53,
+    0x2C8E0FFF,0xE0240F61,0x6EAB0882,0xA201081C,0xA8C40105,0x646E019B,0xEAE10678,0x264B06E6,
+  }
+#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_8 || CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+#ifdef CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+  // beyond this point only relevant for Slicing-by-16
+  ,{
+    0x00000000,0x177B1443,0x2EF62886,0x398D3CC5,0x5DEC510C,0x4A97454F,0x731A798A,0x64616DC9,
+    0xBBD8A218,0xACA3B65B,0x952E8A9E,0x82559EDD,0xE634F314,0xF14FE757,0xC8C2DB92,0xDFB9CFD1,
+    0xACC04271,0xBBBB5632,0x82366AF7,0x954D7EB4,0xF12C137D,0xE657073E,0xDFDA3BFB,0xC8A12FB8,
+    0x1718E069,0x0063F42A,0x39EEC8EF,0x2E95DCAC,0x4AF4B165,0x5D8FA526,0x640299E3,0x73798DA0,
+    0x82F182A3,0x958A96E0,0xAC07AA25,0xBB7CBE66,0xDF1DD3AF,0xC866C7EC,0xF1EBFB29,0xE690EF6A,
+    0x392920BB,0x2E5234F8,0x17DF083D,0x00A41C7E,0x64C571B7,0x73BE65F4,0x4A335931,0x5D484D72,
+    0x2E31C0D2,0x394AD491,0x00C7E854,0x17BCFC17,0x73DD91DE,0x64A6859D,0x5D2BB958,0x4A50AD1B,
+    0x95E962CA,0x82927689,0xBB1F4A4C,0xAC645E0F,0xC80533C6,0xDF7E2785,0xE6F31B40,0xF1880F03,
+    0xDE920307,0xC9E91744,0xF0642B81,0xE71F3FC2,0x837E520B,0x94054648,0xAD887A8D,0xBAF36ECE,
+    0x654AA11F,0x7231B55C,0x4BBC8999,0x5CC79DDA,0x38A6F013,0x2FDDE450,0x1650D895,0x012BCCD6,
+    0x72524176,0x65295535,0x5CA469F0,0x4BDF7DB3,0x2FBE107A,0x38C50439,0x014838FC,0x16332CBF,
+    0xC98AE36E,0xDEF1F72D,0xE77CCBE8,0xF007DFAB,0x9466B262,0x831DA621,0xBA909AE4,0xADEB8EA7,
+    0x5C6381A4,0x4B1895E7,0x7295A922,0x65EEBD61,0x018FD0A8,0x16F4C4EB,0x2F79F82E,0x3802EC6D,
+    0xE7BB23BC,0xF0C037FF,0xC94D0B3A,0xDE361F79,0xBA5772B0,0xAD2C66F3,0x94A15A36,0x83DA4E75,
+    0xF0A3C3D5,0xE7D8D796,0xDE55EB53,0xC92EFF10,0xAD4F92D9,0xBA34869A,0x83B9BA5F,0x94C2AE1C,
+    0x4B7B61CD,0x5C00758E,0x658D494B,0x72F65D08,0x169730C1,0x01EC2482,0x38611847,0x2F1A0C04,
+    0x6655004F,0x712E140C,0x48A328C9,0x5FD83C8A,0x3BB95143,0x2CC24500,0x154F79C5,0x02346D86,
+    0xDD8DA257,0xCAF6B614,0xF37B8AD1,0xE4009E92,0x8061F35B,0x971AE718,0xAE97DBDD,0xB9ECCF9E,
+    0xCA95423E,0xDDEE567D,0xE4636AB8,0xF3187EFB,0x97791332,0x80020771,0xB98F3BB4,0xAEF42FF7,
+    0x714DE026,0x6636F465,0x5FBBC8A0,0x48C0DCE3,0x2CA1B12A,0x3BDAA569,0x025799AC,0x152C8DEF,
+    0xE4A482EC,0xF3DF96AF,0xCA52AA6A,0xDD29BE29,0xB948D3E0,0xAE33C7A3,0x97BEFB66,0x80C5EF25,
+    0x5F7C20F4,0x480734B7,0x718A0872,0x66F11C31,0x029071F8,0x15EB65BB,0x2C66597E,0x3B1D4D3D,
+    0x4864C09D,0x5F1FD4DE,0x6692E81B,0x71E9FC58,0x15889191,0x02F385D2,0x3B7EB917,0x2C05AD54,
+    0xF3BC6285,0xE4C776C6,0xDD4A4A03,0xCA315E40,0xAE503389,0xB92B27CA,0x80A61B0F,0x97DD0F4C,
+    0xB8C70348,0xAFBC170B,0x96312BCE,0x814A3F8D,0xE52B5244,0xF2504607,0xCBDD7AC2,0xDCA66E81,
+    0x031FA150,0x1464B513,0x2DE989D6,0x3A929D95,0x5EF3F05C,0x4988E41F,0x7005D8DA,0x677ECC99,
+    0x14074139,0x037C557A,0x3AF169BF,0x2D8A7DFC,0x49EB1035,0x5E900476,0x671D38B3,0x70662CF0,
+    0xAFDFE321,0xB8A4F762,0x8129CBA7,0x9652DFE4,0xF233B22D,0xE548A66E,0xDCC59AAB,0xCBBE8EE8,
+    0x3A3681EB,0x2D4D95A8,0x14C0A96D,0x03BBBD2E,0x67DAD0E7,0x70A1C4A4,0x492CF861,0x5E57EC22,
+    0x81EE23F3,0x969537B0,0xAF180B75,0xB8631F36,0xDC0272FF,0xCB7966BC,0xF2F45A79,0xE58F4E3A,
+    0x96F6C39A,0x818DD7D9,0xB800EB1C,0xAF7BFF5F,0xCB1A9296,0xDC6186D5,0xE5ECBA10,0xF297AE53,
+    0x2D2E6182,0x3A5575C1,0x03D84904,0x14A35D47,0x70C2308E,0x67B924CD,0x5E341808,0x494F0C4B,
+  },
+
+  {
+    0x00000000,0xEFC26B3E,0x04F5D03D,0xEB37BB03,0x09EBA07A,0xE629CB44,0x0D1E7047,0xE2DC1B79,
+    0x13D740F4,0xFC152BCA,0x172290C9,0xF8E0FBF7,0x1A3CE08E,0xF5FE8BB0,0x1EC930B3,0xF10B5B8D,
+    0x27AE81E8,0xC86CEAD6,0x235B51D5,0xCC993AEB,0x2E452192,0xC1874AAC,0x2AB0F1AF,0xC5729A91,
+    0x3479C11C,0xDBBBAA22,0x308C1121,0xDF4E7A1F,0x3D926166,0xD2500A58,0x3967B15B,0xD6A5DA65,
+    0x4F5D03D0,0xA09F68EE,0x4BA8D3ED,0xA46AB8D3,0x46B6A3AA,0xA974C894,0x42437397,0xAD8118A9,
+    0x5C8A4324,0xB348281A,0x587F9319,0xB7BDF827,0x5561E35E,0xBAA38860,0x51943363,0xBE56585D,
+    0x68F38238,0x8731E906,0x6C065205,0x83C4393B,0x61182242,0x8EDA497C,0x65EDF27F,0x8A2F9941,
+    0x7B24C2CC,0x94E6A9F2,0x7FD112F1,0x901379CF,0x72CF62B6,0x9D0D0988,0x763AB28B,0x99F8D9B5,
+    0x9EBA07A0,0x71786C9E,0x9A4FD79D,0x758DBCA3,0x9751A7DA,0x7893CCE4,0x93A477E7,0x7C661CD9,
+    0x8D6D4754,0x62AF2C6A,0x89989769,0x665AFC57,0x8486E72E,0x6B448C10,0x80733713,0x6FB15C2D,
+    0xB9148648,0x56D6ED76,0xBDE15675,0x52233D4B,0xB0FF2632,0x5F3D4D0C,0xB40AF60F,0x5BC89D31,
+    0xAAC3C6BC,0x4501AD82,0xAE361681,0x41F47DBF,0xA32866C6,0x4CEA0DF8,0xA7DDB6FB,0x481FDDC5,
+    0xD1E70470,0x3E256F4E,0xD512D44D,0x3AD0BF73,0xD80CA40A,0x37CECF34,0xDCF97437,0x333B1F09,
+    0xC2304484,0x2DF22FBA,0xC6C594B9,0x2907FF87,0xCBDBE4FE,0x24198FC0,0xCF2E34C3,0x20EC5FFD,
+    0xF6498598,0x198BEEA6,0xF2BC55A5,0x1D7E3E9B,0xFFA225E2,0x10604EDC,0xFB57F5DF,0x14959EE1,
+    0xE59EC56C,0x0A5CAE52,0xE16B1551,0x0EA97E6F,0xEC756516,0x03B70E28,0xE880B52B,0x0742DE15,
+    0xE6050901,0x09C7623F,0xE2F0D93C,0x0D32B202,0xEFEEA97B,0x002CC245,0xEB1B7946,0x04D91278,
+    0xF5D249F5,0x1A1022CB,0xF12799C8,0x1EE5F2F6,0xFC39E98F,0x13FB82B1,0xF8CC39B2,0x170E528C,
+    0xC1AB88E9,0x2E69E3D7,0xC55E58D4,0x2A9C33EA,0xC8402893,0x278243AD,0xCCB5F8AE,0x23779390,
+    0xD27CC81D,0x3DBEA323,0xD6891820,0x394B731E,0xDB976867,0x34550359,0xDF62B85A,0x30A0D364,
+    0xA9580AD1,0x469A61EF,0xADADDAEC,0x426FB1D2,0xA0B3AAAB,0x4F71C195,0xA4467A96,0x4B8411A8,
+    0xBA8F4A25,0x554D211B,0xBE7A9A18,0x51B8F126,0xB364EA5F,0x5CA68161,0xB7913A62,0x5853515C,
+    0x8EF68B39,0x6134E007,0x8A035B04,0x65C1303A,0x871D2B43,0x68DF407D,0x83E8FB7E,0x6C2A9040,
+    0x9D21CBCD,0x72E3A0F3,0x99D41BF0,0x761670CE,0x94CA6BB7,0x7B080089,0x903FBB8A,0x7FFDD0B4,
+    0x78BF0EA1,0x977D659F,0x7C4ADE9C,0x9388B5A2,0x7154AEDB,0x9E96C5E5,0x75A17EE6,0x9A6315D8,
+    0x6B684E55,0x84AA256B,0x6F9D9E68,0x805FF556,0x6283EE2F,0x8D418511,0x66763E12,0x89B4552C,
+    0x5F118F49,0xB0D3E477,0x5BE45F74,0xB426344A,0x56FA2F33,0xB938440D,0x520FFF0E,0xBDCD9430,
+    0x4CC6CFBD,0xA304A483,0x48331F80,0xA7F174BE,0x452D6FC7,0xAAEF04F9,0x41D8BFFA,0xAE1AD4C4,
+    0x37E20D71,0xD820664F,0x3317DD4C,0xDCD5B672,0x3E09AD0B,0xD1CBC635,0x3AFC7D36,0xD53E1608,
+    0x24354D85,0xCBF726BB,0x20C09DB8,0xCF02F686,0x2DDEEDFF,0xC21C86C1,0x292B3DC2,0xC6E956FC,
+    0x104C8C99,0xFF8EE7A7,0x14B95CA4,0xFB7B379A,0x19A72CE3,0xF66547DD,0x1D52FCDE,0xF29097E0,
+    0x039BCC6D,0xEC59A753,0x076E1C50,0xE8AC776E,0x0A706C17,0xE5B20729,0x0E85BC2A,0xE147D714,
+  },
+
+  {
+    0x00000000,0xC18EDFC0,0x586CB9C1,0x99E26601,0xB0D97382,0x7157AC42,0xE8B5CA43,0x293B1583,
+    0xBAC3E145,0x7B4D3E85,0xE2AF5884,0x23218744,0x0A1A92C7,0xCB944D07,0x52762B06,0x93F8F4C6,
+    0xAEF6C4CB,0x6F781B0B,0xF69A7D0A,0x3714A2CA,0x1E2FB749,0xDFA16889,0x46430E88,0x87CDD148,
+    0x1435258E,0xD5BBFA4E,0x4C599C4F,0x8DD7438F,0xA4EC560C,0x656289CC,0xFC80EFCD,0x3D0E300D,
+    0x869C8FD7,0x47125017,0xDEF03616,0x1F7EE9D6,0x3645FC55,0xF7CB2395,0x6E294594,0xAFA79A54,
+    0x3C5F6E92,0xFDD1B152,0x6433D753,0xA5BD0893,0x8C861D10,0x4D08C2D0,0xD4EAA4D1,0x15647B11,
+    0x286A4B1C,0xE9E494DC,0x7006F2DD,0xB1882D1D,0x98B3389E,0x593DE75E,0xC0DF815F,0x01515E9F,
+    0x92A9AA59,0x53277599,0xCAC51398,0x0B4BCC58,0x2270D9DB,0xE3FE061B,0x7A1C601A,0xBB92BFDA,
+    0xD64819EF,0x17C6C62F,0x8E24A02E,0x4FAA7FEE,0x66916A6D,0xA71FB5AD,0x3EFDD3AC,0xFF730C6C,
+    0x6C8BF8AA,0xAD05276A,0x34E7416B,0xF5699EAB,0xDC528B28,0x1DDC54E8,0x843E32E9,0x45B0ED29,
+    0x78BEDD24,0xB93002E4,0x20D264E5,0xE15CBB25,0xC867AEA6,0x09E97166,0x900B1767,0x5185C8A7,
+    0xC27D3C61,0x03F3E3A1,0x9A1185A0,0x5B9F5A60,0x72A44FE3,0xB32A9023,0x2AC8F622,0xEB4629E2,
+    0x50D49638,0x915A49F8,0x08B82FF9,0xC936F039,0xE00DE5BA,0x21833A7A,0xB8615C7B,0x79EF83BB,
+    0xEA17777D,0x2B99A8BD,0xB27BCEBC,0x73F5117C,0x5ACE04FF,0x9B40DB3F,0x02A2BD3E,0xC32C62FE,
+    0xFE2252F3,0x3FAC8D33,0xA64EEB32,0x67C034F2,0x4EFB2171,0x8F75FEB1,0x169798B0,0xD7194770,
+    0x44E1B3B6,0x856F6C76,0x1C8D0A77,0xDD03D5B7,0xF438C034,0x35B61FF4,0xAC5479F5,0x6DDAA635,
+    0x77E1359F,0xB66FEA5F,0x2F8D8C5E,0xEE03539E,0xC738461D,0x06B699DD,0x9F54FFDC,0x5EDA201C,
+    0xCD22D4DA,0x0CAC0B1A,0x954E6D1B,0x54C0B2DB,0x7DFBA758,0xBC757898,0x25971E99,0xE419C159,
+    0xD917F154,0x18992E94,0x817B4895,0x40F59755,0x69CE82D6,0xA8405D16,0x31A23B17,0xF02CE4D7,
+    0x63D41011,0xA25ACFD1,0x3BB8A9D0,0xFA367610,0xD30D6393,0x1283BC53,0x8B61DA52,0x4AEF0592,
+    0xF17DBA48,0x30F36588,0xA9110389,0x689FDC49,0x41A4C9CA,0x802A160A,0x19C8700B,0xD846AFCB,
+    0x4BBE5B0D,0x8A3084CD,0x13D2E2CC,0xD25C3D0C,0xFB67288F,0x3AE9F74F,0xA30B914E,0x62854E8E,
+    0x5F8B7E83,0x9E05A143,0x07E7C742,0xC6691882,0xEF520D01,0x2EDCD2C1,0xB73EB4C0,0x76B06B00,
+    0xE5489FC6,0x24C64006,0xBD242607,0x7CAAF9C7,0x5591EC44,0x941F3384,0x0DFD5585,0xCC738A45,
+    0xA1A92C70,0x6027F3B0,0xF9C595B1,0x384B4A71,0x11705FF2,0xD0FE8032,0x491CE633,0x889239F3,
+    0x1B6ACD35,0xDAE412F5,0x430674F4,0x8288AB34,0xABB3BEB7,0x6A3D6177,0xF3DF0776,0x3251D8B6,
+    0x0F5FE8BB,0xCED1377B,0x5733517A,0x96BD8EBA,0xBF869B39,0x7E0844F9,0xE7EA22F8,0x2664FD38,
+    0xB59C09FE,0x7412D63E,0xEDF0B03F,0x2C7E6FFF,0x05457A7C,0xC4CBA5BC,0x5D29C3BD,0x9CA71C7D,
+    0x2735A3A7,0xE6BB7C67,0x7F591A66,0xBED7C5A6,0x97ECD025,0x56620FE5,0xCF8069E4,0x0E0EB624,
+    0x9DF642E2,0x5C789D22,0xC59AFB23,0x041424E3,0x2D2F3160,0xECA1EEA0,0x754388A1,0xB4CD5761,
+    0x89C3676C,0x484DB8AC,0xD1AFDEAD,0x1021016D,0x391A14EE,0xF894CB2E,0x6176AD2F,0xA0F872EF,
+    0x33008629,0xF28E59E9,0x6B6C3FE8,0xAAE2E028,0x83D9F5AB,0x42572A6B,0xDBB54C6A,0x1A3B93AA,
+  },
+
+  {
+    0x00000000,0x9BA54C6F,0xEC3B9E9F,0x779ED2F0,0x03063B7F,0x98A37710,0xEF3DA5E0,0x7498E98F,
+    0x060C76FE,0x9DA93A91,0xEA37E861,0x7192A40E,0x050A4D81,0x9EAF01EE,0xE931D31E,0x72949F71,
+    0x0C18EDFC,0x97BDA193,0xE0237363,0x7B863F0C,0x0F1ED683,0x94BB9AEC,0xE325481C,0x78800473,
+    0x0A149B02,0x91B1D76D,0xE62F059D,0x7D8A49F2,0x0912A07D,0x92B7EC12,0xE5293EE2,0x7E8C728D,
+    0x1831DBF8,0x83949797,0xF40A4567,0x6FAF0908,0x1B37E087,0x8092ACE8,0xF70C7E18,0x6CA93277,
+    0x1E3DAD06,0x8598E169,0xF2063399,0x69A37FF6,0x1D3B9679,0x869EDA16,0xF10008E6,0x6AA54489,
+    0x14293604,0x8F8C7A6B,0xF812A89B,0x63B7E4F4,0x172F0D7B,0x8C8A4114,0xFB1493E4,0x60B1DF8B,
+    0x122540FA,0x89800C95,0xFE1EDE65,0x65BB920A,0x11237B85,0x8A8637EA,0xFD18E51A,0x66BDA975,
+    0x3063B7F0,0xABC6FB9F,0xDC58296F,0x47FD6500,0x33658C8F,0xA8C0C0E0,0xDF5E1210,0x44FB5E7F,
+    0x366FC10E,0xADCA8D61,0xDA545F91,0x41F113FE,0x3569FA71,0xAECCB61E,0xD95264EE,0x42F72881,
+    0x3C7B5A0C,0xA7DE1663,0xD040C493,0x4BE588FC,0x3F7D6173,0xA4D82D1C,0xD346FFEC,0x48E3B383,
+    0x3A772CF2,0xA1D2609D,0xD64CB26D,0x4DE9FE02,0x3971178D,0xA2D45BE2,0xD54A8912,0x4EEFC57D,
+    0x28526C08,0xB3F72067,0xC469F297,0x5FCCBEF8,0x2B545777,0xB0F11B18,0xC76FC9E8,0x5CCA8587,
+    0x2E5E1AF6,0xB5FB5699,0xC2658469,0x59C0C806,0x2D582189,0xB6FD6DE6,0xC163BF16,0x5AC6F379,
+    0x244A81F4,0xBFEFCD9B,0xC8711F6B,0x53D45304,0x274CBA8B,0xBCE9F6E4,0xCB772414,0x50D2687B,
+    0x2246F70A,0xB9E3BB65,0xCE7D6995,0x55D825FA,0x2140CC75,0xBAE5801A,0xCD7B52EA,0x56DE1E85,
+    0x60C76FE0,0xFB62238F,0x8CFCF17F,0x1759BD10,0x63C1549F,0xF86418F0,0x8FFACA00,0x145F866F,
+    0x66CB191E,0xFD6E5571,0x8AF08781,0x1155CBEE,0x65CD2261,0xFE686E0E,0x89F6BCFE,0x1253F091,
+    0x6CDF821C,0xF77ACE73,0x80E41C83,0x1B4150EC,0x6FD9B963,0xF47CF50C,0x83E227FC,0x18476B93,
+    0x6AD3F4E2,0xF176B88D,0x86E86A7D,0x1D4D2612,0x69D5CF9D,0xF27083F2,0x85EE5102,0x1E4B1D6D,
+    0x78F6B418,0xE353F877,0x94CD2A87,0x0F6866E8,0x7BF08F67,0xE055C308,0x97CB11F8,0x0C6E5D97,
+    0x7EFAC2E6,0xE55F8E89,0x92C15C79,0x09641016,0x7DFCF999,0xE659B5F6,0x91C76706,0x0A622B69,
+    0x74EE59E4,0xEF4B158B,0x98D5C77B,0x03708B14,0x77E8629B,0xEC4D2EF4,0x9BD3FC04,0x0076B06B,
+    0x72E22F1A,0xE9476375,0x9ED9B185,0x057CFDEA,0x71E41465,0xEA41580A,0x9DDF8AFA,0x067AC695,
+    0x50A4D810,0xCB01947F,0xBC9F468F,0x273A0AE0,0x53A2E36F,0xC807AF00,0xBF997DF0,0x243C319F,
+    0x56A8AEEE,0xCD0DE281,0xBA933071,0x21367C1E,0x55AE9591,0xCE0BD9FE,0xB9950B0E,0x22304761,
+    0x5CBC35EC,0xC7197983,0xB087AB73,0x2B22E71C,0x5FBA0E93,0xC41F42FC,0xB381900C,0x2824DC63,
+    0x5AB04312,0xC1150F7D,0xB68BDD8D,0x2D2E91E2,0x59B6786D,0xC2133402,0xB58DE6F2,0x2E28AA9D,
+    0x489503E8,0xD3304F87,0xA4AE9D77,0x3F0BD118,0x4B933897,0xD03674F8,0xA7A8A608,0x3C0DEA67,
+    0x4E997516,0xD53C3979,0xA2A2EB89,0x3907A7E6,0x4D9F4E69,0xD63A0206,0xA1A4D0F6,0x3A019C99,
+    0x448DEE14,0xDF28A27B,0xA8B6708B,0x33133CE4,0x478BD56B,0xDC2E9904,0xABB04BF4,0x3015079B,
+    0x428198EA,0xD924D485,0xAEBA0675,0x351F4A1A,0x4187A395,0xDA22EFFA,0xADBC3D0A,0x36197165,
+  },
+
+  {
+    0x00000000,0xDD96D985,0x605CB54B,0xBDCA6CCE,0xC0B96A96,0x1D2FB313,0xA0E5DFDD,0x7D730658,
+    0x5A03D36D,0x87950AE8,0x3A5F6626,0xE7C9BFA3,0x9ABAB9FB,0x472C607E,0xFAE60CB0,0x2770D535,
+    0xB407A6DA,0x69917F5F,0xD45B1391,0x09CDCA14,0x74BECC4C,0xA92815C9,0x14E27907,0xC974A082,
+    0xEE0475B7,0x3392AC32,0x8E58C0FC,0x53CE1979,0x2EBD1F21,0xF32BC6A4,0x4EE1AA6A,0x937773EF,
+    0xB37E4BF5,0x6EE89270,0xD322FEBE,0x0EB4273B,0x73C72163,0xAE51F8E6,0x139B9428,0xCE0D4DAD,
+    0xE97D9898,0x34EB411D,0x89212DD3,0x54B7F456,0x29C4F20E,0xF4522B8B,0x49984745,0x940E9EC0,
+    0x0779ED2F,0xDAEF34AA,0x67255864,0xBAB381E1,0xC7C087B9,0x1A565E3C,0xA79C32F2,0x7A0AEB77,
+    0x5D7A3E42,0x80ECE7C7,0x3D268B09,0xE0B0528C,0x9DC354D4,0x40558D51,0xFD9FE19F,0x2009381A,
+    0xBD8D91AB,0x601B482E,0xDDD124E0,0x0047FD65,0x7D34FB3D,0xA0A222B8,0x1D684E76,0xC0FE97F3,
+    0xE78E42C6,0x3A189B43,0x87D2F78D,0x5A442E08,0x27372850,0xFAA1F1D5,0x476B9D1B,0x9AFD449E,
+    0x098A3771,0xD41CEEF4,0x69D6823A,0xB4405BBF,0xC9335DE7,0x14A58462,0xA96FE8AC,0x74F93129,
+    0x5389E41C,0x8E1F3D99,0x33D55157,0xEE4388D2,0x93308E8A,0x4EA6570F,0xF36C3BC1,0x2EFAE244,
+    0x0EF3DA5E,0xD36503DB,0x6EAF6F15,0xB339B690,0xCE4AB0C8,0x13DC694D,0xAE160583,0x7380DC06,
+    0x54F00933,0x8966D0B6,0x34ACBC78,0xE93A65FD,0x944963A5,0x49DFBA20,0xF415D6EE,0x29830F6B,
+    0xBAF47C84,0x6762A501,0xDAA8C9CF,0x073E104A,0x7A4D1612,0xA7DBCF97,0x1A11A359,0xC7877ADC,
+    0xE0F7AFE9,0x3D61766C,0x80AB1AA2,0x5D3DC327,0x204EC57F,0xFDD81CFA,0x40127034,0x9D84A9B1,
+    0xA06A2517,0x7DFCFC92,0xC036905C,0x1DA049D9,0x60D34F81,0xBD459604,0x008FFACA,0xDD19234F,
+    0xFA69F67A,0x27FF2FFF,0x9A354331,0x47A39AB4,0x3AD09CEC,0xE7464569,0x5A8C29A7,0x871AF022,
+    0x146D83CD,0xC9FB5A48,0x74313686,0xA9A7EF03,0xD4D4E95B,0x094230DE,0xB4885C10,0x691E8595,
+    0x4E6E50A0,0x93F88925,0x2E32E5EB,0xF3A43C6E,0x8ED73A36,0x5341E3B3,0xEE8B8F7D,0x331D56F8,
+    0x13146EE2,0xCE82B767,0x7348DBA9,0xAEDE022C,0xD3AD0474,0x0E3BDDF1,0xB3F1B13F,0x6E6768BA,
+    0x4917BD8F,0x9481640A,0x294B08C4,0xF4DDD141,0x89AED719,0x54380E9C,0xE9F26252,0x3464BBD7,
+    0xA713C838,0x7A8511BD,0xC74F7D73,0x1AD9A4F6,0x67AAA2AE,0xBA3C7B2B,0x07F617E5,0xDA60CE60,
+    0xFD101B55,0x2086C2D0,0x9D4CAE1E,0x40DA779B,0x3DA971C3,0xE03FA846,0x5DF5C488,0x80631D0D,
+    0x1DE7B4BC,0xC0716D39,0x7DBB01F7,0xA02DD872,0xDD5EDE2A,0x00C807AF,0xBD026B61,0x6094B2E4,
+    0x47E467D1,0x9A72BE54,0x27B8D29A,0xFA2E0B1F,0x875D0D47,0x5ACBD4C2,0xE701B80C,0x3A976189,
+    0xA9E01266,0x7476CBE3,0xC9BCA72D,0x142A7EA8,0x695978F0,0xB4CFA175,0x0905CDBB,0xD493143E,
+    0xF3E3C10B,0x2E75188E,0x93BF7440,0x4E29ADC5,0x335AAB9D,0xEECC7218,0x53061ED6,0x8E90C753,
+    0xAE99FF49,0x730F26CC,0xCEC54A02,0x13539387,0x6E2095DF,0xB3B64C5A,0x0E7C2094,0xD3EAF911,
+    0xF49A2C24,0x290CF5A1,0x94C6996F,0x495040EA,0x342346B2,0xE9B59F37,0x547FF3F9,0x89E92A7C,
+    0x1A9E5993,0xC7088016,0x7AC2ECD8,0xA754355D,0xDA273305,0x07B1EA80,0xBA7B864E,0x67ED5FCB,
+    0x409D8AFE,0x9D0B537B,0x20C13FB5,0xFD57E630,0x8024E068,0x5DB239ED,0xE0785523,0x3DEE8CA6,
+  },
+
+  {
+    0x00000000,0x9D0FE176,0xE16EC4AD,0x7C6125DB,0x19AC8F1B,0x84A36E6D,0xF8C24BB6,0x65CDAAC0,
+    0x33591E36,0xAE56FF40,0xD237DA9B,0x4F383BED,0x2AF5912D,0xB7FA705B,0xCB9B5580,0x5694B4F6,
+    0x66B23C6C,0xFBBDDD1A,0x87DCF8C1,0x1AD319B7,0x7F1EB377,0xE2115201,0x9E7077DA,0x037F96AC,
+    0x55EB225A,0xC8E4C32C,0xB485E6F7,0x298A0781,0x4C47AD41,0xD1484C37,0xAD2969EC,0x3026889A,
+    0xCD6478D8,0x506B99AE,0x2C0ABC75,0xB1055D03,0xD4C8F7C3,0x49C716B5,0x35A6336E,0xA8A9D218,
+    0xFE3D66EE,0x63328798,0x1F53A243,0x825C4335,0xE791E9F5,0x7A9E0883,0x06FF2D58,0x9BF0CC2E,
+    0xABD644B4,0x36D9A5C2,0x4AB88019,0xD7B7616F,0xB27ACBAF,0x2F752AD9,0x53140F02,0xCE1BEE74,
+    0x988F5A82,0x0580BBF4,0x79E19E2F,0xE4EE7F59,0x8123D599,0x1C2C34EF,0x604D1134,0xFD42F042,
+    0x41B9F7F1,0xDCB61687,0xA0D7335C,0x3DD8D22A,0x581578EA,0xC51A999C,0xB97BBC47,0x24745D31,
+    0x72E0E9C7,0xEFEF08B1,0x938E2D6A,0x0E81CC1C,0x6B4C66DC,0xF64387AA,0x8A22A271,0x172D4307,
+    0x270BCB9D,0xBA042AEB,0xC6650F30,0x5B6AEE46,0x3EA74486,0xA3A8A5F0,0xDFC9802B,0x42C6615D,
+    0x1452D5AB,0x895D34DD,0xF53C1106,0x6833F070,0x0DFE5AB0,0x90F1BBC6,0xEC909E1D,0x719F7F6B,
+    0x8CDD8F29,0x11D26E5F,0x6DB34B84,0xF0BCAAF2,0x95710032,0x087EE144,0x741FC49F,0xE91025E9,
+    0xBF84911F,0x228B7069,0x5EEA55B2,0xC3E5B4C4,0xA6281E04,0x3B27FF72,0x4746DAA9,0xDA493BDF,
+    0xEA6FB345,0x77605233,0x0B0177E8,0x960E969E,0xF3C33C5E,0x6ECCDD28,0x12ADF8F3,0x8FA21985,
+    0xD936AD73,0x44394C05,0x385869DE,0xA55788A8,0xC09A2268,0x5D95C31E,0x21F4E6C5,0xBCFB07B3,
+    0x8373EFE2,0x1E7C0E94,0x621D2B4F,0xFF12CA39,0x9ADF60F9,0x07D0818F,0x7BB1A454,0xE6BE4522,
+    0xB02AF1D4,0x2D2510A2,0x51443579,0xCC4BD40F,0xA9867ECF,0x34899FB9,0x48E8BA62,0xD5E75B14,
+    0xE5C1D38E,0x78CE32F8,0x04AF1723,0x99A0F655,0xFC6D5C95,0x6162BDE3,0x1D039838,0x800C794E,
+    0xD698CDB8,0x4B972CCE,0x37F60915,0xAAF9E863,0xCF3442A3,0x523BA3D5,0x2E5A860E,0xB3556778,
+    0x4E17973A,0xD318764C,0xAF795397,0x3276B2E1,0x57BB1821,0xCAB4F957,0xB6D5DC8C,0x2BDA3DFA,
+    0x7D4E890C,0xE041687A,0x9C204DA1,0x012FACD7,0x64E20617,0xF9EDE761,0x858CC2BA,0x188323CC,
+    0x28A5AB56,0xB5AA4A20,0xC9CB6FFB,0x54C48E8D,0x3109244D,0xAC06C53B,0xD067E0E0,0x4D680196,
+    0x1BFCB560,0x86F35416,0xFA9271CD,0x679D90BB,0x02503A7B,0x9F5FDB0D,0xE33EFED6,0x7E311FA0,
+    0xC2CA1813,0x5FC5F965,0x23A4DCBE,0xBEAB3DC8,0xDB669708,0x4669767E,0x3A0853A5,0xA707B2D3,
+    0xF1930625,0x6C9CE753,0x10FDC288,0x8DF223FE,0xE83F893E,0x75306848,0x09514D93,0x945EACE5,
+    0xA478247F,0x3977C509,0x4516E0D2,0xD81901A4,0xBDD4AB64,0x20DB4A12,0x5CBA6FC9,0xC1B58EBF,
+    0x97213A49,0x0A2EDB3F,0x764FFEE4,0xEB401F92,0x8E8DB552,0x13825424,0x6FE371FF,0xF2EC9089,
+    0x0FAE60CB,0x92A181BD,0xEEC0A466,0x73CF4510,0x1602EFD0,0x8B0D0EA6,0xF76C2B7D,0x6A63CA0B,
+    0x3CF77EFD,0xA1F89F8B,0xDD99BA50,0x40965B26,0x255BF1E6,0xB8541090,0xC435354B,0x593AD43D,
+    0x691C5CA7,0xF413BDD1,0x8872980A,0x157D797C,0x70B0D3BC,0xEDBF32CA,0x91DE1711,0x0CD1F667,
+    0x5A454291,0xC74AA3E7,0xBB2B863C,0x2624674A,0x43E9CD8A,0xDEE62CFC,0xA2870927,0x3F88E851,
+  },
+
+  {
+    0x00000000,0xB9FBDBE8,0xA886B191,0x117D6A79,0x8A7C6563,0x3387BE8B,0x22FAD4F2,0x9B010F1A,
+    0xCF89CC87,0x7672176F,0x670F7D16,0xDEF4A6FE,0x45F5A9E4,0xFC0E720C,0xED731875,0x5488C39D,
+    0x44629F4F,0xFD9944A7,0xECE42EDE,0x551FF536,0xCE1EFA2C,0x77E521C4,0x66984BBD,0xDF639055,
+    0x8BEB53C8,0x32108820,0x236DE259,0x9A9639B1,0x019736AB,0xB86CED43,0xA911873A,0x10EA5CD2,
+    0x88C53E9E,0x313EE576,0x20438F0F,0x99B854E7,0x02B95BFD,0xBB428015,0xAA3FEA6C,0x13C43184,
+    0x474CF219,0xFEB729F1,0xEFCA4388,0x56319860,0xCD30977A,0x74CB4C92,0x65B626EB,0xDC4DFD03,
+    0xCCA7A1D1,0x755C7A39,0x64211040,0xDDDACBA8,0x46DBC4B2,0xFF201F5A,0xEE5D7523,0x57A6AECB,
+    0x032E6D56,0xBAD5B6BE,0xABA8DCC7,0x1253072F,0x89520835,0x30A9D3DD,0x21D4B9A4,0x982F624C,
+    0xCAFB7B7D,0x7300A095,0x627DCAEC,0xDB861104,0x40871E1E,0xF97CC5F6,0xE801AF8F,0x51FA7467,
+    0x0572B7FA,0xBC896C12,0xADF4066B,0x140FDD83,0x8F0ED299,0x36F50971,0x27886308,0x9E73B8E0,
+    0x8E99E432,0x37623FDA,0x261F55A3,0x9FE48E4B,0x04E58151,0xBD1E5AB9,0xAC6330C0,0x1598EB28,
+    0x411028B5,0xF8EBF35D,0xE9969924,0x506D42CC,0xCB6C4DD6,0x7297963E,0x63EAFC47,0xDA1127AF,
+    0x423E45E3,0xFBC59E0B,0xEAB8F472,0x53432F9A,0xC8422080,0x71B9FB68,0x60C49111,0xD93F4AF9,
+    0x8DB78964,0x344C528C,0x253138F5,0x9CCAE31D,0x07CBEC07,0xBE3037EF,0xAF4D5D96,0x16B6867E,
+    0x065CDAAC,0xBFA70144,0xAEDA6B3D,0x1721B0D5,0x8C20BFCF,0x35DB6427,0x24A60E5E,0x9D5DD5B6,
+    0xC9D5162B,0x702ECDC3,0x6153A7BA,0xD8A87C52,0x43A97348,0xFA52A8A0,0xEB2FC2D9,0x52D41931,
+    0x4E87F0BB,0xF77C2B53,0xE601412A,0x5FFA9AC2,0xC4FB95D8,0x7D004E30,0x6C7D2449,0xD586FFA1,
+    0x810E3C3C,0x38F5E7D4,0x29888DAD,0x90735645,0x0B72595F,0xB28982B7,0xA3F4E8CE,0x1A0F3326,
+    0x0AE56FF4,0xB31EB41C,0xA263DE65,0x1B98058D,0x80990A97,0x3962D17F,0x281FBB06,0x91E460EE,
+    0xC56CA373,0x7C97789B,0x6DEA12E2,0xD411C90A,0x4F10C610,0xF6EB1DF8,0xE7967781,0x5E6DAC69,
+    0xC642CE25,0x7FB915CD,0x6EC47FB4,0xD73FA45C,0x4C3EAB46,0xF5C570AE,0xE4B81AD7,0x5D43C13F,
+    0x09CB02A2,0xB030D94A,0xA14DB333,0x18B668DB,0x83B767C1,0x3A4CBC29,0x2B31D650,0x92CA0DB8,
+    0x8220516A,0x3BDB8A82,0x2AA6E0FB,0x935D3B13,0x085C3409,0xB1A7EFE1,0xA0DA8598,0x19215E70,
+    0x4DA99DED,0xF4524605,0xE52F2C7C,0x5CD4F794,0xC7D5F88E,0x7E2E2366,0x6F53491F,0xD6A892F7,
+    0x847C8BC6,0x3D87502E,0x2CFA3A57,0x9501E1BF,0x0E00EEA5,0xB7FB354D,0xA6865F34,0x1F7D84DC,
+    0x4BF54741,0xF20E9CA9,0xE373F6D0,0x5A882D38,0xC1892222,0x7872F9CA,0x690F93B3,0xD0F4485B,
+    0xC01E1489,0x79E5CF61,0x6898A518,0xD1637EF0,0x4A6271EA,0xF399AA02,0xE2E4C07B,0x5B1F1B93,
+    0x0F97D80E,0xB66C03E6,0xA711699F,0x1EEAB277,0x85EBBD6D,0x3C106685,0x2D6D0CFC,0x9496D714,
+    0x0CB9B558,0xB5426EB0,0xA43F04C9,0x1DC4DF21,0x86C5D03B,0x3F3E0BD3,0x2E4361AA,0x97B8BA42,
+    0xC33079DF,0x7ACBA237,0x6BB6C84E,0xD24D13A6,0x494C1CBC,0xF0B7C754,0xE1CAAD2D,0x583176C5,
+    0x48DB2A17,0xF120F1FF,0xE05D9B86,0x59A6406E,0xC2A74F74,0x7B5C949C,0x6A21FEE5,0xD3DA250D,
+    0x8752E690,0x3EA93D78,0x2FD45701,0x962F8CE9,0x0D2E83F3,0xB4D5581B,0xA5A83262,0x1C53E98A,
+  },
+
+  {
+    0x00000000,0xAE689191,0x87A02563,0x29C8B4F2,0xD4314C87,0x7A59DD16,0x539169E4,0xFDF9F875,
+    0x73139F4F,0xDD7B0EDE,0xF4B3BA2C,0x5ADB2BBD,0xA722D3C8,0x094A4259,0x2082F6AB,0x8EEA673A,
+    0xE6273E9E,0x484FAF0F,0x61871BFD,0xCFEF8A6C,0x32167219,0x9C7EE388,0xB5B6577A,0x1BDEC6EB,
+    0x9534A1D1,0x3B5C3040,0x129484B2,0xBCFC1523,0x4105ED56,0xEF6D7CC7,0xC6A5C835,0x68CD59A4,
+    0x173F7B7D,0xB957EAEC,0x909F5E1E,0x3EF7CF8F,0xC30E37FA,0x6D66A66B,0x44AE1299,0xEAC68308,
+    0x642CE432,0xCA4475A3,0xE38CC151,0x4DE450C0,0xB01DA8B5,0x1E753924,0x37BD8DD6,0x99D51C47,
+    0xF11845E3,0x5F70D472,0x76B86080,0xD8D0F111,0x25290964,0x8B4198F5,0xA2892C07,0x0CE1BD96,
+    0x820BDAAC,0x2C634B3D,0x05ABFFCF,0xABC36E5E,0x563A962B,0xF85207BA,0xD19AB348,0x7FF222D9,
+    0x2E7EF6FA,0x8016676B,0xA9DED399,0x07B64208,0xFA4FBA7D,0x54272BEC,0x7DEF9F1E,0xD3870E8F,
+    0x5D6D69B5,0xF305F824,0xDACD4CD6,0x74A5DD47,0x895C2532,0x2734B4A3,0x0EFC0051,0xA09491C0,
+    0xC859C864,0x663159F5,0x4FF9ED07,0xE1917C96,0x1C6884E3,0xB2001572,0x9BC8A180,0x35A03011,
+    0xBB4A572B,0x1522C6BA,0x3CEA7248,0x9282E3D9,0x6F7B1BAC,0xC1138A3D,0xE8DB3ECF,0x46B3AF5E,
+    0x39418D87,0x97291C16,0xBEE1A8E4,0x10893975,0xED70C100,0x43185091,0x6AD0E463,0xC4B875F2,
+    0x4A5212C8,0xE43A8359,0xCDF237AB,0x639AA63A,0x9E635E4F,0x300BCFDE,0x19C37B2C,0xB7ABEABD,
+    0xDF66B319,0x710E2288,0x58C6967A,0xF6AE07EB,0x0B57FF9E,0xA53F6E0F,0x8CF7DAFD,0x229F4B6C,
+    0xAC752C56,0x021DBDC7,0x2BD50935,0x85BD98A4,0x784460D1,0xD62CF140,0xFFE445B2,0x518CD423,
+    0x5CFDEDF4,0xF2957C65,0xDB5DC897,0x75355906,0x88CCA173,0x26A430E2,0x0F6C8410,0xA1041581,
+    0x2FEE72BB,0x8186E32A,0xA84E57D8,0x0626C649,0xFBDF3E3C,0x55B7AFAD,0x7C7F1B5F,0xD2178ACE,
+    0xBADAD36A,0x14B242FB,0x3D7AF609,0x93126798,0x6EEB9FED,0xC0830E7C,0xE94BBA8E,0x47232B1F,
+    0xC9C94C25,0x67A1DDB4,0x4E696946,0xE001F8D7,0x1DF800A2,0xB3909133,0x9A5825C1,0x3430B450,
+    0x4BC29689,0xE5AA0718,0xCC62B3EA,0x620A227B,0x9FF3DA0E,0x319B4B9F,0x1853FF6D,0xB63B6EFC,
+    0x38D109C6,0x96B99857,0xBF712CA5,0x1119BD34,0xECE04541,0x4288D4D0,0x6B406022,0xC528F1B3,
+    0xADE5A817,0x038D3986,0x2A458D74,0x842D1CE5,0x79D4E490,0xD7BC7501,0xFE74C1F3,0x501C5062,
+    0xDEF63758,0x709EA6C9,0x5956123B,0xF73E83AA,0x0AC77BDF,0xA4AFEA4E,0x8D675EBC,0x230FCF2D,
+    0x72831B0E,0xDCEB8A9F,0xF5233E6D,0x5B4BAFFC,0xA6B25789,0x08DAC618,0x211272EA,0x8F7AE37B,
+    0x01908441,0xAFF815D0,0x8630A122,0x285830B3,0xD5A1C8C6,0x7BC95957,0x5201EDA5,0xFC697C34,
+    0x94A42590,0x3ACCB401,0x130400F3,0xBD6C9162,0x40956917,0xEEFDF886,0xC7354C74,0x695DDDE5,
+    0xE7B7BADF,0x49DF2B4E,0x60179FBC,0xCE7F0E2D,0x3386F658,0x9DEE67C9,0xB426D33B,0x1A4E42AA,
+    0x65BC6073,0xCBD4F1E2,0xE21C4510,0x4C74D481,0xB18D2CF4,0x1FE5BD65,0x362D0997,0x98459806,
+    0x16AFFF3C,0xB8C76EAD,0x910FDA5F,0x3F674BCE,0xC29EB3BB,0x6CF6222A,0x453E96D8,0xEB560749,
+    0x839B5EED,0x2DF3CF7C,0x043B7B8E,0xAA53EA1F,0x57AA126A,0xF9C283FB,0xD00A3709,0x7E62A698,
+    0xF088C1A2,0x5EE05033,0x7728E4C1,0xD9407550,0x24B98D25,0x8AD11CB4,0xA319A846,0x0D7139D7,
+  }
+#endif // CRC32_USE_LOOKUP_TABLE_SLICING_BY_16
+};
+#endif
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/file_adapter.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/file_adapter.h
new file mode 100644
index 0000000000000000000000000000000000000000..23307abc904d0cd3235cd36559ce4b4c71916ee6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/file_adapter.h
@@ -0,0 +1,36 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <fstream>
+#include <memory>
+
+#include "caffe2/serialize/istream_adapter.h"
+#include "caffe2/serialize/read_adapter_interface.h"
+
+namespace caffe2 {
+namespace serialize {
+
+class TORCH_API FileAdapter final : public ReadAdapterInterface {
+ public:
+  C10_DISABLE_COPY_AND_ASSIGN(FileAdapter);
+  explicit FileAdapter(const std::string& file_name);
+  size_t size() const override;
+  size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
+      const override;
+  ~FileAdapter() override;
+
+ private:
+  // An RAII Wrapper for a FILE pointer. Closes on destruction.
+  struct RAIIFile {
+    FILE* fp_;
+    explicit RAIIFile(const std::string& file_name);
+    ~RAIIFile();
+  };
+
+  RAIIFile file_;
+  // The size of the opened file in bytes
+  uint64_t size_;
+};
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9f5619ac3bc54205c79426d02e4cdf683eda09a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/in_memory_adapter.h
@@ -0,0 +1,30 @@
+#pragma once
+#include <caffe2/serialize/read_adapter_interface.h>
+#include <cstring>
+
+namespace caffe2 {
+namespace serialize {
+
+class MemoryReadAdapter final : public caffe2::serialize::ReadAdapterInterface {
+ public:
+  explicit MemoryReadAdapter(const void* data, off_t size)
+      : data_(data), size_(size) {}
+
+  size_t size() const override {
+    return size_;
+  }
+
+  size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
+      const override {
+    (void)what;
+    memcpy(buf, (int8_t*)(data_) + pos, n);
+    return n;
+  }
+
+ private:
+  const void* data_;
+  off_t size_;
+};
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/inline_container.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/inline_container.h
new file mode 100644
index 0000000000000000000000000000000000000000..59e0991399a3a5d3592af092a97639f5dfbddf7c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/inline_container.h
@@ -0,0 +1,295 @@
+#pragma once
+
+#include <cerrno>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <istream>
+#include <mutex>
+#include <ostream>
+#include <unordered_set>
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Backend.h>
+
+#include "caffe2/serialize/istream_adapter.h"
+#include "caffe2/serialize/read_adapter_interface.h"
+#include "caffe2/serialize/versions.h"
+
+extern "C" {
+typedef struct mz_zip_archive mz_zip_archive;
+}
+
+// PyTorch containers are a special zip archive with the following layout
+// archive_name.zip contains:
+//    archive_name/
+//        version # a file with a single decimal number written in ascii,
+//                # used to establish the version of the archive format
+//        model.json # overall model description, this is a json output of
+//                   # ModelDef from torch.proto
+//        # the following names are by convention only, model.json will
+//        # refer to these files by full names
+//        tensors/
+//          0 # flat storage for tensor data, meta-data about shapes, etc. is
+//            # in model.json
+//          1
+//          ...
+//        # code entries will only exist for modules that have methods attached
+//        code/
+//          archive_name.py # serialized torch script code (python syntax, using
+//          PythonPrint) archive_name_my_submodule.py # submodules have separate
+//          files
+//
+// The PyTorchStreamWriter also ensures additional useful properties for these
+// files
+// 1. All files are stored uncompressed.
+// 2. All files in the archive are aligned to 64 byte boundaries such that
+//    it is possible to mmap the entire file and get an aligned pointer to
+//    tensor data.
+// 3. We universally write in ZIP64 format for consistency.
+
+// The PyTorchStreamReader also provides additional properties:
+// 1. It can read zip files that are created with common
+//    zip tools. This means that even though our writer doesn't compress files,
+//    the reader can still read files that were compressed.
+// 2. It provides a getRecordOffset function which returns the offset into the
+//    raw file where file data lives. If the file was written with
+//    PyTorchStreamWriter it is guaranteed to be 64 byte aligned.
+
+// PyTorchReader/Writer handle checking the version number on the archive format
+// and ensure that all files are written to a archive_name directory so they
+// unzip cleanly.
+
+// When developing this format we want to pay particular attention to the
+// following use cases:
+//
+// -- Reading --
+// 1) Reading with full random access
+//   a) Reading with file api's such as fread()
+//   b) mmaping the file and jumping around the mapped region
+// 2) Reading with 1-pass sequential access
+//      -> A reader will need to build up a data structure of parsed structures
+//         as it reads
+//
+// -- Writing --
+// 1) Writing with full random access
+// 2) Writing with 1-pass sequential access
+//      -> We must take care not to require updating values that have already
+//         been written. We place the variable-length index at the end and do
+//         not put any indicies into the header to fulfill this constraint.
+
+// The model.json, which contains all the metadata information,
+// should be written as the last file. One reason is that the size of tensor
+// data is usually stable. As long as the shape and type of the tensor do not
+// change, the size of the data won't change. On the other sied, the size of the
+// serialized model is likely to change, so we store it as the last record, and
+// we don't need to move previous records when updating the model data.
+
+// The zip format is sufficiently flexible to handle the above use-case.
+// it puts its central directory at the end of the archive and we write
+// model.json as the last file when writing after we have accumulated all
+// other information.
+
+namespace caffe2 {
+namespace serialize {
+
+static constexpr const char* kSerializationIdRecordName =
+    ".data/serialization_id";
+
+struct MzZipReaderIterWrapper;
+
+class TORCH_API ChunkRecordIterator {
+ public:
+  ~ChunkRecordIterator();
+
+  // Read at most `chunkSize` into `buf`. Return the number of actual bytes
+  // read.
+  size_t next(void* buf);
+  size_t recordSize() const {
+    return recordSize_;
+  }
+
+ private:
+  ChunkRecordIterator(
+      size_t recordSize,
+      size_t chunkSize,
+      std::unique_ptr<MzZipReaderIterWrapper> iter);
+
+  const size_t recordSize_;
+  const size_t chunkSize_;
+  size_t offset_;
+  std::unique_ptr<MzZipReaderIterWrapper> iter_;
+
+  friend class PyTorchStreamReader;
+};
+
+class TORCH_API PyTorchStreamReader final {
+ public:
+  explicit PyTorchStreamReader(const std::string& file_name);
+  explicit PyTorchStreamReader(std::istream* in);
+  explicit PyTorchStreamReader(std::shared_ptr<ReadAdapterInterface> in);
+
+  // return dataptr, size
+  std::tuple<at::DataPtr, size_t> getRecord(const std::string& name);
+  // multi-thread getRecord
+  std::tuple<at::DataPtr, size_t> getRecord(
+      const std::string& name,
+      std::vector<std::shared_ptr<ReadAdapterInterface>>& additionalReaders);
+  // inplace memory writing
+  size_t getRecord(const std::string& name, void* dst, size_t n);
+  // inplace memory writing, multi-threads.
+  // When additionalReaders is empty, the default behavior is call
+  // getRecord(name, dst, n) with default reader This approach can be used for
+  // reading large tensors.
+  size_t getRecord(
+      const std::string& name,
+      void* dst,
+      size_t n,
+      std::vector<std::shared_ptr<ReadAdapterInterface>>& additionalReaders);
+  size_t getRecord(
+      const std::string& name,
+      void* dst,
+      size_t n,
+      size_t chunk_size,
+      void* buf,
+      const std::function<void(void*, const void*, size_t)>& memcpy_func =
+          nullptr);
+
+  // Concurrent reading records with multiple readers.
+  // additionalReaders are additional clients to access the underlying record at
+  // different offsets and write to different trunks of buffers. If the overall
+  // size of the tensor is 10, and size of additionalReader is 2. The default
+  // thread will read [0,4), the additional reader will read [4,8). The default
+  // reader will read [8,10). The default reader will write to buffer[0,4), the
+  // additional reader will write to buffer[4,8), the additional reader will
+  // write to buffer[8,10). When additionalReaders is empty, the default
+  // behavior is call getRecord(name) with default reader This approach can be
+  // used for reading large tensors.
+  size_t getRecordMultiReaders(
+      const std::string& name,
+      std::vector<std::shared_ptr<ReadAdapterInterface>>& additionalReaders,
+      void* dst,
+      size_t n);
+
+  size_t getRecordSize(const std::string& name);
+
+  size_t getRecordOffset(const std::string& name);
+  bool hasRecord(const std::string& name);
+  std::vector<std::string> getAllRecords();
+
+  ChunkRecordIterator createChunkReaderIter(
+      const std::string& name,
+      const size_t recordSize,
+      const size_t chunkSize);
+
+  ~PyTorchStreamReader();
+  uint64_t version() const {
+    return version_;
+  }
+  const std::string& serializationId() {
+    return serialization_id_;
+  }
+
+  void setShouldLoadDebugSymbol(bool should_load_debug_symbol) {
+    load_debug_symbol_ = should_load_debug_symbol;
+  }
+  void setAdditionalReaderSizeThreshold(const size_t& size) {
+    additional_reader_size_threshold_ = size;
+  }
+
+ private:
+  void init();
+  size_t read(uint64_t pos, char* buf, size_t n);
+  void valid(const char* what, const char* info = "");
+  size_t getRecordID(const std::string& name);
+
+  friend size_t
+  istream_read_func(void* pOpaque, uint64_t file_ofs, void* pBuf, size_t n);
+  std::unique_ptr<mz_zip_archive> ar_;
+  std::string archive_name_;
+  std::string archive_name_plus_slash_;
+  std::shared_ptr<ReadAdapterInterface> in_;
+  int64_t version_;
+  std::mutex reader_lock_;
+  bool load_debug_symbol_ = true;
+  std::string serialization_id_;
+  size_t additional_reader_size_threshold_;
+};
+
+class TORCH_API PyTorchStreamWriter final {
+ public:
+  explicit PyTorchStreamWriter(
+      const std::string& archive_name,
+      bool compute_crc32 = true);
+  explicit PyTorchStreamWriter(
+      const std::function<size_t(const void*, size_t)> writer_func,
+      bool compute_crc32 = true);
+
+  void setMinVersion(const uint64_t version);
+
+  void writeRecord(
+      const std::string& name,
+      const void* data,
+      size_t size,
+      bool compress = false);
+  void writeEndOfFile();
+
+  const std::unordered_set<std::string>& getAllWrittenRecords();
+
+  bool finalized() const {
+    return finalized_;
+  }
+
+  const std::string& archiveName() {
+    return archive_name_;
+  }
+
+  const std::string& serializationId() {
+    return serialization_id_;
+  }
+
+  ~PyTorchStreamWriter();
+
+ private:
+  void setup(const std::string& file_name);
+  void valid(const char* what, const char* info = "");
+  void writeSerializationId();
+  size_t current_pos_ = 0;
+  std::unordered_set<std::string> files_written_;
+  std::unique_ptr<mz_zip_archive> ar_;
+  std::string archive_name_;
+  std::string archive_name_plus_slash_;
+  std::string padding_;
+  std::ofstream file_stream_;
+  std::function<size_t(const void*, size_t)> writer_func_;
+  uint64_t combined_uncomp_crc32_ = 0;
+  std::string serialization_id_;
+  bool compute_crc32_;
+
+  // This number will be updated when the model has operators
+  // that have valid upgraders.
+  uint64_t version_ = kMinProducedFileFormatVersion;
+  bool finalized_ = false;
+  bool err_seen_ = false;
+  friend size_t ostream_write_func(
+      void* pOpaque,
+      uint64_t file_ofs,
+      const void* pBuf,
+      size_t n);
+};
+
+namespace detail {
+// Writer-specific constants
+constexpr uint64_t kFieldAlignment = 64;
+
+// Returns a record to be appended to the local user extra data entry in order
+// to make data beginning aligned at kFieldAlignment bytes boundary.
+size_t getPadding(
+    size_t cursor,
+    size_t filename_size,
+    size_t size,
+    std::string& padding_buf);
+} // namespace detail
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/istream_adapter.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/istream_adapter.h
new file mode 100644
index 0000000000000000000000000000000000000000..680c288a15f2e89d5a3b8b51d8aaddf44f727d19
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/istream_adapter.h
@@ -0,0 +1,27 @@
+#pragma once
+
+#include <istream>
+
+#include "c10/macros/Macros.h"
+#include "caffe2/serialize/read_adapter_interface.h"
+
+namespace caffe2 {
+namespace serialize {
+
+// this is a reader implemented by std::istream
+class TORCH_API IStreamAdapter final : public ReadAdapterInterface {
+ public:
+  C10_DISABLE_COPY_AND_ASSIGN(IStreamAdapter);
+  explicit IStreamAdapter(std::istream* istream);
+  size_t size() const override;
+  size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
+      const override;
+  ~IStreamAdapter() override;
+
+ private:
+  std::istream* istream_;
+  void validate(const char* what) const;
+};
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a6b5b74a762e7c90b64a6f93717802f658dd164
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/read_adapter_interface.h
@@ -0,0 +1,23 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+
+#include "c10/macros/Macros.h"
+
+namespace caffe2 {
+namespace serialize {
+
+// this is the interface for the (file/stream/memory) reader in
+// PyTorchStreamReader. with this interface, we can extend the support
+// besides standard istream
+class TORCH_API ReadAdapterInterface {
+ public:
+  virtual size_t size() const = 0;
+  virtual size_t read(uint64_t pos, void* buf, size_t n, const char* what = "")
+      const = 0;
+  virtual ~ReadAdapterInterface();
+};
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/caffe2/serialize/versions.h b/lib/python3.11/site-packages/torch/include/caffe2/serialize/versions.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e2c27adc8fae8fdb07dcd9dafe72c503c243c2b
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/caffe2/serialize/versions.h
@@ -0,0 +1,133 @@
+#pragma once
+#include <cstdint>
+
+namespace caffe2 {
+namespace serialize {
+
+constexpr uint64_t kMinSupportedFileFormatVersion = 0x1L;
+
+constexpr uint64_t kMaxSupportedFileFormatVersion = 0xAL;
+
+// Versions (i.e. why was the version number bumped?)
+
+// Note [Dynamic Versions and torch.jit.save vs. torch.save]
+//
+// Our versioning scheme has a "produced file format version" which
+// describes how an archive is to be read. The version written in an archive
+// is at least this current produced file format version, but may be greater
+// if it includes certain symbols. We refer to these conditional versions
+// as "dynamic," since they are identified at runtime.
+//
+// Dynamic versioning is useful when an operator's semantics are updated.
+// When using torch.jit.save we want those semantics to be preserved. If
+// we bumped the produced file format version on every change, however,
+// then older versions of PyTorch couldn't read even simple archives, like
+// a single tensor, from newer versions of PyTorch. Instead, we
+// assign dynamic versions to these changes that override the
+// produced file format version as needed. That is, when the semantics
+// of torch.div changed it was assigned dynamic version 4, and when
+// torch.jit.saving modules that use torch.div those archives also have
+// (at least) version 4. This prevents earlier versions of PyTorch
+// from accidentally performing the wrong kind of division. Modules
+// that don't use torch.div or other operators with dynamic versions
+// can write the produced file format version, and these programs will
+// run as expected on earlier versions of PyTorch.
+//
+// While torch.jit.save attempts to preserve operator semantics,
+// torch.save does not. torch.save is analogous to pickling Python, so
+// a function that uses torch.div will have different behavior if torch.saved
+// and torch.loaded across PyTorch versions. From a technical perspective,
+// torch.save ignores dynamic versioning.
+
+// 1. Initial version
+// 2. Removed op_version_set version numbers
+// 3. Added type tags to pickle serialization of container types
+// 4. (Dynamic) Stopped integer division using torch.div
+//      (a versioned symbol preserves the historic behavior of versions 1--3)
+// 5. (Dynamic) Stops torch.full inferring a floating point dtype
+//      when given bool or integer fill values.
+// 6. Write version string to `./data/version` instead of `version`.
+
+// [12/15/2021]
+// kProducedFileFormatVersion is set to 7 from 3 due to a different
+// interpretation of what file format version is.
+// Whenever there is new upgrader introduced,
+// this number should be bumped.
+// The reasons that version is bumped in the past:
+//     1. aten::div is changed at version 4
+//     2. aten::full is changed at version 5
+//     3. torch.package uses version 6
+//     4. Introduce new upgrader design and set the version number to 7
+//        mark this change
+// --------------------------------------------------
+// We describe new operator version bump reasons here:
+// 1) [01/24/2022]
+//     We bump the version number to 8 to update aten::linspace
+//     and aten::linspace.out to error out when steps is not
+//     provided. (see: https://github.com/pytorch/pytorch/issues/55951)
+// 2) [01/30/2022]
+//     Bump the version number to 9 to update aten::logspace and
+//     and aten::logspace.out to error out when steps is not
+//     provided. (see: https://github.com/pytorch/pytorch/issues/55951)
+// 3) [02/11/2022]
+//     Bump the version number to 10 to update aten::gelu and
+//     and aten::gelu.out to support the new approximate kwarg.
+//     (see: https://github.com/pytorch/pytorch/pull/61439)
+constexpr uint64_t kProducedFileFormatVersion = 0xAL;
+
+// Absolute minimum version we will write packages. This
+// means that every package from now on will always be
+// greater than this number.
+constexpr uint64_t kMinProducedFileFormatVersion = 0x3L;
+
+// The version we write when the archive contains bytecode.
+// It must be higher or eq to kProducedFileFormatVersion.
+// Because torchscript changes is likely introduce bytecode change.
+// If kProducedFileFormatVersion is increased, kProducedBytecodeVersion
+// should be increased too. The relationship is:
+// kMaxSupportedFileFormatVersion >= (most likely ==) kProducedBytecodeVersion
+//   >= kProducedFileFormatVersion
+// If a format change is forward compatible (still readable by older
+// executables), we will not increment the version number, to minimize the
+// risk of breaking existing clients. TODO: A better way would be to allow
+// the caller that creates a model to specify a maximum version that its
+// clients can accept.
+// Versions:
+//  0x1L: Initial version
+//  0x2L: (Comment missing)
+//  0x3L: (Comment missing)
+//  0x4L: (update) Added schema to function tuple. Forward-compatible change.
+//  0x5L: (update) Update bytecode is sharing constant tensor files from
+//  torchscript, and only serialize extra tensors that are not in the
+//  torchscript constant table. Also update tensor storage schema adapting to
+//  the unify format, the root key of tensor storage is updated from {index} to
+//  {the_pointer_value_the_tensor.storage}, for example:
+//  `140245072983168.storage` Forward-compatibility change.
+//  0x6L: Implicit opereator versioning using number of specified argument.
+//  Refer to the summary of https://github.com/pytorch/pytorch/pull/56845 for
+//  details.
+//  0x7L: Enable support for operators with default arguments plus out
+//  arguments. Refer. See https://github.com/pytorch/pytorch/pull/63651 for
+//  details.
+//  0x8L: Emit promoted operators as instructions. See
+//  https://github.com/pytorch/pytorch/pull/71662 for details.
+//  0x9L: Change serialization format from pickle to format This version is to
+//  serve migration. v8 pickle and v9 flatbuffer are the same. Refer to the
+//  summary of https://github.com/pytorch/pytorch/pull/75201 for more details.
+constexpr uint64_t kProducedBytecodeVersion = 0x8L;
+
+// static_assert(
+//     kProducedBytecodeVersion >= kProducedFileFormatVersion,
+//     "kProducedBytecodeVersion must be higher or equal to
+//     kProducedFileFormatVersion.");
+
+// Introduce kMinSupportedBytecodeVersion and kMaxSupportedBytecodeVersion
+// for limited backward/forward compatibility support of bytecode. If
+// kMinSupportedBytecodeVersion <= model_version <= kMaxSupportedBytecodeVersion
+// (in loader), we should support this model_version. For example, we provide a
+// wrapper to handle an updated operator.
+constexpr uint64_t kMinSupportedBytecodeVersion = 0x4L;
+constexpr uint64_t kMaxSupportedBytecodeVersion = 0x9L;
+
+} // namespace serialize
+} // namespace caffe2
diff --git a/lib/python3.11/site-packages/torch/include/kineto/AbstractConfig.h b/lib/python3.11/site-packages/torch/include/kineto/AbstractConfig.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a7d66def89369b670498a74eae951c34fe6ef67
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/AbstractConfig.h
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <chrono>
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace libkineto {
+
+class AbstractConfig {
+ public:
+  AbstractConfig& operator=(const AbstractConfig&) = delete;
+  AbstractConfig(AbstractConfig&&) = delete;
+  AbstractConfig& operator=(AbstractConfig&&) = delete;
+
+  virtual ~AbstractConfig() {
+    for (const auto& p : featureConfigs_) {
+      delete p.second;
+    }
+  }
+
+  // Return a copy of the full derived class
+  virtual AbstractConfig* cloneDerived(AbstractConfig& parent) const = 0;
+
+  // Returns true if successfully parsed the config string
+  bool parse(const std::string& conf);
+
+  // Default setup for signal-triggered profiling
+  virtual void setSignalDefaults() {
+    for (auto& p : featureConfigs_) {
+      p.second->setSignalDefaults();
+    }
+  }
+
+  // Default setup for client-triggered profiling
+  virtual void setClientDefaults() {
+    for (auto& p : featureConfigs_) {
+      p.second->setClientDefaults();
+    }
+  }
+
+  // Time config was created / updated
+  std::chrono::time_point<std::chrono::system_clock> timestamp() const {
+    return timestamp_;
+  }
+
+  // Source config string that this was parsed from
+  const std::string& source() const {
+    return source_;
+  }
+
+  AbstractConfig& feature(std::string name) const {
+    const auto& pos = featureConfigs_.find(name);
+    return *pos->second;
+  }
+
+  // Transfers ownership of cfg arg
+  void addFeature(const std::string& name, AbstractConfig* cfg) {
+    featureConfigs_[name] = cfg;
+  }
+
+ protected:
+  AbstractConfig() {}
+  AbstractConfig(const AbstractConfig& other) = default;
+
+  // Return true if the option was recognized and successfully parsed.
+  // Throw std::invalid_argument if val is invalid.
+  virtual bool handleOption(const std::string& name, std::string& val);
+
+  // Perform post-validation checks, typically conditons involving
+  // multiple options.
+  // Throw std::invalid_argument if automatic correction can not be made.
+  //
+  // @param fallbackProfileStartTime Specify a fallback profile start timestamp
+  // in case it was never specified by the client
+  virtual void validate(
+      const std::chrono::time_point<std::chrono::system_clock>&
+          fallbackProfileStartTime) = 0;
+
+  // TODO: Separate out each profiler type into features?
+  virtual void printActivityProfilerConfig(std::ostream& s) const;
+  virtual void setActivityDependentConfig();
+
+  // Helpers for use in handleOption
+  // Split a string by delimiter and remove external white space
+  std::vector<std::string> splitAndTrim(const std::string& s, char delim) const;
+  // Lowercase for case-insensitive comparisons
+  std::string toLower(std::string& s) const;
+  // Does string end with suffix
+  bool endsWith(const std::string& s, const std::string& suffix) const;
+  // Conversions
+  int64_t toIntRange(const std::string& val, int64_t min, int64_t max) const;
+  int32_t toInt32(const std::string& val) const;
+  int64_t toInt64(const std::string& val) const;
+  bool toBool(std::string& val) const;
+
+  void cloneFeaturesInto(AbstractConfig& cfg) const {
+    for (const auto& feature : featureConfigs_) {
+      cfg.featureConfigs_[feature.first] = feature.second->cloneDerived(cfg);
+    }
+  }
+
+ private:
+  // Time config was created / updated
+  std::chrono::time_point<std::chrono::system_clock> timestamp_{};
+
+  // Original configuration string, used for comparison
+  std::string source_{""};
+
+  // Configuration objects for optional features
+  std::map<std::string, AbstractConfig*> featureConfigs_{};
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ActivityProfilerInterface.h b/lib/python3.11/site-packages/torch/include/kineto/ActivityProfilerInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..a9af8198b455967bdc900e8019617a45f107fa07
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ActivityProfilerInterface.h
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+#include <set>
+#include <thread>
+#include <vector>
+
+#include "ActivityTraceInterface.h"
+#include "ActivityType.h"
+#include "IActivityProfiler.h"
+
+namespace libkineto {
+
+class ActivityProfilerController;
+struct CpuTraceBuffer;
+class Config;
+
+class ActivityProfilerInterface {
+ public:
+  virtual ~ActivityProfilerInterface() {}
+
+  virtual void init() {}
+  virtual bool isInitialized() {
+    return false;
+  }
+  virtual bool isActive() {
+    return false;
+  }
+
+  // *** Asynchronous API ***
+  // Instead of starting and stopping the trace manually, provide a start time
+  // and duration and / or iteration stop criterion.
+  // Tracing terminates when either condition is met.
+  virtual void scheduleTrace(const std::string& configStr) {}
+
+  // *** Synchronous API ***
+  // These must be called in order:
+  // prepareTrace -> startTrace -> stopTrace.
+
+  // Many tracing structures are lazily initialized during trace collection,
+  // with potentially high overhead.
+  // Call prepareTrace to enable tracing, then run the region to trace
+  // at least once (and ideally run the same code that is to be traced) to
+  // allow tracing structures to be initialized.
+  virtual void prepareTrace(
+      const std::set<ActivityType>& activityTypes,
+      const std::string& configStr = "") {}
+
+  // Toggle GPU tracing as a trace is running to omit certain parts of a graph
+  virtual void toggleCollectionDynamic(const bool enable) {}
+
+  // Start recording, potentially reusing any buffers allocated since
+  // prepareTrace was called.
+  virtual void startTrace() {}
+
+  // Stop and process trace, producing an in-memory list of trace records.
+  // The processing will be done synchronously (using the calling thread.)
+  virtual std::unique_ptr<ActivityTraceInterface> stopTrace() {
+    return nullptr;
+  }
+
+  // Re-evaluate internal state to allow for triggering operations based
+  // on number of iteration. each implicitly increments the iteration count
+  virtual void step() {}
+
+  // *** TraceActivity API ***
+  // FIXME: Pass activityProfiler interface into clientInterface?
+  virtual void pushCorrelationId(uint64_t id) {}
+  virtual void popCorrelationId() {}
+  virtual void transferCpuTrace(std::unique_ptr<CpuTraceBuffer> traceBuffer) {}
+
+  // Correlation ids for user defined spans
+  virtual void pushUserCorrelationId(uint64_t) {}
+  virtual void popUserCorrelationId() {}
+
+  // Saves information for the current thread to be used in profiler output
+  // Client must record any new kernel thread where the activity has occured.
+  virtual void recordThreadInfo() {}
+
+  // Record trace metadata, currently supporting only string key and values,
+  // values with the same key are overwritten
+  virtual void addMetadata(
+      const std::string& key,
+      const std::string& value) = 0;
+
+  // Add a child activity profiler, this enables frameworks in the application
+  // to enable custom framework events.
+  virtual void addChildActivityProfiler(
+      std::unique_ptr<IActivityProfiler> profiler) {}
+
+  // Log Invariant Violation to factories enabled. This helps record
+  // instances when the profiler behaves unexpectedly.
+  virtual void logInvariantViolation(
+      const std::string&,
+      const std::string&,
+      const std::string&,
+      const std::string& = "") {}
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ActivityTraceInterface.h b/lib/python3.11/site-packages/torch/include/kineto/ActivityTraceInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..ceae7ca61a527ee7a5477011dcbb65b1bb551b41
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ActivityTraceInterface.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace libkineto {
+
+struct ITraceActivity;
+
+class ActivityTraceInterface {
+ public:
+  virtual ~ActivityTraceInterface() {}
+  virtual const std::vector<const ITraceActivity*>* activities() {
+    return nullptr;
+  }
+  virtual void save(const std::string& path) {}
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ActivityType.h b/lib/python3.11/site-packages/torch/include/kineto/ActivityType.h
new file mode 100644
index 0000000000000000000000000000000000000000..84887c0b58c2ceea1d588a217623a468b50a267c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ActivityType.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <array>
+#include <set>
+#include <string>
+
+namespace libkineto {
+
+// Note : All activity types are not enabled by default. Please add them
+// at correct position in the enum
+enum class ActivityType {
+  // Activity types enabled by default
+  CPU_OP = 0, // cpu side ops
+  USER_ANNOTATION,
+  GPU_USER_ANNOTATION,
+  GPU_MEMCPY,
+  GPU_MEMSET,
+  CONCURRENT_KERNEL, // on-device kernels
+  EXTERNAL_CORRELATION,
+  CUDA_RUNTIME, // host side cuda runtime events
+  CUDA_DRIVER, // host side cuda driver events
+  CPU_INSTANT_EVENT, // host side point-like events
+  PYTHON_FUNCTION,
+  OVERHEAD, // CUPTI induced overhead events sampled from its overhead API.
+  MTIA_RUNTIME, // host side MTIA runtime events
+  MTIA_CCP_EVENTS, // MTIA ondevice CCP events
+  CUDA_SYNC, // synchronization events between runtime and kernels
+
+  // Optional Activity types
+  GLOW_RUNTIME, // host side glow runtime events
+  CUDA_PROFILER_RANGE, // CUPTI Profiler range for performance metrics
+  HPU_OP, // HPU host side runtime event
+  XPU_RUNTIME, // host side xpu runtime events
+  COLLECTIVE_COMM, // collective communication
+  MTIA_WORKLOADD, // MTIA workloadd events
+
+  // PRIVATEUSE1 Activity types are used for custom backends.
+  // The corresponding device type is `DeviceType::PrivateUse1` in PyTorch.
+  PRIVATEUSE1_RUNTIME, // host side privateUse1 runtime events
+  PRIVATEUSE1_DRIVER, // host side privateUse1 driver events
+
+  ENUM_COUNT, // This is to add buffer and not used for any profiling logic. Add
+  // your new type before it.
+  OPTIONAL_ACTIVITY_TYPE_START = GLOW_RUNTIME,
+};
+
+const char* toString(ActivityType t);
+ActivityType toActivityType(const std::string& str);
+
+// Return an array of all activity types except COUNT
+constexpr int activityTypeCount = (int)ActivityType::ENUM_COUNT;
+constexpr int defaultActivityTypeCount =
+    (int)ActivityType::OPTIONAL_ACTIVITY_TYPE_START;
+const std::array<ActivityType, activityTypeCount> activityTypes();
+const std::array<ActivityType, defaultActivityTypeCount> defaultActivityTypes();
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ClientInterface.h b/lib/python3.11/site-packages/torch/include/kineto/ClientInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..950c3be69004297d85b33a5d3fda36d009b32d6e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ClientInterface.h
@@ -0,0 +1,22 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+namespace libkineto {
+
+class ClientInterface {
+ public:
+  virtual ~ClientInterface() {}
+  virtual void init() = 0;
+  virtual void prepare(bool, bool, bool, bool, bool) = 0;
+  virtual void start() = 0;
+  virtual void stop() = 0;
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/Config.h b/lib/python3.11/site-packages/torch/include/kineto/Config.h
new file mode 100644
index 0000000000000000000000000000000000000000..aabe5fafbb41793a517e55e142be68bf9b3770a3
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/Config.h
@@ -0,0 +1,510 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include "AbstractConfig.h"
+#include "ActivityType.h"
+
+#include <assert.h>
+#include <chrono>
+#include <functional>
+#include <set>
+#include <string>
+#include <vector>
+
+namespace libkineto {
+
+class Config : public AbstractConfig {
+ public:
+  Config();
+  Config& operator=(const Config&) = delete;
+  Config(Config&&) = delete;
+  Config& operator=(Config&&) = delete;
+
+  // Return a full copy including feature config object
+  std::unique_ptr<Config> clone() const {
+    auto cfg = std::unique_ptr<Config>(new Config(*this));
+    cloneFeaturesInto(*cfg);
+    return cfg;
+  }
+
+  bool handleOption(const std::string& name, std::string& val) override;
+
+  void setClientDefaults() override;
+
+  // Log events to this file
+  const std::string& eventLogFile() const {
+    return eventLogFile_;
+  }
+
+  bool activityProfilerEnabled() const {
+    return activityProfilerEnabled_ ||
+        activitiesOnDemandTimestamp_.time_since_epoch().count() > 0;
+  }
+
+  // Log activitiy trace to this file
+  const std::string& activitiesLogFile() const {
+    return activitiesLogFile_;
+  }
+
+  // Log activitiy trace to this url
+  const std::string& activitiesLogUrl() const {
+    return activitiesLogUrl_;
+  }
+
+  void setActivitiesLogUrl(const std::string& url) {
+    activitiesLogUrl_ = url;
+  }
+
+  bool activitiesLogToMemory() const {
+    return activitiesLogToMemory_;
+  }
+
+  bool eventProfilerEnabled() const {
+    return !eventNames_.empty() || !metricNames_.empty();
+  }
+
+  // Is profiling enabled for the given device?
+  bool eventProfilerEnabledForDevice(uint32_t dev) const {
+    return 0 != (eventProfilerDeviceMask_ & (1 << dev));
+  }
+
+  // Take a sample (read hardware counters) at this frequency.
+  // This controls how often counters are read - if all counters cannot
+  // be collected simultaneously then multiple samples are needed to
+  // collect all requested counters - see multiplex period.
+  std::chrono::milliseconds samplePeriod() const {
+    return samplePeriod_;
+  }
+
+  void setSamplePeriod(std::chrono::milliseconds period) {
+    samplePeriod_ = period;
+  }
+
+  // When all requested counters cannot be collected simultaneously,
+  // counters will be multiplexed at this frequency.
+  // Multiplexing can have a large performance impact if done frequently.
+  // To avoid a perf impact, keep this at 1s or above.
+  std::chrono::milliseconds multiplexPeriod() const {
+    return multiplexPeriod_;
+  }
+
+  void setMultiplexPeriod(std::chrono::milliseconds period) {
+    multiplexPeriod_ = period;
+  }
+
+  // Report counters at this frequency. Note that several samples can
+  // be reported each time, see samplesPerReport.
+  std::chrono::milliseconds reportPeriod() const {
+    return reportPeriod_;
+  }
+
+  void setReportPeriod(std::chrono::milliseconds msecs);
+
+  // Number of samples dispatched each report period.
+  // Must be in the range [1, report period / sample period].
+  // In other words, aggregation is supported but not interpolation.
+  int samplesPerReport() const {
+    return samplesPerReport_;
+  }
+
+  void setSamplesPerReport(int count) {
+    samplesPerReport_ = count;
+  }
+
+  // The names of events to collect
+  const std::set<std::string>& eventNames() const {
+    return eventNames_;
+  }
+
+  // Add additional events to be profiled
+  void addEvents(const std::set<std::string>& names) {
+    eventNames_.insert(names.begin(), names.end());
+  }
+
+  // The names of metrics to collect
+  const std::set<std::string>& metricNames() const {
+    return metricNames_;
+  }
+
+  // Add additional metrics to be profiled
+  void addMetrics(const std::set<std::string>& names) {
+    metricNames_.insert(names.begin(), names.end());
+  }
+
+  const std::vector<int>& percentiles() const {
+    return eventReportPercentiles_;
+  }
+
+  // Profile for this long, then revert to base config
+  std::chrono::seconds eventProfilerOnDemandDuration() const {
+    return eventProfilerOnDemandDuration_;
+  }
+
+  void setEventProfilerOnDemandDuration(std::chrono::seconds duration) {
+    eventProfilerOnDemandDuration_ = duration;
+  }
+
+  // Too many event profilers on a single system can overload the driver.
+  // At some point, latencies shoot through the roof and collection of samples
+  // becomes impossible. To avoid this situation we have a limit of profilers
+  // per GPU.
+  // NOTE: Communication with a daemon is needed for this feature.
+  // Library must be built with an active DaemonConfigLoader.
+  int maxEventProfilersPerGpu() const {
+    return eventProfilerMaxInstancesPerGpu_;
+  }
+
+  // On Cuda11 we've seen occasional hangs when reprogramming counters
+  // Monitor profiling threads and report when a thread is not responding
+  // for a given number of seconds.
+  // A period of 0 means disable.
+  std::chrono::seconds eventProfilerHeartbeatMonitorPeriod() const {
+    return eventProfilerHeartbeatMonitorPeriod_;
+  }
+
+  // The types of activities selected in the configuration file
+  const std::set<ActivityType>& selectedActivityTypes() const {
+    return selectedActivityTypes_;
+  }
+
+  void setSelectedActivityTypes(const std::set<ActivityType>& types) {
+    selectedActivityTypes_ = types;
+  }
+
+  bool isReportInputShapesEnabled() const {
+    return enableReportInputShapes_;
+  }
+
+  bool isProfileMemoryEnabled() const {
+    return enableProfileMemory_;
+  }
+
+  bool isWithStackEnabled() const {
+    return enableWithStack_;
+  }
+
+  bool isWithFlopsEnabled() const {
+    return enableWithFlops_;
+  }
+
+  bool isWithModulesEnabled() const {
+    return enableWithModules_;
+  }
+
+  // Trace for this long
+  std::chrono::milliseconds activitiesDuration() const {
+    return activitiesDuration_;
+  }
+
+  // Trace for this many iterations, determined by external API
+  int activitiesRunIterations() const {
+    return activitiesRunIterations_;
+  }
+
+  int activitiesMaxGpuBufferSize() const {
+    return activitiesMaxGpuBufferSize_;
+  }
+
+  std::chrono::seconds activitiesWarmupDuration() const {
+    return activitiesWarmupDuration_;
+  }
+
+  int activitiesWarmupIterations() const {
+    return activitiesWarmupIterations_;
+  }
+
+  // Show CUDA Synchronization Stream Wait Events
+  bool activitiesCudaSyncWaitEvents() const {
+    return activitiesCudaSyncWaitEvents_;
+  }
+
+  void setActivitiesCudaSyncWaitEvents(bool enable) {
+    activitiesCudaSyncWaitEvents_ = enable;
+  }
+
+  // Timestamp at which the profiling to start, requested by the user.
+  const std::chrono::time_point<std::chrono::system_clock> requestTimestamp()
+      const {
+    if (profileStartTime_.time_since_epoch().count()) {
+      return profileStartTime_;
+    }
+    // If no one requested timestamp, return 0.
+    if (requestTimestamp_.time_since_epoch().count() == 0) {
+      return requestTimestamp_;
+    }
+
+    // TODO(T94634890): Deprecate requestTimestamp
+    return requestTimestamp_ + maxRequestAge() + activitiesWarmupDuration();
+  }
+
+  bool hasProfileStartTime() const {
+    return requestTimestamp_.time_since_epoch().count() > 0 ||
+        profileStartTime_.time_since_epoch().count() > 0;
+  }
+
+  int profileStartIteration() const {
+    return profileStartIteration_;
+  }
+
+  bool hasProfileStartIteration() const {
+    return profileStartIteration_ >= 0 && activitiesRunIterations_ > 0;
+  }
+
+  void setProfileStartIteration(int iter) {
+    profileStartIteration_ = iter;
+  }
+
+  int profileStartIterationRoundUp() const {
+    return profileStartIterationRoundUp_;
+  }
+
+  // calculate the start iteration accounting for warmup
+  int startIterationIncludingWarmup() const {
+    if (!hasProfileStartIteration()) {
+      return -1;
+    }
+    return profileStartIteration_ - activitiesWarmupIterations_;
+  }
+
+  const std::chrono::seconds maxRequestAge() const;
+
+  // All VLOG* macros will log if the verbose log level is >=
+  // the verbosity specified for the verbose log message.
+  // Default value is -1, so messages with log level 0 will log by default.
+  int verboseLogLevel() const {
+    return verboseLogLevel_;
+  }
+
+  // Modules for which verbose logging is enabled.
+  // If empty, logging is enabled for all modules.
+  const std::vector<std::string>& verboseLogModules() const {
+    return verboseLogModules_;
+  }
+
+  bool sigUsr2Enabled() const {
+    return enableSigUsr2_;
+  }
+
+  bool ipcFabricEnabled() const {
+    return enableIpcFabric_;
+  }
+
+  std::chrono::seconds onDemandConfigUpdateIntervalSecs() const {
+    return onDemandConfigUpdateIntervalSecs_;
+  }
+
+  static std::chrono::milliseconds alignUp(
+      std::chrono::milliseconds duration,
+      std::chrono::milliseconds alignment) {
+    duration += alignment;
+    return duration - (duration % alignment);
+  }
+
+  std::chrono::time_point<std::chrono::system_clock>
+  eventProfilerOnDemandStartTime() const {
+    return eventProfilerOnDemandTimestamp_;
+  }
+
+  std::chrono::time_point<std::chrono::system_clock>
+  eventProfilerOnDemandEndTime() const {
+    return eventProfilerOnDemandTimestamp_ + eventProfilerOnDemandDuration_;
+  }
+
+  std::chrono::time_point<std::chrono::system_clock>
+  activityProfilerRequestReceivedTime() const {
+    return activitiesOnDemandTimestamp_;
+  }
+
+  static constexpr std::chrono::milliseconds kControllerIntervalMsecs{1000};
+
+  // Users may request and set trace id and group trace id.
+  const std::string& requestTraceID() const {
+    return requestTraceID_;
+  }
+
+  void setRequestTraceID(const std::string& tid) {
+    requestTraceID_ = tid;
+  }
+
+  const std::string& requestGroupTraceID() const {
+    return requestGroupTraceID_;
+  }
+
+  void setRequestGroupTraceID(const std::string& gtid) {
+    requestGroupTraceID_ = gtid;
+  }
+
+  size_t cuptiDeviceBufferSize() const {
+    return cuptiDeviceBufferSize_;
+  }
+
+  size_t cuptiDeviceBufferPoolLimit() const {
+    return cuptiDeviceBufferPoolLimit_;
+  }
+
+  void updateActivityProfilerRequestReceivedTime();
+
+  void printActivityProfilerConfig(std::ostream& s) const override;
+  void setActivityDependentConfig() override;
+
+  void validate(const std::chrono::time_point<std::chrono::system_clock>&
+                    fallbackProfileStartTime) override;
+
+  static void addConfigFactory(
+      std::string name,
+      std::function<AbstractConfig*(Config&)> factory);
+
+  void print(std::ostream& s) const;
+
+  // Config relies on some state with global static lifetime. If other
+  // threads are using the config, it's possible that the global state
+  // is destroyed before the threads stop. By hanging onto this handle,
+  // correct destruction order can be ensured.
+  static std::shared_ptr<void> getStaticObjectsLifetimeHandle();
+
+  bool getTSCTimestampFlag() const {
+    return useTSCTimestamp_;
+  }
+
+  void setTSCTimestampFlag(bool flag) {
+    useTSCTimestamp_ = flag;
+  }
+
+ private:
+  explicit Config(const Config& other) = default;
+
+  AbstractConfig* cloneDerived(AbstractConfig& parent) const override {
+    // Clone from AbstractConfig not supported
+    assert(false);
+    return nullptr;
+  }
+
+  uint8_t createDeviceMask(const std::string& val);
+
+  // Adds valid activity types from the user defined string list in the
+  // configuration file
+  void setActivityTypes(const std::vector<std::string>& selected_activities);
+
+  // Sets the default activity types to be traced
+  void selectDefaultActivityTypes() {
+    // If the user has not specified an activity list, add all types
+    for (ActivityType t : defaultActivityTypes()) {
+      selectedActivityTypes_.insert(t);
+    }
+  }
+
+  int verboseLogLevel_;
+  std::vector<std::string> verboseLogModules_;
+
+  // Event profiler
+  // These settings are also supported in on-demand mode
+  std::chrono::milliseconds samplePeriod_;
+  std::chrono::milliseconds reportPeriod_;
+  int samplesPerReport_;
+  std::set<std::string> eventNames_;
+  std::set<std::string> metricNames_;
+
+  // On-demand duration
+  std::chrono::seconds eventProfilerOnDemandDuration_;
+  // Last on-demand request
+  std::chrono::time_point<std::chrono::system_clock>
+      eventProfilerOnDemandTimestamp_;
+
+  int eventProfilerMaxInstancesPerGpu_;
+
+  // Monitor whether event profiler threads are stuck
+  // at this frequency
+  std::chrono::seconds eventProfilerHeartbeatMonitorPeriod_;
+
+  // These settings can not be changed on-demand
+  std::string eventLogFile_;
+  std::vector<int> eventReportPercentiles_ = {5, 25, 50, 75, 95};
+  uint8_t eventProfilerDeviceMask_ = ~0;
+  std::chrono::milliseconds multiplexPeriod_;
+
+  // Activity profiler
+  bool activityProfilerEnabled_;
+  std::set<ActivityType> selectedActivityTypes_;
+
+  // The activity profiler settings are all on-demand
+  std::string activitiesLogFile_;
+
+  std::string activitiesLogUrl_;
+
+  // Log activities to memory buffer
+  bool activitiesLogToMemory_{false};
+
+  int activitiesMaxGpuBufferSize_;
+  std::chrono::seconds activitiesWarmupDuration_;
+  int activitiesWarmupIterations_;
+  bool activitiesCudaSyncWaitEvents_;
+
+  // Enable Profiler Config Options
+  // Temporarily disable shape collection until we re-roll out the feature for
+  // on-demand cases
+  bool enableReportInputShapes_{false};
+  bool enableProfileMemory_{false};
+  bool enableWithStack_{false};
+  bool enableWithFlops_{false};
+  bool enableWithModules_{false};
+
+  // Profile for specified iterations and duration
+  std::chrono::milliseconds activitiesDuration_;
+  int activitiesRunIterations_;
+
+  // Below are not used
+  // Use this net name for iteration count
+  std::string activitiesExternalAPIIterationsTarget_;
+  // Only profile nets that includes this in the name
+  std::vector<std::string> activitiesExternalAPIFilter_;
+  // Only profile nets with at least this many operators
+  int activitiesExternalAPINetSizeThreshold_;
+  // Only profile nets with at least this many GPU operators
+  int activitiesExternalAPIGpuOpCountThreshold_;
+  // Last activity profiler request
+  std::chrono::time_point<std::chrono::system_clock>
+      activitiesOnDemandTimestamp_;
+
+  // ActivityProfilers are triggered by either:
+  // Synchronized start timestamps
+  std::chrono::time_point<std::chrono::system_clock> profileStartTime_;
+  // Or start iterations.
+  int profileStartIteration_;
+  int profileStartIterationRoundUp_;
+
+  // DEPRECATED
+  std::chrono::time_point<std::chrono::system_clock> requestTimestamp_;
+
+  // Enable profiling via SIGUSR2
+  bool enableSigUsr2_;
+
+  // Enable IPC Fabric instead of thrift communication
+  bool enableIpcFabric_;
+  std::chrono::seconds onDemandConfigUpdateIntervalSecs_;
+
+  // Logger Metadata
+  std::string requestTraceID_;
+  std::string requestGroupTraceID_;
+
+  // CUPTI Device Buffer
+  size_t cuptiDeviceBufferSize_;
+  size_t cuptiDeviceBufferPoolLimit_;
+
+  // CUPTI Timestamp Format
+  bool useTSCTimestamp_{true};
+};
+
+constexpr char kUseDaemonEnvVar[] = "KINETO_USE_DAEMON";
+
+#if __linux__
+bool isDaemonEnvVarSet();
+#endif
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/GenericTraceActivity.h b/lib/python3.11/site-packages/torch/include/kineto/GenericTraceActivity.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e7bc6ba4a6169015eb4d818707b202a0a81ca49
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/GenericTraceActivity.h
@@ -0,0 +1,154 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <fmt/format.h>
+#include <sstream>
+#include <string>
+#include <thread>
+#include <unordered_map>
+#include <vector>
+
+#include "ITraceActivity.h"
+#include "ThreadUtil.h"
+#include "TraceSpan.h"
+
+namespace libkineto {
+
+// Link type, used in GenericTraceActivity.flow.type
+constexpr unsigned int kLinkFwdBwd = 1;
+constexpr unsigned int kLinkAsyncCpuGpu = 2;
+
+// @lint-ignore-every CLANGTIDY
+// cppcoreguidelines-non-private-member-variables-in-classes
+// @lint-ignore-every CLANGTIDY cppcoreguidelines-pro-type-member-init
+class GenericTraceActivity : public ITraceActivity {
+ public:
+  GenericTraceActivity()
+      : activityType(ActivityType::ENUM_COUNT), traceSpan_(nullptr) {}
+
+  GenericTraceActivity(
+      const TraceSpan& trace,
+      ActivityType type,
+      const std::string& name)
+      : activityType(type), activityName(name), traceSpan_(&trace) {}
+
+  int64_t deviceId() const override {
+    return device;
+  }
+
+  int64_t resourceId() const override {
+    return resource;
+  }
+
+  int32_t getThreadId() const override {
+    return threadId;
+  }
+
+  int64_t timestamp() const override {
+    return startTime;
+  }
+
+  int64_t duration() const override {
+    return endTime - startTime;
+  }
+
+  int64_t correlationId() const override {
+    return id;
+  }
+
+  ActivityType type() const override {
+    return activityType;
+  }
+
+  const ITraceActivity* linkedActivity() const override {
+    return linked;
+  }
+
+  int flowType() const override {
+    return flow.type;
+  }
+
+  int flowId() const override {
+    return flow.id;
+  }
+
+  bool flowStart() const override {
+    return flow.start;
+  }
+
+  const std::string name() const override {
+    return activityName;
+  }
+
+  const TraceSpan* traceSpan() const override {
+    return traceSpan_;
+  }
+
+  void log(ActivityLogger& logger) const override;
+
+  // Encode client side metadata as a key/value
+  template <typename ValType>
+  void addMetadata(const std::string& key, const ValType& value) {
+    metadataMap_.emplace(key, std::make_pair(fmt::format("{}", value), false));
+  }
+
+  void addMetadataQuoted(const std::string& key, const std::string& value) {
+    metadataMap_.emplace(key, std::make_pair(value, true));
+  }
+
+  const std::string getMetadataValue(const std::string& key) const override {
+    if (auto it = metadataMap_.find(key); it != metadataMap_.end()) {
+      return it->second.first;
+    }
+    return "";
+  }
+
+  const std::string metadataJson() const override {
+    std::stringstream json;
+    bool first = true;
+    for (const auto& [key, val] : metadataMap_) {
+      if (!first) {
+        json << ", ";
+      }
+      val.second ? json << fmt::format("\"{}\": \"{}\"", key, val.first)
+                 : json << fmt::format("\"{}\": {}", key, val.first);
+      first = false;
+    }
+    return json.str();
+  }
+
+  virtual ~GenericTraceActivity() override {}
+
+  int64_t startTime{0};
+  int64_t endTime{0};
+  int32_t id{0};
+  int32_t device{0};
+  int32_t resource{0};
+  int32_t threadId{0};
+  ActivityType activityType;
+  std::string activityName;
+  struct Flow {
+    Flow() : id(0), type(0), start(0) {}
+    // Ids must be unique within each type
+    uint32_t id : 27;
+    // Type will be used to connect flows between profilers, as
+    // well as look up flow information (name etc)
+    uint32_t type : 4;
+    uint32_t start : 1;
+  } flow;
+  const ITraceActivity* linked{nullptr};
+
+ private:
+  const TraceSpan* traceSpan_;
+  // Metadata map: { key: (value, quoted)}
+  std::unordered_map<std::string, std::pair<std::string, bool>> metadataMap_;
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/IActivityProfiler.h b/lib/python3.11/site-packages/torch/include/kineto/IActivityProfiler.h
new file mode 100644
index 0000000000000000000000000000000000000000..4c88594e28c1ae1472fa04e6f6f80e5357beccbd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/IActivityProfiler.h
@@ -0,0 +1,167 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+#include <set>
+#include <vector>
+
+#include "Config.h"
+#include "GenericTraceActivity.h"
+
+/* This file includes an abstract base class for an activity profiler
+ * that can be implemented by multiple tracing agents in the application.
+ * The high level Kineto profiler can co-ordinate start and end of tracing
+ * and combine together events from multiple such activity profilers.
+ */
+
+namespace libkineto {
+
+struct CpuTraceBuffer;
+
+#ifdef _MSC_VER
+// workaround for the predefined ERROR macro on Windows
+#undef ERROR
+#endif // _MSC_VER
+
+enum class TraceStatus {
+  READY, // Accepting trace requests
+  WARMUP, // Performing trace warmup
+  RECORDING, // Actively collecting activities
+  PROCESSING, // Recording is complete, preparing results
+  ERROR, // One or more errors (and possibly also warnings) occurred.
+  WARNING, // One or more warnings occurred.
+};
+
+/* DeviceInfo:
+ *   Can be used to specify process name, sort order, PID and device label.
+ *   The sort order is determined by the sortIndex field to handle ordering of
+ *   processes and gpu rows in the trace viewer.
+ */
+struct DeviceInfo {
+  DeviceInfo(
+      int64_t id,
+      int64_t sortIndex,
+      const std::string& name,
+      const std::string& label)
+      : id(id), sortIndex(sortIndex), name(name), label(label) {}
+  int64_t id; // process id
+  int64_t sortIndex; // position in trace view
+  const std::string name; // process name
+  const std::string label; // device label
+};
+
+/* ResourceInfo:
+ *   Can be used to specify resource inside device
+ */
+struct ResourceInfo {
+  ResourceInfo(
+      int64_t deviceId,
+      int64_t id,
+      int64_t sortIndex,
+      const std::string& name)
+      : id(id), sortIndex(sortIndex), deviceId(deviceId), name(name) {}
+  int64_t id; // resource id
+  int64_t sortIndex; // position in trace view
+  int64_t deviceId; // id of device which owns this resource (specified in
+                    // DeviceInfo.id)
+  const std::string name; // resource name
+};
+
+using getLinkedActivityCallback = std::function<const ITraceActivity*(int32_t)>;
+
+/* IActivityProfilerSession:
+ *   an opaque object that can be used by a high level profiler to
+ *   start/stop and return trace events.
+ */
+class IActivityProfilerSession {
+ public:
+  virtual ~IActivityProfilerSession() {}
+
+  // start the trace collection synchronously
+  virtual void start() = 0;
+
+  // stop the trace collection synchronously
+  virtual void stop() = 0;
+
+  TraceStatus status() {
+    return status_;
+  }
+
+  // returns errors with this trace
+  virtual std::vector<std::string> errors() = 0;
+
+  // processes trace activities using logger
+  virtual void processTrace(ActivityLogger& logger) = 0;
+
+  virtual void processTrace(
+      ActivityLogger& logger,
+      getLinkedActivityCallback /*getLinkedActivity*/,
+      int64_t /*startTime*/,
+      int64_t /*endTime*/) {
+    processTrace(logger);
+  }
+
+  // returns device info used in this trace, could be nullptr
+  virtual std::unique_ptr<DeviceInfo> getDeviceInfo() = 0;
+
+  // returns resource info used in this trace, could be empty
+  virtual std::vector<ResourceInfo> getResourceInfos() = 0;
+
+  // release ownership of the trace events and metadata
+  virtual std::unique_ptr<CpuTraceBuffer> getTraceBuffer() = 0;
+
+  // XXX define trace formats
+  // virtual save(string name, TraceFormat format)
+
+  virtual void pushCorrelationId(uint64_t /*id*/) {}
+  virtual void popCorrelationId() {}
+
+  virtual void pushUserCorrelationId(uint64_t /*id*/) {}
+  virtual void popUserCorrelationId() {}
+
+  virtual std::string getDeviceProperties() {
+    return "";
+  }
+
+ protected:
+  TraceStatus status_ = TraceStatus::READY;
+};
+
+/* Activity Profiler Plugins:
+ *   These allow other frameworks to integrate into Kineto's primariy
+ *   activity profiler. While the primary activity profiler handles
+ *   timing the trace collections and correlating events the plugins
+ *   can become source of new trace activity types.
+ */
+class IActivityProfiler {
+ public:
+  virtual ~IActivityProfiler() {}
+
+  // name of profiler
+  virtual const std::string& name() const = 0;
+
+  // returns activity types this profiler supports
+  virtual const std::set<ActivityType>& availableActivities() const = 0;
+
+  // Calls prepare() on registered tracer providers passing in the relevant
+  // activity types. Returns a profiler session handle
+  virtual std::unique_ptr<IActivityProfilerSession> configure(
+      const std::set<ActivityType>& activity_types,
+      const Config& config) = 0;
+
+  // asynchronous version of the above with future timestamp and duration.
+  virtual std::unique_ptr<IActivityProfilerSession> configure(
+      int64_t ts_ms,
+      int64_t duration_ms,
+      const std::set<ActivityType>& activity_types,
+      const Config& config) = 0;
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ILoggerObserver.h b/lib/python3.11/site-packages/torch/include/kineto/ILoggerObserver.h
new file mode 100644
index 0000000000000000000000000000000000000000..b0bb46183ba4d6a269edac829e6d5ebfca4d38c4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ILoggerObserver.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <string>
+
+// Stages in libkineto used when pushing logs to UST Logger.
+constexpr char kWarmUpStage[] = "Warm Up";
+constexpr char kCollectionStage[] = "Collection";
+constexpr char kPostProcessingStage[] = "Post Processing";
+
+// Special string in UST for determining if traces are empty
+constexpr char kEmptyTrace[] =
+    "No Valid Trace Events (CPU/GPU) found. Outputting empty trace.";
+
+#if !USE_GOOGLE_LOG
+
+#include <map>
+#include <vector>
+
+#include <stdint.h>
+
+namespace libkineto {
+
+enum LoggerOutputType {
+  VERBOSE = 0,
+  INFO = 1,
+  WARNING = 2,
+  STAGE = 3,
+  ERROR = 4,
+  ENUM_COUNT = 5
+};
+
+const char* toString(LoggerOutputType t);
+LoggerOutputType toLoggerOutputType(const std::string& str);
+
+constexpr int LoggerTypeCount = (int)LoggerOutputType::ENUM_COUNT;
+
+class ILoggerObserver {
+ public:
+  virtual ~ILoggerObserver() = default;
+  virtual void write(const std::string& message, LoggerOutputType ot) = 0;
+  virtual const std::map<LoggerOutputType, std::vector<std::string>>
+  extractCollectorMetadata() = 0;
+  virtual void reset() = 0;
+  virtual void addDevice(const int64_t device) = 0;
+  virtual void setTraceDurationMS(const int64_t duration) = 0;
+  virtual void addEventCount(const int64_t count) = 0;
+  virtual void setTraceID(const std::string&) {}
+  virtual void setGroupTraceID(const std::string&) {}
+  virtual void addDestination(const std::string& dest) = 0;
+  virtual void setTriggerOnDemand() {}
+  virtual void addMetadata(
+      const std::string& key,
+      const std::string& value) = 0;
+};
+
+} // namespace libkineto
+
+#endif // !USE_GOOGLE_LOG
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ITraceActivity.h b/lib/python3.11/site-packages/torch/include/kineto/ITraceActivity.h
new file mode 100644
index 0000000000000000000000000000000000000000..34a3f3248b1887b840354ddbcc596b1baf950f4a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ITraceActivity.h
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <string>
+
+#include "ActivityType.h"
+
+namespace libkineto {
+
+class ActivityLogger;
+struct TraceSpan;
+
+// Generic activity interface is borrowed from tensorboard protobuf format.
+struct ITraceActivity {
+  virtual ~ITraceActivity() {}
+  // Device is a physical or logical entity, e.g. CPU, GPU or process
+  virtual int64_t deviceId() const = 0;
+  // A resource is something on the device, h/w thread,
+  // functional units etc.
+  virtual int64_t resourceId() const = 0;
+  // s/w thread
+  virtual int32_t getThreadId() const = 0;
+  // Start timestamp in nanoseconds
+  virtual int64_t timestamp() const = 0;
+  // Duration in nanoseconds
+  virtual int64_t duration() const = 0;
+  // Used to link up async activities
+  virtual int64_t correlationId() const = 0;
+  // Part of a flow, identified by flow id and type
+  virtual int flowType() const = 0;
+  virtual int flowId() const = 0;
+  virtual bool flowStart() const = 0;
+  virtual ActivityType type() const = 0;
+  virtual const std::string name() const = 0;
+  // Optional linked activity
+  virtual const ITraceActivity* linkedActivity() const = 0;
+  // Optional containing trace object
+  virtual const TraceSpan* traceSpan() const = 0;
+  // Log activity
+  virtual void log(ActivityLogger& logger) const = 0;
+  // Return json formatted metadata
+  // FIXME: Return iterator to dynamic type map here instead
+  virtual const std::string metadataJson() const = 0;
+  // Return the metadata value in string format with key
+  // @lint-ignore CLANGTIDY: clang-diagnostic-unused-parameter
+  virtual const std::string getMetadataValue(const std::string& key) const {
+    return "";
+  }
+
+  static int64_t nsToUs(int64_t ns) {
+    // It's important that this conversion is the same everywhere.
+    // No rounding!
+    return ns / 1000;
+  }
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/LoggingAPI.h b/lib/python3.11/site-packages/torch/include/kineto/LoggingAPI.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc3ac2b27f31ec6b87a35e4bdfdf2b35b6da0502
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/LoggingAPI.h
@@ -0,0 +1,14 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+namespace libkineto {
+int getLogSeverityLevel();
+void setLogSeverityLevel(int level);
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/ThreadUtil.h b/lib/python3.11/site-packages/torch/include/kineto/ThreadUtil.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca52c6343c1cbfc4c07fd7c34cd76b6a87c1e7fa
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/ThreadUtil.h
@@ -0,0 +1,34 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace libkineto {
+
+int32_t systemThreadId(bool cache = true);
+int32_t threadId();
+bool setThreadName(const std::string& name);
+std::string getThreadName();
+
+int32_t processId(bool cache = true);
+std::string processName(int32_t pid);
+
+// Return a list of pids and process names for the current process
+// and its parents.
+std::vector<std::pair<int32_t, std::string>> pidCommandPairsOfAncestors();
+
+// Resets all cached Thread local state, this must be done on
+// forks to prevent stale values from being retained.
+void resetTLS();
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/TraceSpan.h b/lib/python3.11/site-packages/torch/include/kineto/TraceSpan.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc62a2aec6546471231b63ced752e15e4dd5aa55
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/TraceSpan.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <atomic>
+#include <string>
+#include <thread>
+
+namespace libkineto {
+
+struct TraceSpan {
+  TraceSpan() = delete;
+  TraceSpan(int64_t startTime, int64_t endTime, std::string name)
+      : startTime(startTime), endTime(endTime), name(std::move(name)) {}
+  TraceSpan(int opCount, int it, std::string name, std::string prefix)
+      : opCount(opCount),
+        iteration(it),
+        name(std::move(name)),
+        prefix(std::move(prefix)) {}
+
+  // FIXME: change to duration?
+  int64_t startTime{0};
+  int64_t endTime{0};
+  int opCount{0};
+  int iteration{-1};
+  // Name is used to identify timeline
+  std::string name;
+  // Prefix used to distinguish trace spans on the same timeline
+  std::string prefix;
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/libkineto.h b/lib/python3.11/site-packages/torch/include/kineto/libkineto.h
new file mode 100644
index 0000000000000000000000000000000000000000..a122a77a524157c1c2129751b14aee48cbdb7cab
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/libkineto.h
@@ -0,0 +1,162 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+// Mediator for initialization and profiler control
+
+#pragma once
+
+#include <atomic>
+#include <chrono>
+#include <deque>
+#include <functional>
+#include <memory>
+#include <mutex>
+#include <set>
+#include <string>
+#include <thread>
+#include <vector>
+
+#include "ActivityProfilerInterface.h"
+#include "ActivityTraceInterface.h"
+#include "ActivityType.h"
+#include "ClientInterface.h"
+#include "GenericTraceActivity.h"
+#include "IActivityProfiler.h"
+#include "ILoggerObserver.h"
+#include "LoggingAPI.h"
+#include "TraceSpan.h"
+
+#include "ThreadUtil.h"
+
+extern "C" {
+void suppressLibkinetoLogMessages();
+int InitializeInjection(void);
+void libkineto_init(bool cpuOnly, bool logOnError);
+bool hasTestEnvVar();
+}
+
+namespace libkineto {
+
+class Config;
+class ConfigLoader;
+
+struct CpuTraceBuffer {
+  template <class... Args>
+  void emplace_activity(Args&&... args) {
+    activities.emplace_back(
+        std::make_unique<GenericTraceActivity>(std::forward<Args>(args)...));
+  }
+
+  static GenericTraceActivity& toRef(
+      std::unique_ptr<GenericTraceActivity>& ref) {
+    return *ref;
+  }
+
+  static const GenericTraceActivity& toRef(
+      const std::unique_ptr<GenericTraceActivity>& ref) {
+    return *ref;
+  }
+
+  TraceSpan span{0, 0, "none"};
+  int gpuOpCount;
+  std::deque<std::unique_ptr<GenericTraceActivity>> activities;
+};
+
+using ChildActivityProfilerFactory =
+    std::function<std::unique_ptr<IActivityProfiler>()>;
+
+class LibkinetoApi {
+ public:
+  explicit LibkinetoApi(ConfigLoader& configLoader)
+      : configLoader_(configLoader) {}
+
+  // Called by client that supports tracing API.
+  // libkineto can still function without this.
+  void registerClient(ClientInterface* client);
+
+  // Called by libkineto on init
+  void registerProfiler(std::unique_ptr<ActivityProfilerInterface> profiler) {
+    activityProfiler_ = std::move(profiler);
+    initClientIfRegistered();
+  }
+
+  ActivityProfilerInterface& activityProfiler() {
+    return *activityProfiler_;
+  }
+
+  ClientInterface* client() {
+    return client_;
+  }
+
+  void initProfilerIfRegistered() {
+    static std::once_flag once;
+    if (activityProfiler_) {
+      std::call_once(once, [this] {
+        if (!activityProfiler_->isInitialized()) {
+          activityProfiler_->init();
+          initChildActivityProfilers();
+        }
+      });
+    }
+  }
+
+  bool isProfilerInitialized() const {
+    return activityProfiler_ && activityProfiler_->isInitialized();
+  }
+
+  bool isProfilerRegistered() const {
+    return activityProfiler_ != nullptr;
+  }
+
+  void suppressLogMessages() {
+    suppressLibkinetoLogMessages();
+  }
+
+  void resetKinetoTLS() {
+    resetTLS();
+  }
+
+  // Provides access to profier configuration manaegement
+  ConfigLoader& configLoader() {
+    return configLoader_;
+  }
+
+  void registerProfilerFactory(ChildActivityProfilerFactory factory) {
+    if (isProfilerInitialized()) {
+      activityProfiler_->addChildActivityProfiler(factory());
+    } else {
+      childProfilerFactories_.push_back(factory);
+    }
+  }
+
+ private:
+  void initChildActivityProfilers() {
+    if (!isProfilerInitialized()) {
+      return;
+    }
+    for (const auto& factory : childProfilerFactories_) {
+      activityProfiler_->addChildActivityProfiler(factory());
+    }
+    childProfilerFactories_.clear();
+  }
+
+  // Client is initialized once both it and libkineto has registered
+  void initClientIfRegistered();
+
+  ConfigLoader& configLoader_;
+  std::unique_ptr<ActivityProfilerInterface> activityProfiler_{};
+  ClientInterface* client_{};
+  int32_t clientRegisterThread_{0};
+
+  std::vector<ChildActivityProfilerFactory> childProfilerFactories_;
+};
+
+// Singleton
+LibkinetoApi& api();
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/output_base.h b/lib/python3.11/site-packages/torch/include/kineto/output_base.h
new file mode 100644
index 0000000000000000000000000000000000000000..303d3e9a15bd7643aa70acb23077f40716166998
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/output_base.h
@@ -0,0 +1,76 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <fstream>
+#include <map>
+#include <ostream>
+#include <thread>
+#include <unordered_map>
+
+// TODO(T90238193)
+// @lint-ignore-every CLANGTIDY facebook-hte-RelativeInclude
+#include "GenericTraceActivity.h"
+#include "IActivityProfiler.h"
+#include "ThreadUtil.h"
+#include "TraceSpan.h"
+
+namespace KINETO_NAMESPACE {
+struct ActivityBuffers;
+}
+
+namespace libkineto {
+
+using namespace KINETO_NAMESPACE;
+
+// Used by sortIndex to put GPU tracks at the bottom
+// of the trace timelines. The largest valid CPU PID is 4,194,304,
+// so 5000000 is enough to guarantee that GPU tracks are sorted after CPU.
+constexpr int64_t kExceedMaxPid = 5000000;
+
+class ActivityLogger {
+ public:
+  virtual ~ActivityLogger() = default;
+
+  struct OverheadInfo {
+    explicit OverheadInfo(const std::string& name) : name(name) {}
+    const std::string name;
+  };
+
+  virtual void handleDeviceInfo(const DeviceInfo& info, uint64_t time) = 0;
+
+  virtual void handleResourceInfo(const ResourceInfo& info, int64_t time) = 0;
+
+  virtual void handleOverheadInfo(const OverheadInfo& info, int64_t time) = 0;
+
+  virtual void handleTraceSpan(const TraceSpan& span) = 0;
+
+  virtual void handleActivity(const libkineto::ITraceActivity& activity) = 0;
+  virtual void handleGenericActivity(
+      const libkineto::GenericTraceActivity& activity) = 0;
+
+  virtual void handleTraceStart(
+      const std::unordered_map<std::string, std::string>& metadata,
+      const std::string& device_properties) = 0;
+
+  void handleTraceStart() {
+    handleTraceStart(std::unordered_map<std::string, std::string>(), "");
+  }
+
+  virtual void finalizeTrace(
+      const Config& config,
+      std::unique_ptr<ActivityBuffers> buffers,
+      int64_t endTime,
+      std::unordered_map<std::string, std::vector<std::string>>& metadata) = 0;
+
+ protected:
+  ActivityLogger() = default;
+};
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/kineto/time_since_epoch.h b/lib/python3.11/site-packages/torch/include/kineto/time_since_epoch.h
new file mode 100644
index 0000000000000000000000000000000000000000..17faccec68d446a7cbff76acb243db90bb0c1f40
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/kineto/time_since_epoch.h
@@ -0,0 +1,21 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <chrono>
+
+namespace libkineto {
+template <class ClockT>
+inline int64_t timeSinceEpoch(const std::chrono::time_point<ClockT>& t) {
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(
+             t.time_since_epoch())
+      .count();
+}
+
+} // namespace libkineto
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/attr.h b/lib/python3.11/site-packages/torch/include/pybind11/attr.h
new file mode 100644
index 0000000000000000000000000000000000000000..1044db94d906ac5fcf6faab6ac7668187314598f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/attr.h
@@ -0,0 +1,690 @@
+/*
+    pybind11/attr.h: Infrastructure for processing custom
+    type and function attributes
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "cast.h"
+
+#include <functional>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+/// \addtogroup annotations
+/// @{
+
+/// Annotation for methods
+struct is_method {
+    handle class_;
+    explicit is_method(const handle &c) : class_(c) {}
+};
+
+/// Annotation for setters
+struct is_setter {};
+
+/// Annotation for operators
+struct is_operator {};
+
+/// Annotation for classes that cannot be subclassed
+struct is_final {};
+
+/// Annotation for parent scope
+struct scope {
+    handle value;
+    explicit scope(const handle &s) : value(s) {}
+};
+
+/// Annotation for documentation
+struct doc {
+    const char *value;
+    explicit doc(const char *value) : value(value) {}
+};
+
+/// Annotation for function names
+struct name {
+    const char *value;
+    explicit name(const char *value) : value(value) {}
+};
+
+/// Annotation indicating that a function is an overload associated with a given "sibling"
+struct sibling {
+    handle value;
+    explicit sibling(const handle &value) : value(value.ptr()) {}
+};
+
+/// Annotation indicating that a class derives from another given type
+template <typename T>
+struct base {
+
+    PYBIND11_DEPRECATED(
+        "base<T>() was deprecated in favor of specifying 'T' as a template argument to class_")
+    base() = default;
+};
+
+/// Keep patient alive while nurse lives
+template <size_t Nurse, size_t Patient>
+struct keep_alive {};
+
+/// Annotation indicating that a class is involved in a multiple inheritance relationship
+struct multiple_inheritance {};
+
+/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class
+struct dynamic_attr {};
+
+/// Annotation which enables the buffer protocol for a type
+struct buffer_protocol {};
+
+/// Annotation which requests that a special metaclass is created for a type
+struct metaclass {
+    handle value;
+
+    PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.")
+    metaclass() = default;
+
+    /// Override pybind11's default metaclass
+    explicit metaclass(handle value) : value(value) {}
+};
+
+/// Specifies a custom callback with signature `void (PyHeapTypeObject*)` that
+/// may be used to customize the Python type.
+///
+/// The callback is invoked immediately before `PyType_Ready`.
+///
+/// Note: This is an advanced interface, and uses of it may require changes to
+/// work with later versions of pybind11.  You may wish to consult the
+/// implementation of `make_new_python_type` in `detail/classes.h` to understand
+/// the context in which the callback will be run.
+struct custom_type_setup {
+    using callback = std::function<void(PyHeapTypeObject *heap_type)>;
+
+    explicit custom_type_setup(callback value) : value(std::move(value)) {}
+
+    callback value;
+};
+
+/// Annotation that marks a class as local to the module:
+struct module_local {
+    const bool value;
+    constexpr explicit module_local(bool v = true) : value(v) {}
+};
+
+/// Annotation to mark enums as an arithmetic type
+struct arithmetic {};
+
+/// Mark a function for addition at the beginning of the existing overload chain instead of the end
+struct prepend {};
+
+/** \rst
+    A call policy which places one or more guard variables (``Ts...``) around the function call.
+
+    For example, this definition:
+
+    .. code-block:: cpp
+
+        m.def("foo", foo, py::call_guard<T>());
+
+    is equivalent to the following pseudocode:
+
+    .. code-block:: cpp
+
+        m.def("foo", [](args...) {
+            T scope_guard;
+            return foo(args...); // forwarded arguments
+        });
+ \endrst */
+template <typename... Ts>
+struct call_guard;
+
+template <>
+struct call_guard<> {
+    using type = detail::void_type;
+};
+
+template <typename T>
+struct call_guard<T> {
+    static_assert(std::is_default_constructible<T>::value,
+                  "The guard type must be default constructible");
+
+    using type = T;
+};
+
+template <typename T, typename... Ts>
+struct call_guard<T, Ts...> {
+    struct type {
+        T guard{}; // Compose multiple guard types with left-to-right default-constructor order
+        typename call_guard<Ts...>::type next{};
+    };
+};
+
+/// @} annotations
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+/* Forward declarations */
+enum op_id : int;
+enum op_type : int;
+struct undefined_t;
+template <op_id id, op_type ot, typename L = undefined_t, typename R = undefined_t>
+struct op_;
+void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret);
+
+/// Internal data structure which holds metadata about a keyword argument
+struct argument_record {
+    const char *name;  ///< Argument name
+    const char *descr; ///< Human-readable version of the argument value
+    handle value;      ///< Associated Python object
+    bool convert : 1;  ///< True if the argument is allowed to convert when loading
+    bool none : 1;     ///< True if None is allowed when loading
+
+    argument_record(const char *name, const char *descr, handle value, bool convert, bool none)
+        : name(name), descr(descr), value(value), convert(convert), none(none) {}
+};
+
+/// Internal data structure which holds metadata about a bound function (signature, overloads,
+/// etc.)
+struct function_record {
+    function_record()
+        : is_constructor(false), is_new_style_constructor(false), is_stateless(false),
+          is_operator(false), is_method(false), is_setter(false), has_args(false),
+          has_kwargs(false), prepend(false) {}
+
+    /// Function name
+    char *name = nullptr; /* why no C++ strings? They generate heavier code.. */
+
+    // User-specified documentation string
+    char *doc = nullptr;
+
+    /// Human-readable version of the function signature
+    char *signature = nullptr;
+
+    /// List of registered keyword arguments
+    std::vector<argument_record> args;
+
+    /// Pointer to lambda function which converts arguments and performs the actual call
+    handle (*impl)(function_call &) = nullptr;
+
+    /// Storage for the wrapped function pointer and captured data, if any
+    void *data[3] = {};
+
+    /// Pointer to custom destructor for 'data' (if needed)
+    void (*free_data)(function_record *ptr) = nullptr;
+
+    /// Return value policy associated with this function
+    return_value_policy policy = return_value_policy::automatic;
+
+    /// True if name == '__init__'
+    bool is_constructor : 1;
+
+    /// True if this is a new-style `__init__` defined in `detail/init.h`
+    bool is_new_style_constructor : 1;
+
+    /// True if this is a stateless function pointer
+    bool is_stateless : 1;
+
+    /// True if this is an operator (__add__), etc.
+    bool is_operator : 1;
+
+    /// True if this is a method
+    bool is_method : 1;
+
+    /// True if this is a setter
+    bool is_setter : 1;
+
+    /// True if the function has a '*args' argument
+    bool has_args : 1;
+
+    /// True if the function has a '**kwargs' argument
+    bool has_kwargs : 1;
+
+    /// True if this function is to be inserted at the beginning of the overload resolution chain
+    bool prepend : 1;
+
+    /// Number of arguments (including py::args and/or py::kwargs, if present)
+    std::uint16_t nargs;
+
+    /// Number of leading positional arguments, which are terminated by a py::args or py::kwargs
+    /// argument or by a py::kw_only annotation.
+    std::uint16_t nargs_pos = 0;
+
+    /// Number of leading arguments (counted in `nargs`) that are positional-only
+    std::uint16_t nargs_pos_only = 0;
+
+    /// Python method object
+    PyMethodDef *def = nullptr;
+
+    /// Python handle to the parent scope (a class or a module)
+    handle scope;
+
+    /// Python handle to the sibling function representing an overload chain
+    handle sibling;
+
+    /// Pointer to next overload
+    function_record *next = nullptr;
+};
+
+/// Special data structure which (temporarily) holds metadata about a bound class
+struct type_record {
+    PYBIND11_NOINLINE type_record()
+        : multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false),
+          default_holder(true), module_local(false), is_final(false) {}
+
+    /// Handle to the parent scope
+    handle scope;
+
+    /// Name of the class
+    const char *name = nullptr;
+
+    // Pointer to RTTI type_info data structure
+    const std::type_info *type = nullptr;
+
+    /// How large is the underlying C++ type?
+    size_t type_size = 0;
+
+    /// What is the alignment of the underlying C++ type?
+    size_t type_align = 0;
+
+    /// How large is the type's holder?
+    size_t holder_size = 0;
+
+    /// The global operator new can be overridden with a class-specific variant
+    void *(*operator_new)(size_t) = nullptr;
+
+    /// Function pointer to class_<..>::init_instance
+    void (*init_instance)(instance *, const void *) = nullptr;
+
+    /// Function pointer to class_<..>::dealloc
+    void (*dealloc)(detail::value_and_holder &) = nullptr;
+
+    /// List of base classes of the newly created type
+    list bases;
+
+    /// Optional docstring
+    const char *doc = nullptr;
+
+    /// Custom metaclass (optional)
+    handle metaclass;
+
+    /// Custom type setup.
+    custom_type_setup::callback custom_type_setup_callback;
+
+    /// Multiple inheritance marker
+    bool multiple_inheritance : 1;
+
+    /// Does the class manage a __dict__?
+    bool dynamic_attr : 1;
+
+    /// Does the class implement the buffer protocol?
+    bool buffer_protocol : 1;
+
+    /// Is the default (unique_ptr) holder type used?
+    bool default_holder : 1;
+
+    /// Is the class definition local to the module shared object?
+    bool module_local : 1;
+
+    /// Is the class inheritable from python classes?
+    bool is_final : 1;
+
+    PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *) ) {
+        auto *base_info = detail::get_type_info(base, false);
+        if (!base_info) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name)
+                          + "\" referenced unknown base type \"" + tname + "\"");
+        }
+
+        if (default_holder != base_info->default_holder) {
+            std::string tname(base.name());
+            detail::clean_type_id(tname);
+            pybind11_fail("generic_type: type \"" + std::string(name) + "\" "
+                          + (default_holder ? "does not have" : "has")
+                          + " a non-default holder type while its base \"" + tname + "\" "
+                          + (base_info->default_holder ? "does not" : "does"));
+        }
+
+        bases.append((PyObject *) base_info->type);
+
+#if PY_VERSION_HEX < 0x030B0000
+        dynamic_attr |= base_info->type->tp_dictoffset != 0;
+#else
+        dynamic_attr |= (base_info->type->tp_flags & Py_TPFLAGS_MANAGED_DICT) != 0;
+#endif
+
+        if (caster) {
+            base_info->implicit_casts.emplace_back(type, caster);
+        }
+    }
+};
+
+inline function_call::function_call(const function_record &f, handle p) : func(f), parent(p) {
+    args.reserve(f.nargs);
+    args_convert.reserve(f.nargs);
+}
+
+/// Tag for a new-style `__init__` defined in `detail/init.h`
+struct is_new_style_constructor {};
+
+/**
+ * Partial template specializations to process custom attributes provided to
+ * cpp_function_ and class_. These are either used to initialize the respective
+ * fields in the type_record and function_record data structures or executed at
+ * runtime to deal with custom call policies (e.g. keep_alive).
+ */
+template <typename T, typename SFINAE = void>
+struct process_attribute;
+
+template <typename T>
+struct process_attribute_default {
+    /// Default implementation: do nothing
+    static void init(const T &, function_record *) {}
+    static void init(const T &, type_record *) {}
+    static void precall(function_call &) {}
+    static void postcall(function_call &, handle) {}
+};
+
+/// Process an attribute specifying the function's name
+template <>
+struct process_attribute<name> : process_attribute_default<name> {
+    static void init(const name &n, function_record *r) { r->name = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring
+template <>
+struct process_attribute<doc> : process_attribute_default<doc> {
+    static void init(const doc &n, function_record *r) { r->doc = const_cast<char *>(n.value); }
+};
+
+/// Process an attribute specifying the function's docstring (provided as a C-style string)
+template <>
+struct process_attribute<const char *> : process_attribute_default<const char *> {
+    static void init(const char *d, function_record *r) { r->doc = const_cast<char *>(d); }
+    static void init(const char *d, type_record *r) { r->doc = d; }
+};
+template <>
+struct process_attribute<char *> : process_attribute<const char *> {};
+
+/// Process an attribute indicating the function's return value policy
+template <>
+struct process_attribute<return_value_policy> : process_attribute_default<return_value_policy> {
+    static void init(const return_value_policy &p, function_record *r) { r->policy = p; }
+};
+
+/// Process an attribute which indicates that this is an overloaded function associated with a
+/// given sibling
+template <>
+struct process_attribute<sibling> : process_attribute_default<sibling> {
+    static void init(const sibling &s, function_record *r) { r->sibling = s.value; }
+};
+
+/// Process an attribute which indicates that this function is a method
+template <>
+struct process_attribute<is_method> : process_attribute_default<is_method> {
+    static void init(const is_method &s, function_record *r) {
+        r->is_method = true;
+        r->scope = s.class_;
+    }
+};
+
+/// Process an attribute which indicates that this function is a setter
+template <>
+struct process_attribute<is_setter> : process_attribute_default<is_setter> {
+    static void init(const is_setter &, function_record *r) { r->is_setter = true; }
+};
+
+/// Process an attribute which indicates the parent scope of a method
+template <>
+struct process_attribute<scope> : process_attribute_default<scope> {
+    static void init(const scope &s, function_record *r) { r->scope = s.value; }
+};
+
+/// Process an attribute which indicates that this function is an operator
+template <>
+struct process_attribute<is_operator> : process_attribute_default<is_operator> {
+    static void init(const is_operator &, function_record *r) { r->is_operator = true; }
+};
+
+template <>
+struct process_attribute<is_new_style_constructor>
+    : process_attribute_default<is_new_style_constructor> {
+    static void init(const is_new_style_constructor &, function_record *r) {
+        r->is_new_style_constructor = true;
+    }
+};
+
+inline void check_kw_only_arg(const arg &a, function_record *r) {
+    if (r->args.size() > r->nargs_pos && (!a.name || a.name[0] == '\0')) {
+        pybind11_fail("arg(): cannot specify an unnamed argument after a kw_only() annotation or "
+                      "args() argument");
+    }
+}
+
+inline void append_self_arg_if_needed(function_record *r) {
+    if (r->is_method && r->args.empty()) {
+        r->args.emplace_back("self", nullptr, handle(), /*convert=*/true, /*none=*/false);
+    }
+}
+
+/// Process a keyword argument attribute (*without* a default value)
+template <>
+struct process_attribute<arg> : process_attribute_default<arg> {
+    static void init(const arg &a, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword argument attribute (*with* a default value)
+template <>
+struct process_attribute<arg_v> : process_attribute_default<arg_v> {
+    static void init(const arg_v &a, function_record *r) {
+        if (r->is_method && r->args.empty()) {
+            r->args.emplace_back(
+                "self", /*descr=*/nullptr, /*parent=*/handle(), /*convert=*/true, /*none=*/false);
+        }
+
+        if (!a.value) {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            std::string descr("'");
+            if (a.name) {
+                descr += std::string(a.name) + ": ";
+            }
+            descr += a.type + "'";
+            if (r->is_method) {
+                if (r->name) {
+                    descr += " in method '" + (std::string) str(r->scope) + "."
+                             + (std::string) r->name + "'";
+                } else {
+                    descr += " in method of '" + (std::string) str(r->scope) + "'";
+                }
+            } else if (r->name) {
+                descr += " in function '" + (std::string) r->name + "'";
+            }
+            pybind11_fail("arg(): could not convert default argument " + descr
+                          + " into a Python object (type not registered yet?)");
+#else
+            pybind11_fail("arg(): could not convert default argument "
+                          "into a Python object (type not registered yet?). "
+                          "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for "
+                          "more information.");
+#endif
+        }
+        r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none);
+
+        check_kw_only_arg(a, r);
+    }
+};
+
+/// Process a keyword-only-arguments-follow pseudo argument
+template <>
+struct process_attribute<kw_only> : process_attribute_default<kw_only> {
+    static void init(const kw_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        if (r->has_args && r->nargs_pos != static_cast<std::uint16_t>(r->args.size())) {
+            pybind11_fail("Mismatched args() and kw_only(): they must occur at the same relative "
+                          "argument location (or omit kw_only() entirely)");
+        }
+        r->nargs_pos = static_cast<std::uint16_t>(r->args.size());
+    }
+};
+
+/// Process a positional-only-argument maker
+template <>
+struct process_attribute<pos_only> : process_attribute_default<pos_only> {
+    static void init(const pos_only &, function_record *r) {
+        append_self_arg_if_needed(r);
+        r->nargs_pos_only = static_cast<std::uint16_t>(r->args.size());
+        if (r->nargs_pos_only > r->nargs_pos) {
+            pybind11_fail("pos_only(): cannot follow a py::args() argument");
+        }
+        // It also can't follow a kw_only, but a static_assert in pybind11.h checks that
+    }
+};
+
+/// Process a parent class attribute.  Single inheritance only (class_ itself already guarantees
+/// that)
+template <typename T>
+struct process_attribute<T, enable_if_t<is_pyobject<T>::value>>
+    : process_attribute_default<handle> {
+    static void init(const handle &h, type_record *r) { r->bases.append(h); }
+};
+
+/// Process a parent class attribute (deprecated, does not support multiple inheritance)
+template <typename T>
+struct process_attribute<base<T>> : process_attribute_default<base<T>> {
+    static void init(const base<T> &, type_record *r) { r->add_base(typeid(T), nullptr); }
+};
+
+/// Process a multiple inheritance attribute
+template <>
+struct process_attribute<multiple_inheritance> : process_attribute_default<multiple_inheritance> {
+    static void init(const multiple_inheritance &, type_record *r) {
+        r->multiple_inheritance = true;
+    }
+};
+
+template <>
+struct process_attribute<dynamic_attr> : process_attribute_default<dynamic_attr> {
+    static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; }
+};
+
+template <>
+struct process_attribute<custom_type_setup> {
+    static void init(const custom_type_setup &value, type_record *r) {
+        r->custom_type_setup_callback = value.value;
+    }
+};
+
+template <>
+struct process_attribute<is_final> : process_attribute_default<is_final> {
+    static void init(const is_final &, type_record *r) { r->is_final = true; }
+};
+
+template <>
+struct process_attribute<buffer_protocol> : process_attribute_default<buffer_protocol> {
+    static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; }
+};
+
+template <>
+struct process_attribute<metaclass> : process_attribute_default<metaclass> {
+    static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; }
+};
+
+template <>
+struct process_attribute<module_local> : process_attribute_default<module_local> {
+    static void init(const module_local &l, type_record *r) { r->module_local = l.value; }
+};
+
+/// Process a 'prepend' attribute, putting this at the beginning of the overload chain
+template <>
+struct process_attribute<prepend> : process_attribute_default<prepend> {
+    static void init(const prepend &, function_record *r) { r->prepend = true; }
+};
+
+/// Process an 'arithmetic' attribute for enums (does nothing here)
+template <>
+struct process_attribute<arithmetic> : process_attribute_default<arithmetic> {};
+
+template <typename... Ts>
+struct process_attribute<call_guard<Ts...>> : process_attribute_default<call_guard<Ts...>> {};
+
+/**
+ * Process a keep_alive call policy -- invokes keep_alive_impl during the
+ * pre-call handler if both Nurse, Patient != 0 and use the post-call handler
+ * otherwise
+ */
+template <size_t Nurse, size_t Patient>
+struct process_attribute<keep_alive<Nurse, Patient>>
+    : public process_attribute_default<keep_alive<Nurse, Patient>> {
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void precall(function_call &call) {
+        keep_alive_impl(Nurse, Patient, call, handle());
+    }
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N != 0 && P != 0, int> = 0>
+    static void postcall(function_call &, handle) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void precall(function_call &) {}
+    template <size_t N = Nurse, size_t P = Patient, enable_if_t<N == 0 || P == 0, int> = 0>
+    static void postcall(function_call &call, handle ret) {
+        keep_alive_impl(Nurse, Patient, call, ret);
+    }
+};
+
+/// Recursively iterate over variadic template arguments
+template <typename... Args>
+struct process_attributes {
+    static void init(const Args &...args, function_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{
+            0, ((void) process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void init(const Args &...args, type_record *r) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::init(args, r), 0)...};
+    }
+    static void precall(function_call &call) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call);
+        using expander = int[];
+        (void) expander{0,
+                        (process_attribute<typename std::decay<Args>::type>::precall(call), 0)...};
+    }
+    static void postcall(function_call &call, handle fn_ret) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call, fn_ret);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(fn_ret);
+        using expander = int[];
+        (void) expander{
+            0, (process_attribute<typename std::decay<Args>::type>::postcall(call, fn_ret), 0)...};
+    }
+};
+
+template <typename T>
+using is_call_guard = is_instantiation<call_guard, T>;
+
+/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found)
+template <typename... Extra>
+using extract_guard_t = typename exactly_one_t<is_call_guard, call_guard<>, Extra...>::type;
+
+/// Check the number of named arguments at compile time
+template <typename... Extra,
+          size_t named = constexpr_sum(std::is_base_of<arg, Extra>::value...),
+          size_t self = constexpr_sum(std::is_same<is_method, Extra>::value...)>
+constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(nargs, has_args, has_kwargs);
+    return named == 0 || (self + named + size_t(has_args) + size_t(has_kwargs)) == nargs;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/buffer_info.h b/lib/python3.11/site-packages/torch/include/pybind11/buffer_info.h
new file mode 100644
index 0000000000000000000000000000000000000000..75aec0ba3092a401a73f7cdb09b0894aef85cc27
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/buffer_info.h
@@ -0,0 +1,208 @@
+/*
+    pybind11/buffer_info.h: Python buffer object interface
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Default, C-style strides
+inline std::vector<ssize_t> c_strides(const std::vector<ssize_t> &shape, ssize_t itemsize) {
+    auto ndim = shape.size();
+    std::vector<ssize_t> strides(ndim, itemsize);
+    if (ndim > 0) {
+        for (size_t i = ndim - 1; i > 0; --i) {
+            strides[i - 1] = strides[i] * shape[i];
+        }
+    }
+    return strides;
+}
+
+// F-style strides; default when constructing an array_t with `ExtraFlags & f_style`
+inline std::vector<ssize_t> f_strides(const std::vector<ssize_t> &shape, ssize_t itemsize) {
+    auto ndim = shape.size();
+    std::vector<ssize_t> strides(ndim, itemsize);
+    for (size_t i = 1; i < ndim; ++i) {
+        strides[i] = strides[i - 1] * shape[i - 1];
+    }
+    return strides;
+}
+
+template <typename T, typename SFINAE = void>
+struct compare_buffer_info;
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Information record describing a Python buffer object
+struct buffer_info {
+    void *ptr = nullptr;          // Pointer to the underlying storage
+    ssize_t itemsize = 0;         // Size of individual items in bytes
+    ssize_t size = 0;             // Total number of entries
+    std::string format;           // For homogeneous buffers, this should be set to
+                                  // format_descriptor<T>::format()
+    ssize_t ndim = 0;             // Number of dimensions
+    std::vector<ssize_t> shape;   // Shape of the tensor (1 entry per dimension)
+    std::vector<ssize_t> strides; // Number of bytes between adjacent entries
+                                  // (for each per dimension)
+    bool readonly = false;        // flag to indicate if the underlying storage may be written to
+
+    buffer_info() = default;
+
+    buffer_info(void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t ndim,
+                detail::any_container<ssize_t> shape_in,
+                detail::any_container<ssize_t> strides_in,
+                bool readonly = false)
+        : ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim),
+          shape(std::move(shape_in)), strides(std::move(strides_in)), readonly(readonly) {
+        if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size()) {
+            pybind11_fail("buffer_info: ndim doesn't match shape and/or strides length");
+        }
+        for (size_t i = 0; i < (size_t) ndim; ++i) {
+            size *= shape[i];
+        }
+    }
+
+    template <typename T>
+    buffer_info(T *ptr,
+                detail::any_container<ssize_t> shape_in,
+                detail::any_container<ssize_t> strides_in,
+                bool readonly = false)
+        : buffer_info(private_ctr_tag(),
+                      ptr,
+                      sizeof(T),
+                      format_descriptor<T>::format(),
+                      static_cast<ssize_t>(shape_in->size()),
+                      std::move(shape_in),
+                      std::move(strides_in),
+                      readonly) {}
+
+    buffer_info(void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t size,
+                bool readonly = false)
+        : buffer_info(ptr, itemsize, format, 1, {size}, {itemsize}, readonly) {}
+
+    template <typename T>
+    buffer_info(T *ptr, ssize_t size, bool readonly = false)
+        : buffer_info(ptr, sizeof(T), format_descriptor<T>::format(), size, readonly) {}
+
+    template <typename T>
+    buffer_info(const T *ptr, ssize_t size, bool readonly = true)
+        : buffer_info(
+              const_cast<T *>(ptr), sizeof(T), format_descriptor<T>::format(), size, readonly) {}
+
+    explicit buffer_info(Py_buffer *view, bool ownview = true)
+        : buffer_info(
+              view->buf,
+              view->itemsize,
+              view->format,
+              view->ndim,
+              {view->shape, view->shape + view->ndim},
+              /* Though buffer::request() requests PyBUF_STRIDES, ctypes objects
+               * ignore this flag and return a view with NULL strides.
+               * When strides are NULL, build them manually.  */
+              view->strides
+                  ? std::vector<ssize_t>(view->strides, view->strides + view->ndim)
+                  : detail::c_strides({view->shape, view->shape + view->ndim}, view->itemsize),
+              (view->readonly != 0)) {
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        this->m_view = view;
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        this->ownview = ownview;
+    }
+
+    buffer_info(const buffer_info &) = delete;
+    buffer_info &operator=(const buffer_info &) = delete;
+
+    buffer_info(buffer_info &&other) noexcept { (*this) = std::move(other); }
+
+    buffer_info &operator=(buffer_info &&rhs) noexcept {
+        ptr = rhs.ptr;
+        itemsize = rhs.itemsize;
+        size = rhs.size;
+        format = std::move(rhs.format);
+        ndim = rhs.ndim;
+        shape = std::move(rhs.shape);
+        strides = std::move(rhs.strides);
+        std::swap(m_view, rhs.m_view);
+        std::swap(ownview, rhs.ownview);
+        readonly = rhs.readonly;
+        return *this;
+    }
+
+    ~buffer_info() {
+        if (m_view && ownview) {
+            PyBuffer_Release(m_view);
+            delete m_view;
+        }
+    }
+
+    Py_buffer *view() const { return m_view; }
+    Py_buffer *&view() { return m_view; }
+
+    /* True if the buffer item type is equivalent to `T`. */
+    // To define "equivalent" by example:
+    // `buffer_info::item_type_is_equivalent_to<int>(b)` and
+    // `buffer_info::item_type_is_equivalent_to<long>(b)` may both be true
+    // on some platforms, but `int` and `unsigned` will never be equivalent.
+    // For the ground truth, please inspect `detail::compare_buffer_info<>`.
+    template <typename T>
+    bool item_type_is_equivalent_to() const {
+        return detail::compare_buffer_info<T>::compare(*this);
+    }
+
+private:
+    struct private_ctr_tag {};
+
+    buffer_info(private_ctr_tag,
+                void *ptr,
+                ssize_t itemsize,
+                const std::string &format,
+                ssize_t ndim,
+                detail::any_container<ssize_t> &&shape_in,
+                detail::any_container<ssize_t> &&strides_in,
+                bool readonly)
+        : buffer_info(
+              ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in), readonly) {}
+
+    Py_buffer *m_view = nullptr;
+    bool ownview = false;
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T, typename SFINAE>
+struct compare_buffer_info {
+    static bool compare(const buffer_info &b) {
+        // NOLINTNEXTLINE(bugprone-sizeof-expression) Needed for `PyObject *`
+        return b.format == format_descriptor<T>::format() && b.itemsize == (ssize_t) sizeof(T);
+    }
+};
+
+template <typename T>
+struct compare_buffer_info<T, detail::enable_if_t<std::is_integral<T>::value>> {
+    static bool compare(const buffer_info &b) {
+        return (size_t) b.itemsize == sizeof(T)
+               && (b.format == format_descriptor<T>::value
+                   || ((sizeof(T) == sizeof(long))
+                       && b.format == (std::is_unsigned<T>::value ? "L" : "l"))
+                   || ((sizeof(T) == sizeof(size_t))
+                       && b.format == (std::is_unsigned<T>::value ? "N" : "n")));
+    }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/cast.h b/lib/python3.11/site-packages/torch/include/pybind11/cast.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f3091f6869900d14d521791198d97fcccf40629
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/cast.h
@@ -0,0 +1,1855 @@
+/*
+    pybind11/cast.h: Partial template specializations to cast between
+    C++ and Python types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "detail/descr.h"
+#include "detail/type_caster_base.h"
+#include "detail/typeid.h"
+#include "pytypes.h"
+
+#include <array>
+#include <cstring>
+#include <functional>
+#include <iosfwd>
+#include <iterator>
+#include <memory>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename type, typename SFINAE = void>
+class type_caster : public type_caster_base<type> {};
+template <typename type>
+using make_caster = type_caster<intrinsic_t<type>>;
+
+// Shortcut for calling a caster's `cast_op_type` cast operator for casting a type_caster to a T
+template <typename T>
+typename make_caster<T>::template cast_op_type<T> cast_op(make_caster<T> &caster) {
+    using result_t = typename make_caster<T>::template cast_op_type<T>; // See PR #4893
+    return caster.operator result_t();
+}
+template <typename T>
+typename make_caster<T>::template cast_op_type<typename std::add_rvalue_reference<T>::type>
+cast_op(make_caster<T> &&caster) {
+    using result_t = typename make_caster<T>::template cast_op_type<
+        typename std::add_rvalue_reference<T>::type>; // See PR #4893
+    return std::move(caster).operator result_t();
+}
+
+template <typename type>
+class type_caster<std::reference_wrapper<type>> {
+private:
+    using caster_t = make_caster<type>;
+    caster_t subcaster;
+    using reference_t = type &;
+    using subcaster_cast_op_type = typename caster_t::template cast_op_type<reference_t>;
+
+    static_assert(
+        std::is_same<typename std::remove_const<type>::type &, subcaster_cast_op_type>::value
+            || std::is_same<reference_t, subcaster_cast_op_type>::value,
+        "std::reference_wrapper<T> caster requires T to have a caster with an "
+        "`operator T &()` or `operator const T &()`");
+
+public:
+    bool load(handle src, bool convert) { return subcaster.load(src, convert); }
+    static constexpr auto name = caster_t::name;
+    static handle
+    cast(const std::reference_wrapper<type> &src, return_value_policy policy, handle parent) {
+        // It is definitely wrong to take ownership of this pointer, so mask that rvp
+        if (policy == return_value_policy::take_ownership
+            || policy == return_value_policy::automatic) {
+            policy = return_value_policy::automatic_reference;
+        }
+        return caster_t::cast(&src.get(), policy, parent);
+    }
+    template <typename T>
+    using cast_op_type = std::reference_wrapper<type>;
+    explicit operator std::reference_wrapper<type>() { return cast_op<type &>(subcaster); }
+};
+
+#define PYBIND11_TYPE_CASTER(type, py_name)                                                       \
+protected:                                                                                        \
+    type value;                                                                                   \
+                                                                                                  \
+public:                                                                                           \
+    static constexpr auto name = py_name;                                                         \
+    template <typename T_,                                                                        \
+              ::pybind11::detail::enable_if_t<                                                    \
+                  std::is_same<type, ::pybind11::detail::remove_cv_t<T_>>::value,                 \
+                  int>                                                                            \
+              = 0>                                                                                \
+    static ::pybind11::handle cast(                                                               \
+        T_ *src, ::pybind11::return_value_policy policy, ::pybind11::handle parent) {             \
+        if (!src)                                                                                 \
+            return ::pybind11::none().release();                                                  \
+        if (policy == ::pybind11::return_value_policy::take_ownership) {                          \
+            auto h = cast(std::move(*src), policy, parent);                                       \
+            delete src;                                                                           \
+            return h;                                                                             \
+        }                                                                                         \
+        return cast(*src, policy, parent);                                                        \
+    }                                                                                             \
+    operator type *() { return &value; }               /* NOLINT(bugprone-macro-parentheses) */   \
+    operator type &() { return value; }                /* NOLINT(bugprone-macro-parentheses) */   \
+    operator type &&() && { return std::move(value); } /* NOLINT(bugprone-macro-parentheses) */   \
+    template <typename T_>                                                                        \
+    using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>
+
+template <typename CharT>
+using is_std_char_type = any_of<std::is_same<CharT, char>, /* std::string */
+#if defined(PYBIND11_HAS_U8STRING)
+                                std::is_same<CharT, char8_t>, /* std::u8string */
+#endif
+                                std::is_same<CharT, char16_t>, /* std::u16string */
+                                std::is_same<CharT, char32_t>, /* std::u32string */
+                                std::is_same<CharT, wchar_t>   /* std::wstring */
+                                >;
+
+template <typename T>
+struct type_caster<T, enable_if_t<std::is_arithmetic<T>::value && !is_std_char_type<T>::value>> {
+    using _py_type_0 = conditional_t<sizeof(T) <= sizeof(long), long, long long>;
+    using _py_type_1 = conditional_t<std::is_signed<T>::value,
+                                     _py_type_0,
+                                     typename std::make_unsigned<_py_type_0>::type>;
+    using py_type = conditional_t<std::is_floating_point<T>::value, double, _py_type_1>;
+
+public:
+    bool load(handle src, bool convert) {
+        py_type py_value;
+
+        if (!src) {
+            return false;
+        }
+
+#if !defined(PYPY_VERSION)
+        auto index_check = [](PyObject *o) { return PyIndex_Check(o); };
+#else
+        // In PyPy 7.3.3, `PyIndex_Check` is implemented by calling `__index__`,
+        // while CPython only considers the existence of `nb_index`/`__index__`.
+        auto index_check = [](PyObject *o) { return hasattr(o, "__index__"); };
+#endif
+
+        if (std::is_floating_point<T>::value) {
+            if (convert || PyFloat_Check(src.ptr())) {
+                py_value = (py_type) PyFloat_AsDouble(src.ptr());
+            } else {
+                return false;
+            }
+        } else if (PyFloat_Check(src.ptr())
+                   || (!convert && !PYBIND11_LONG_CHECK(src.ptr()) && !index_check(src.ptr()))) {
+            return false;
+        } else {
+            handle src_or_index = src;
+            // PyPy: 7.3.7's 3.8 does not implement PyLong_*'s __index__ calls.
+#if PY_VERSION_HEX < 0x03080000 || defined(PYPY_VERSION)
+            object index;
+            if (!PYBIND11_LONG_CHECK(src.ptr())) { // So: index_check(src.ptr())
+                index = reinterpret_steal<object>(PyNumber_Index(src.ptr()));
+                if (!index) {
+                    PyErr_Clear();
+                    if (!convert)
+                        return false;
+                } else {
+                    src_or_index = index;
+                }
+            }
+#endif
+            if (std::is_unsigned<py_type>::value) {
+                py_value = as_unsigned<py_type>(src_or_index.ptr());
+            } else { // signed integer:
+                py_value = sizeof(T) <= sizeof(long)
+                               ? (py_type) PyLong_AsLong(src_or_index.ptr())
+                               : (py_type) PYBIND11_LONG_AS_LONGLONG(src_or_index.ptr());
+            }
+        }
+
+        // Python API reported an error
+        bool py_err = py_value == (py_type) -1 && PyErr_Occurred();
+
+        // Check to see if the conversion is valid (integers should match exactly)
+        // Signed/unsigned checks happen elsewhere
+        if (py_err
+            || (std::is_integral<T>::value && sizeof(py_type) != sizeof(T)
+                && py_value != (py_type) (T) py_value)) {
+            PyErr_Clear();
+            if (py_err && convert && (PyNumber_Check(src.ptr()) != 0)) {
+                auto tmp = reinterpret_steal<object>(std::is_floating_point<T>::value
+                                                         ? PyNumber_Float(src.ptr())
+                                                         : PyNumber_Long(src.ptr()));
+                PyErr_Clear();
+                return load(tmp, false);
+            }
+            return false;
+        }
+
+        value = (T) py_value;
+        return true;
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<std::is_floating_point<U>::value, handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyFloat_FromDouble((double) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_signed<U>::value
+                                       && (sizeof(U) <= sizeof(long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PYBIND11_LONG_FROM_SIGNED((long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_unsigned<U>::value
+                                       && (sizeof(U) <= sizeof(unsigned long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PYBIND11_LONG_FROM_UNSIGNED((unsigned long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_signed<U>::value
+                                       && (sizeof(U) > sizeof(long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyLong_FromLongLong((long long) src);
+    }
+
+    template <typename U = T>
+    static typename std::enable_if<!std::is_floating_point<U>::value && std::is_unsigned<U>::value
+                                       && (sizeof(U) > sizeof(unsigned long)),
+                                   handle>::type
+    cast(U src, return_value_policy /* policy */, handle /* parent */) {
+        return PyLong_FromUnsignedLongLong((unsigned long long) src);
+    }
+
+    PYBIND11_TYPE_CASTER(T, const_name<std::is_integral<T>::value>("int", "float"));
+};
+
+template <typename T>
+struct void_caster {
+public:
+    bool load(handle src, bool) {
+        if (src && src.is_none()) {
+            return true;
+        }
+        return false;
+    }
+    static handle cast(T, return_value_policy /* policy */, handle /* parent */) {
+        return none().release();
+    }
+    PYBIND11_TYPE_CASTER(T, const_name("None"));
+};
+
+template <>
+class type_caster<void_type> : public void_caster<void_type> {};
+
+template <>
+class type_caster<void> : public type_caster<void_type> {
+public:
+    using type_caster<void_type>::cast;
+
+    bool load(handle h, bool) {
+        if (!h) {
+            return false;
+        }
+        if (h.is_none()) {
+            value = nullptr;
+            return true;
+        }
+
+        /* Check if this is a capsule */
+        if (isinstance<capsule>(h)) {
+            value = reinterpret_borrow<capsule>(h);
+            return true;
+        }
+
+        /* Check if this is a C++ type */
+        const auto &bases = all_type_info((PyTypeObject *) type::handle_of(h).ptr());
+        if (bases.size() == 1) { // Only allowing loading from a single-value type
+            value = values_and_holders(reinterpret_cast<instance *>(h.ptr())).begin()->value_ptr();
+            return true;
+        }
+
+        /* Fail */
+        return false;
+    }
+
+    static handle cast(const void *ptr, return_value_policy /* policy */, handle /* parent */) {
+        if (ptr) {
+            return capsule(ptr).release();
+        }
+        return none().release();
+    }
+
+    template <typename T>
+    using cast_op_type = void *&;
+    explicit operator void *&() { return value; }
+    static constexpr auto name = const_name("capsule");
+
+private:
+    void *value = nullptr;
+};
+
+template <>
+class type_caster<std::nullptr_t> : public void_caster<std::nullptr_t> {};
+
+template <>
+class type_caster<bool> {
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.ptr() == Py_True) {
+            value = true;
+            return true;
+        }
+        if (src.ptr() == Py_False) {
+            value = false;
+            return true;
+        }
+        if (convert || is_numpy_bool(src)) {
+            // (allow non-implicit conversion for numpy booleans), use strncmp
+            // since NumPy 1.x had an additional trailing underscore.
+
+            Py_ssize_t res = -1;
+            if (src.is_none()) {
+                res = 0; // None is implicitly converted to False
+            }
+#if defined(PYPY_VERSION)
+            // On PyPy, check that "__bool__" attr exists
+            else if (hasattr(src, PYBIND11_BOOL_ATTR)) {
+                res = PyObject_IsTrue(src.ptr());
+            }
+#else
+            // Alternate approach for CPython: this does the same as the above, but optimized
+            // using the CPython API so as to avoid an unneeded attribute lookup.
+            else if (auto *tp_as_number = src.ptr()->ob_type->tp_as_number) {
+                if (PYBIND11_NB_BOOL(tp_as_number)) {
+                    res = (*PYBIND11_NB_BOOL(tp_as_number))(src.ptr());
+                }
+            }
+#endif
+            if (res == 0 || res == 1) {
+                value = (res != 0);
+                return true;
+            }
+            PyErr_Clear();
+        }
+        return false;
+    }
+    static handle cast(bool src, return_value_policy /* policy */, handle /* parent */) {
+        return handle(src ? Py_True : Py_False).inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(bool, const_name("bool"));
+
+private:
+    // Test if an object is a NumPy boolean (without fetching the type).
+    static inline bool is_numpy_bool(handle object) {
+        const char *type_name = Py_TYPE(object.ptr())->tp_name;
+        // Name changed to `numpy.bool` in NumPy 2, `numpy.bool_` is needed for 1.x support
+        return std::strcmp("numpy.bool", type_name) == 0
+               || std::strcmp("numpy.bool_", type_name) == 0;
+    }
+};
+
+// Helper class for UTF-{8,16,32} C++ stl strings:
+template <typename StringType, bool IsView = false>
+struct string_caster {
+    using CharT = typename StringType::value_type;
+
+    // Simplify life by being able to assume standard char sizes (the standard only guarantees
+    // minimums, but Python requires exact sizes)
+    static_assert(!std::is_same<CharT, char>::value || sizeof(CharT) == 1,
+                  "Unsupported char size != 1");
+#if defined(PYBIND11_HAS_U8STRING)
+    static_assert(!std::is_same<CharT, char8_t>::value || sizeof(CharT) == 1,
+                  "Unsupported char8_t size != 1");
+#endif
+    static_assert(!std::is_same<CharT, char16_t>::value || sizeof(CharT) == 2,
+                  "Unsupported char16_t size != 2");
+    static_assert(!std::is_same<CharT, char32_t>::value || sizeof(CharT) == 4,
+                  "Unsupported char32_t size != 4");
+    // wchar_t can be either 16 bits (Windows) or 32 (everywhere else)
+    static_assert(!std::is_same<CharT, wchar_t>::value || sizeof(CharT) == 2 || sizeof(CharT) == 4,
+                  "Unsupported wchar_t size != 2/4");
+    static constexpr size_t UTF_N = 8 * sizeof(CharT);
+
+    bool load(handle src, bool) {
+        handle load_src = src;
+        if (!src) {
+            return false;
+        }
+        if (!PyUnicode_Check(load_src.ptr())) {
+            return load_raw(load_src);
+        }
+
+        // For UTF-8 we avoid the need for a temporary `bytes` object by using
+        // `PyUnicode_AsUTF8AndSize`.
+        if (UTF_N == 8) {
+            Py_ssize_t size = -1;
+            const auto *buffer
+                = reinterpret_cast<const CharT *>(PyUnicode_AsUTF8AndSize(load_src.ptr(), &size));
+            if (!buffer) {
+                PyErr_Clear();
+                return false;
+            }
+            value = StringType(buffer, static_cast<size_t>(size));
+            return true;
+        }
+
+        auto utfNbytes
+            = reinterpret_steal<object>(PyUnicode_AsEncodedString(load_src.ptr(),
+                                                                  UTF_N == 8    ? "utf-8"
+                                                                  : UTF_N == 16 ? "utf-16"
+                                                                                : "utf-32",
+                                                                  nullptr));
+        if (!utfNbytes) {
+            PyErr_Clear();
+            return false;
+        }
+
+        const auto *buffer
+            = reinterpret_cast<const CharT *>(PYBIND11_BYTES_AS_STRING(utfNbytes.ptr()));
+        size_t length = (size_t) PYBIND11_BYTES_SIZE(utfNbytes.ptr()) / sizeof(CharT);
+        // Skip BOM for UTF-16/32
+        if (UTF_N > 8) {
+            buffer++;
+            length--;
+        }
+        value = StringType(buffer, length);
+
+        // If we're loading a string_view we need to keep the encoded Python object alive:
+        if (IsView) {
+            loader_life_support::add_patient(utfNbytes);
+        }
+
+        return true;
+    }
+
+    static handle
+    cast(const StringType &src, return_value_policy /* policy */, handle /* parent */) {
+        const char *buffer = reinterpret_cast<const char *>(src.data());
+        auto nbytes = ssize_t(src.size() * sizeof(CharT));
+        handle s = decode_utfN(buffer, nbytes);
+        if (!s) {
+            throw error_already_set();
+        }
+        return s;
+    }
+
+    PYBIND11_TYPE_CASTER(StringType, const_name(PYBIND11_STRING_NAME));
+
+private:
+    static handle decode_utfN(const char *buffer, ssize_t nbytes) {
+#if !defined(PYPY_VERSION)
+        return UTF_N == 8    ? PyUnicode_DecodeUTF8(buffer, nbytes, nullptr)
+               : UTF_N == 16 ? PyUnicode_DecodeUTF16(buffer, nbytes, nullptr, nullptr)
+                             : PyUnicode_DecodeUTF32(buffer, nbytes, nullptr, nullptr);
+#else
+        // PyPy segfaults when on PyUnicode_DecodeUTF16 (and possibly on PyUnicode_DecodeUTF32 as
+        // well), so bypass the whole thing by just passing the encoding as a string value, which
+        // works properly:
+        return PyUnicode_Decode(buffer,
+                                nbytes,
+                                UTF_N == 8    ? "utf-8"
+                                : UTF_N == 16 ? "utf-16"
+                                              : "utf-32",
+                                nullptr);
+#endif
+    }
+
+    // When loading into a std::string or char*, accept a bytes/bytearray object as-is (i.e.
+    // without any encoding/decoding attempt).  For other C++ char sizes this is a no-op.
+    // which supports loading a unicode from a str, doesn't take this path.
+    template <typename C = CharT>
+    bool load_raw(enable_if_t<std::is_same<C, char>::value, handle> src) {
+        if (PYBIND11_BYTES_CHECK(src.ptr())) {
+            // We were passed raw bytes; accept it into a std::string or char*
+            // without any encoding attempt.
+            const char *bytes = PYBIND11_BYTES_AS_STRING(src.ptr());
+            if (!bytes) {
+                pybind11_fail("Unexpected PYBIND11_BYTES_AS_STRING() failure.");
+            }
+            value = StringType(bytes, (size_t) PYBIND11_BYTES_SIZE(src.ptr()));
+            return true;
+        }
+        if (PyByteArray_Check(src.ptr())) {
+            // We were passed a bytearray; accept it into a std::string or char*
+            // without any encoding attempt.
+            const char *bytearray = PyByteArray_AsString(src.ptr());
+            if (!bytearray) {
+                pybind11_fail("Unexpected PyByteArray_AsString() failure.");
+            }
+            value = StringType(bytearray, (size_t) PyByteArray_Size(src.ptr()));
+            return true;
+        }
+
+        return false;
+    }
+
+    template <typename C = CharT>
+    bool load_raw(enable_if_t<!std::is_same<C, char>::value, handle>) {
+        return false;
+    }
+};
+
+template <typename CharT, class Traits, class Allocator>
+struct type_caster<std::basic_string<CharT, Traits, Allocator>,
+                   enable_if_t<is_std_char_type<CharT>::value>>
+    : string_caster<std::basic_string<CharT, Traits, Allocator>> {};
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+template <typename CharT, class Traits>
+struct type_caster<std::basic_string_view<CharT, Traits>,
+                   enable_if_t<is_std_char_type<CharT>::value>>
+    : string_caster<std::basic_string_view<CharT, Traits>, true> {};
+#endif
+
+// Type caster for C-style strings.  We basically use a std::string type caster, but also add the
+// ability to use None as a nullptr char* (which the string caster doesn't allow).
+template <typename CharT>
+struct type_caster<CharT, enable_if_t<is_std_char_type<CharT>::value>> {
+    using StringType = std::basic_string<CharT>;
+    using StringCaster = make_caster<StringType>;
+    StringCaster str_caster;
+    bool none = false;
+    CharT one_char = 0;
+
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            none = true;
+            return true;
+        }
+        return str_caster.load(src, convert);
+    }
+
+    static handle cast(const CharT *src, return_value_policy policy, handle parent) {
+        if (src == nullptr) {
+            return pybind11::none().release();
+        }
+        return StringCaster::cast(StringType(src), policy, parent);
+    }
+
+    static handle cast(CharT src, return_value_policy policy, handle parent) {
+        if (std::is_same<char, CharT>::value) {
+            handle s = PyUnicode_DecodeLatin1((const char *) &src, 1, nullptr);
+            if (!s) {
+                throw error_already_set();
+            }
+            return s;
+        }
+        return StringCaster::cast(StringType(1, src), policy, parent);
+    }
+
+    explicit operator CharT *() {
+        return none ? nullptr : const_cast<CharT *>(static_cast<StringType &>(str_caster).c_str());
+    }
+    explicit operator CharT &() {
+        if (none) {
+            throw value_error("Cannot convert None to a character");
+        }
+
+        auto &value = static_cast<StringType &>(str_caster);
+        size_t str_len = value.size();
+        if (str_len == 0) {
+            throw value_error("Cannot convert empty string to a character");
+        }
+
+        // If we're in UTF-8 mode, we have two possible failures: one for a unicode character that
+        // is too high, and one for multiple unicode characters (caught later), so we need to
+        // figure out how long the first encoded character is in bytes to distinguish between these
+        // two errors.  We also allow want to allow unicode characters U+0080 through U+00FF, as
+        // those can fit into a single char value.
+        if (StringCaster::UTF_N == 8 && str_len > 1 && str_len <= 4) {
+            auto v0 = static_cast<unsigned char>(value[0]);
+            // low bits only: 0-127
+            // 0b110xxxxx - start of 2-byte sequence
+            // 0b1110xxxx - start of 3-byte sequence
+            // 0b11110xxx - start of 4-byte sequence
+            size_t char0_bytes = (v0 & 0x80) == 0      ? 1
+                                 : (v0 & 0xE0) == 0xC0 ? 2
+                                 : (v0 & 0xF0) == 0xE0 ? 3
+                                                       : 4;
+
+            if (char0_bytes == str_len) {
+                // If we have a 128-255 value, we can decode it into a single char:
+                if (char0_bytes == 2 && (v0 & 0xFC) == 0xC0) { // 0x110000xx 0x10xxxxxx
+                    one_char = static_cast<CharT>(((v0 & 3) << 6)
+                                                  + (static_cast<unsigned char>(value[1]) & 0x3F));
+                    return one_char;
+                }
+                // Otherwise we have a single character, but it's > U+00FF
+                throw value_error("Character code point not in range(0x100)");
+            }
+        }
+
+        // UTF-16 is much easier: we can only have a surrogate pair for values above U+FFFF, thus a
+        // surrogate pair with total length 2 instantly indicates a range error (but not a "your
+        // string was too long" error).
+        else if (StringCaster::UTF_N == 16 && str_len == 2) {
+            one_char = static_cast<CharT>(value[0]);
+            if (one_char >= 0xD800 && one_char < 0xE000) {
+                throw value_error("Character code point not in range(0x10000)");
+            }
+        }
+
+        if (str_len != 1) {
+            throw value_error("Expected a character, but multi-character string found");
+        }
+
+        one_char = value[0];
+        return one_char;
+    }
+
+    static constexpr auto name = const_name(PYBIND11_STRING_NAME);
+    template <typename _T>
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+};
+
+// Base implementation for std::tuple and std::pair
+template <template <typename...> class Tuple, typename... Ts>
+class tuple_caster {
+    using type = Tuple<Ts...>;
+    static constexpr auto size = sizeof...(Ts);
+    using indices = make_index_sequence<size>;
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src)) {
+            return false;
+        }
+        const auto seq = reinterpret_borrow<sequence>(src);
+        if (seq.size() != size) {
+            return false;
+        }
+        return load_impl(seq, convert, indices{});
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        return cast_impl(std::forward<T>(src), policy, parent, indices{});
+    }
+
+    // copied from the PYBIND11_TYPE_CASTER macro
+    template <typename T>
+    static handle cast(T *src, return_value_policy policy, handle parent) {
+        if (!src) {
+            return none().release();
+        }
+        if (policy == return_value_policy::take_ownership) {
+            auto h = cast(std::move(*src), policy, parent);
+            delete src;
+            return h;
+        }
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name = const_name("tuple[")
+                                 + ::pybind11::detail::concat(make_caster<Ts>::name...)
+                                 + const_name("]");
+
+    template <typename T>
+    using cast_op_type = type;
+
+    explicit operator type() & { return implicit_cast(indices{}); }
+    explicit operator type() && { return std::move(*this).implicit_cast(indices{}); }
+
+protected:
+    template <size_t... Is>
+    type implicit_cast(index_sequence<Is...>) & {
+        return type(cast_op<Ts>(std::get<Is>(subcasters))...);
+    }
+    template <size_t... Is>
+    type implicit_cast(index_sequence<Is...>) && {
+        return type(cast_op<Ts>(std::move(std::get<Is>(subcasters)))...);
+    }
+
+    static constexpr bool load_impl(const sequence &, bool, index_sequence<>) { return true; }
+
+    template <size_t... Is>
+    bool load_impl(const sequence &seq, bool convert, index_sequence<Is...>) {
+#ifdef __cpp_fold_expressions
+        if ((... || !std::get<Is>(subcasters).load(seq[Is], convert))) {
+            return false;
+        }
+#else
+        for (bool r : {std::get<Is>(subcasters).load(seq[Is], convert)...}) {
+            if (!r) {
+                return false;
+            }
+        }
+#endif
+        return true;
+    }
+
+    /* Implementation: Convert a C++ tuple into a Python tuple */
+    template <typename T, size_t... Is>
+    static handle
+    cast_impl(T &&src, return_value_policy policy, handle parent, index_sequence<Is...>) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(src, policy, parent);
+        PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(policy, parent);
+        std::array<object, size> entries{{reinterpret_steal<object>(
+            make_caster<Ts>::cast(std::get<Is>(std::forward<T>(src)), policy, parent))...}};
+        for (const auto &entry : entries) {
+            if (!entry) {
+                return handle();
+            }
+        }
+        tuple result(size);
+        int counter = 0;
+        for (auto &entry : entries) {
+            PyTuple_SET_ITEM(result.ptr(), counter++, entry.release().ptr());
+        }
+        return result.release();
+    }
+
+    Tuple<make_caster<Ts>...> subcasters;
+};
+
+template <typename T1, typename T2>
+class type_caster<std::pair<T1, T2>> : public tuple_caster<std::pair, T1, T2> {};
+
+template <typename... Ts>
+class type_caster<std::tuple<Ts...>> : public tuple_caster<std::tuple, Ts...> {};
+
+template <>
+class type_caster<std::tuple<>> : public tuple_caster<std::tuple> {
+public:
+    // PEP 484 specifies this syntax for an empty tuple
+    static constexpr auto name = const_name("tuple[()]");
+};
+
+/// Helper class which abstracts away certain actions. Users can provide specializations for
+/// custom holders, but it's only necessary if the type has a non-standard interface.
+template <typename T>
+struct holder_helper {
+    static auto get(const T &p) -> decltype(p.get()) { return p.get(); }
+};
+
+/// Type caster for holder types like std::shared_ptr, etc.
+/// The SFINAE hook is provided to help work around the current lack of support
+/// for smart-pointer interoperability. Please consider it an implementation
+/// detail that may change in the future, as formal support for smart-pointer
+/// interoperability is added into pybind11.
+template <typename type, typename holder_type, typename SFINAE = void>
+struct copyable_holder_caster : public type_caster_base<type> {
+public:
+    using base = type_caster_base<type>;
+    static_assert(std::is_base_of<base, type_caster<type>>::value,
+                  "Holder classes are only supported for custom types");
+    using base::base;
+    using base::cast;
+    using base::typeinfo;
+    using base::value;
+
+    bool load(handle src, bool convert) {
+        return base::template load_impl<copyable_holder_caster<type, holder_type>>(src, convert);
+    }
+
+    explicit operator type *() { return this->value; }
+    // static_cast works around compiler error with MSVC 17 and CUDA 10.2
+    // see issue #2180
+    explicit operator type &() { return *(static_cast<type *>(this->value)); }
+    explicit operator holder_type *() { return std::addressof(holder); }
+    explicit operator holder_type &() { return holder; }
+
+    static handle cast(const holder_type &src, return_value_policy, handle) {
+        const auto *ptr = holder_helper<holder_type>::get(src);
+        return type_caster_base<type>::cast_holder(ptr, &src);
+    }
+
+protected:
+    friend class type_caster_generic;
+    void check_holder_compat() {
+        if (typeinfo->default_holder) {
+            throw cast_error("Unable to load a custom holder type from a default-holder instance");
+        }
+    }
+
+    void load_value(value_and_holder &&v_h) {
+        if (v_h.holder_constructed()) {
+            value = v_h.value_ptr();
+            holder = v_h.template holder<holder_type>();
+            return;
+        }
+        throw cast_error("Unable to cast from non-held to held instance (T& to Holder<T>) "
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                         "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for "
+                         "type information)");
+#else
+                         "of type '"
+                         + type_id<holder_type>() + "''");
+#endif
+    }
+
+    template <typename T = holder_type,
+              detail::enable_if_t<!std::is_constructible<T, const T &, type *>::value, int> = 0>
+    bool try_implicit_casts(handle, bool) {
+        return false;
+    }
+
+    template <typename T = holder_type,
+              detail::enable_if_t<std::is_constructible<T, const T &, type *>::value, int> = 0>
+    bool try_implicit_casts(handle src, bool convert) {
+        for (auto &cast : typeinfo->implicit_casts) {
+            copyable_holder_caster sub_caster(*cast.first);
+            if (sub_caster.load(src, convert)) {
+                value = cast.second(sub_caster.value);
+                holder = holder_type(sub_caster.holder, (type *) value);
+                return true;
+            }
+        }
+        return false;
+    }
+
+    static bool try_direct_conversions(handle) { return false; }
+
+    holder_type holder;
+};
+
+/// Specialize for the common std::shared_ptr, so users don't need to
+template <typename T>
+class type_caster<std::shared_ptr<T>> : public copyable_holder_caster<T, std::shared_ptr<T>> {};
+
+/// Type caster for holder types like std::unique_ptr.
+/// Please consider the SFINAE hook an implementation detail, as explained
+/// in the comment for the copyable_holder_caster.
+template <typename type, typename holder_type, typename SFINAE = void>
+struct move_only_holder_caster {
+    static_assert(std::is_base_of<type_caster_base<type>, type_caster<type>>::value,
+                  "Holder classes are only supported for custom types");
+
+    static handle cast(holder_type &&src, return_value_policy, handle) {
+        auto *ptr = holder_helper<holder_type>::get(src);
+        return type_caster_base<type>::cast_holder(ptr, std::addressof(src));
+    }
+    static constexpr auto name = type_caster_base<type>::name;
+};
+
+template <typename type, typename deleter>
+class type_caster<std::unique_ptr<type, deleter>>
+    : public move_only_holder_caster<type, std::unique_ptr<type, deleter>> {};
+
+template <typename type, typename holder_type>
+using type_caster_holder = conditional_t<is_copy_constructible<holder_type>::value,
+                                         copyable_holder_caster<type, holder_type>,
+                                         move_only_holder_caster<type, holder_type>>;
+
+template <typename T, bool Value = false>
+struct always_construct_holder {
+    static constexpr bool value = Value;
+};
+
+/// Create a specialization for custom holder types (silently ignores std::shared_ptr)
+#define PYBIND11_DECLARE_HOLDER_TYPE(type, holder_type, ...)                                      \
+    PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)                                                  \
+    namespace detail {                                                                            \
+    template <typename type>                                                                      \
+    struct always_construct_holder<holder_type> : always_construct_holder<void, ##__VA_ARGS__> {  \
+    };                                                                                            \
+    template <typename type>                                                                      \
+    class type_caster<holder_type, enable_if_t<!is_shared_ptr<holder_type>::value>>               \
+        : public type_caster_holder<type, holder_type> {};                                        \
+    }                                                                                             \
+    PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
+
+// PYBIND11_DECLARE_HOLDER_TYPE holder types:
+template <typename base, typename holder>
+struct is_holder_type
+    : std::is_base_of<detail::type_caster_holder<base, holder>, detail::type_caster<holder>> {};
+// Specialization for always-supported unique_ptr holders:
+template <typename base, typename deleter>
+struct is_holder_type<base, std::unique_ptr<base, deleter>> : std::true_type {};
+
+#ifdef PYBIND11_DISABLE_HANDLE_TYPE_NAME_DEFAULT_IMPLEMENTATION // See PR #4888
+
+// This leads to compilation errors if a specialization is missing.
+template <typename T>
+struct handle_type_name;
+
+#else
+
+template <typename T>
+struct handle_type_name {
+    static constexpr auto name = const_name<T>();
+};
+
+#endif
+
+template <>
+struct handle_type_name<object> {
+    static constexpr auto name = const_name("object");
+};
+template <>
+struct handle_type_name<list> {
+    static constexpr auto name = const_name("list");
+};
+template <>
+struct handle_type_name<dict> {
+    static constexpr auto name = const_name("dict");
+};
+template <>
+struct handle_type_name<anyset> {
+    static constexpr auto name = const_name("Union[set, frozenset]");
+};
+template <>
+struct handle_type_name<set> {
+    static constexpr auto name = const_name("set");
+};
+template <>
+struct handle_type_name<frozenset> {
+    static constexpr auto name = const_name("frozenset");
+};
+template <>
+struct handle_type_name<str> {
+    static constexpr auto name = const_name("str");
+};
+template <>
+struct handle_type_name<tuple> {
+    static constexpr auto name = const_name("tuple");
+};
+template <>
+struct handle_type_name<bool_> {
+    static constexpr auto name = const_name("bool");
+};
+template <>
+struct handle_type_name<bytes> {
+    static constexpr auto name = const_name(PYBIND11_BYTES_NAME);
+};
+template <>
+struct handle_type_name<buffer> {
+    static constexpr auto name = const_name("Buffer");
+};
+template <>
+struct handle_type_name<int_> {
+    static constexpr auto name = const_name("int");
+};
+template <>
+struct handle_type_name<iterable> {
+    static constexpr auto name = const_name("Iterable");
+};
+template <>
+struct handle_type_name<iterator> {
+    static constexpr auto name = const_name("Iterator");
+};
+template <>
+struct handle_type_name<float_> {
+    static constexpr auto name = const_name("float");
+};
+template <>
+struct handle_type_name<function> {
+    static constexpr auto name = const_name("Callable");
+};
+template <>
+struct handle_type_name<handle> {
+    static constexpr auto name = handle_type_name<object>::name;
+};
+template <>
+struct handle_type_name<none> {
+    static constexpr auto name = const_name("None");
+};
+template <>
+struct handle_type_name<sequence> {
+    static constexpr auto name = const_name("Sequence");
+};
+template <>
+struct handle_type_name<bytearray> {
+    static constexpr auto name = const_name("bytearray");
+};
+template <>
+struct handle_type_name<memoryview> {
+    static constexpr auto name = const_name("memoryview");
+};
+template <>
+struct handle_type_name<slice> {
+    static constexpr auto name = const_name("slice");
+};
+template <>
+struct handle_type_name<type> {
+    static constexpr auto name = const_name("type");
+};
+template <>
+struct handle_type_name<capsule> {
+    static constexpr auto name = const_name("capsule");
+};
+template <>
+struct handle_type_name<ellipsis> {
+    static constexpr auto name = const_name("ellipsis");
+};
+template <>
+struct handle_type_name<weakref> {
+    static constexpr auto name = const_name("weakref");
+};
+template <>
+struct handle_type_name<args> {
+    static constexpr auto name = const_name("*args");
+};
+template <>
+struct handle_type_name<kwargs> {
+    static constexpr auto name = const_name("**kwargs");
+};
+template <>
+struct handle_type_name<obj_attr_accessor> {
+    static constexpr auto name = const_name<obj_attr_accessor>();
+};
+template <>
+struct handle_type_name<str_attr_accessor> {
+    static constexpr auto name = const_name<str_attr_accessor>();
+};
+template <>
+struct handle_type_name<item_accessor> {
+    static constexpr auto name = const_name<item_accessor>();
+};
+template <>
+struct handle_type_name<sequence_accessor> {
+    static constexpr auto name = const_name<sequence_accessor>();
+};
+template <>
+struct handle_type_name<list_accessor> {
+    static constexpr auto name = const_name<list_accessor>();
+};
+template <>
+struct handle_type_name<tuple_accessor> {
+    static constexpr auto name = const_name<tuple_accessor>();
+};
+
+template <typename type>
+struct pyobject_caster {
+    template <typename T = type, enable_if_t<std::is_same<T, handle>::value, int> = 0>
+    pyobject_caster() : value() {}
+
+    // `type` may not be default constructible (e.g. frozenset, anyset).  Initializing `value`
+    // to a nil handle is safe since it will only be accessed if `load` succeeds.
+    template <typename T = type, enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+    pyobject_caster() : value(reinterpret_steal<type>(handle())) {}
+
+    template <typename T = type, enable_if_t<std::is_same<T, handle>::value, int> = 0>
+    bool load(handle src, bool /* convert */) {
+        value = src;
+        return static_cast<bool>(value);
+    }
+
+    template <typename T = type, enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+    bool load(handle src, bool /* convert */) {
+        if (!isinstance<type>(src)) {
+            return false;
+        }
+        value = reinterpret_borrow<type>(src);
+        return true;
+    }
+
+    static handle cast(const handle &src, return_value_policy /* policy */, handle /* parent */) {
+        return src.inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name);
+};
+
+template <typename T>
+class type_caster<T, enable_if_t<is_pyobject<T>::value>> : public pyobject_caster<T> {};
+
+// Our conditions for enabling moving are quite restrictive:
+// At compile time:
+// - T needs to be a non-const, non-pointer, non-reference type
+// - type_caster<T>::operator T&() must exist
+// - the type must be move constructible (obviously)
+// At run-time:
+// - if the type is non-copy-constructible, the object must be the sole owner of the type (i.e. it
+//   must have ref_count() == 1)h
+// If any of the above are not satisfied, we fall back to copying.
+template <typename T>
+using move_is_plain_type
+    = satisfies_none_of<T, std::is_void, std::is_pointer, std::is_reference, std::is_const>;
+template <typename T, typename SFINAE = void>
+struct move_always : std::false_type {};
+template <typename T>
+struct move_always<
+    T,
+    enable_if_t<
+        all_of<move_is_plain_type<T>,
+               negation<is_copy_constructible<T>>,
+               is_move_constructible<T>,
+               std::is_same<decltype(std::declval<make_caster<T>>().operator T &()), T &>>::value>>
+    : std::true_type {};
+template <typename T, typename SFINAE = void>
+struct move_if_unreferenced : std::false_type {};
+template <typename T>
+struct move_if_unreferenced<
+    T,
+    enable_if_t<
+        all_of<move_is_plain_type<T>,
+               negation<move_always<T>>,
+               is_move_constructible<T>,
+               std::is_same<decltype(std::declval<make_caster<T>>().operator T &()), T &>>::value>>
+    : std::true_type {};
+template <typename T>
+using move_never = none_of<move_always<T>, move_if_unreferenced<T>>;
+
+// Detect whether returning a `type` from a cast on type's type_caster is going to result in a
+// reference or pointer to a local variable of the type_caster.  Basically, only
+// non-reference/pointer `type`s and reference/pointers from a type_caster_generic are safe;
+// everything else returns a reference/pointer to a local variable.
+template <typename type>
+using cast_is_temporary_value_reference
+    = bool_constant<(std::is_reference<type>::value || std::is_pointer<type>::value)
+                    && !std::is_base_of<type_caster_generic, make_caster<type>>::value
+                    && !std::is_same<intrinsic_t<type>, void>::value>;
+
+// When a value returned from a C++ function is being cast back to Python, we almost always want to
+// force `policy = move`, regardless of the return value policy the function/method was declared
+// with.
+template <typename Return, typename SFINAE = void>
+struct return_value_policy_override {
+    static return_value_policy policy(return_value_policy p) { return p; }
+};
+
+template <typename Return>
+struct return_value_policy_override<
+    Return,
+    detail::enable_if_t<std::is_base_of<type_caster_generic, make_caster<Return>>::value, void>> {
+    static return_value_policy policy(return_value_policy p) {
+        return !std::is_lvalue_reference<Return>::value && !std::is_pointer<Return>::value
+                   ? return_value_policy::move
+                   : p;
+    }
+};
+
+// Basic python -> C++ casting; throws if casting fails
+template <typename T, typename SFINAE>
+type_caster<T, SFINAE> &load_type(type_caster<T, SFINAE> &conv, const handle &handle) {
+    static_assert(!detail::is_pyobject<T>::value,
+                  "Internal error: type_caster should only be used for C++ types");
+    if (!conv.load(handle, true)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        throw cast_error(
+            "Unable to cast Python instance of type "
+            + str(type::handle_of(handle)).cast<std::string>()
+            + " to C++ type '?' (#define "
+              "PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+#else
+        throw cast_error("Unable to cast Python instance of type "
+                         + str(type::handle_of(handle)).cast<std::string>() + " to C++ type '"
+                         + type_id<T>() + "'");
+#endif
+    }
+    return conv;
+}
+// Wrapper around the above that also constructs and returns a type_caster
+template <typename T>
+make_caster<T> load_type(const handle &handle) {
+    make_caster<T> conv;
+    load_type(conv, handle);
+    return conv;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+// pytype -> C++ type
+template <typename T,
+          detail::enable_if_t<!detail::is_pyobject<T>::value
+                                  && !detail::is_same_ignoring_cvref<T, PyObject *>::value,
+                              int>
+          = 0>
+T cast(const handle &handle) {
+    using namespace detail;
+    static_assert(!cast_is_temporary_value_reference<T>::value,
+                  "Unable to cast type to reference: value is local to type caster");
+    return cast_op<T>(load_type<T>(handle));
+}
+
+// pytype -> pytype (calls converting constructor)
+template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+T cast(const handle &handle) {
+    return T(reinterpret_borrow<object>(handle));
+}
+
+// Note that `cast<PyObject *>(obj)` increments the reference count of `obj`.
+// This is necessary for the case that `obj` is a temporary, and could
+// not possibly be different, given
+// 1. the established convention that the passed `handle` is borrowed, and
+// 2. we don't want to force all generic code using `cast<T>()` to special-case
+//    handling of `T` = `PyObject *` (to increment the reference count there).
+// It is the responsibility of the caller to ensure that the reference count
+// is decremented.
+template <typename T,
+          typename Handle,
+          detail::enable_if_t<detail::is_same_ignoring_cvref<T, PyObject *>::value
+                                  && detail::is_same_ignoring_cvref<Handle, handle>::value,
+                              int>
+          = 0>
+T cast(Handle &&handle) {
+    return handle.inc_ref().ptr();
+}
+// To optimize way an inc_ref/dec_ref cycle:
+template <typename T,
+          typename Object,
+          detail::enable_if_t<detail::is_same_ignoring_cvref<T, PyObject *>::value
+                                  && detail::is_same_ignoring_cvref<Object, object>::value,
+                              int>
+          = 0>
+T cast(Object &&obj) {
+    return obj.release().ptr();
+}
+
+// C++ type -> py::object
+template <typename T, detail::enable_if_t<!detail::is_pyobject<T>::value, int> = 0>
+object cast(T &&value,
+            return_value_policy policy = return_value_policy::automatic_reference,
+            handle parent = handle()) {
+    using no_ref_T = typename std::remove_reference<T>::type;
+    if (policy == return_value_policy::automatic) {
+        policy = std::is_pointer<no_ref_T>::value     ? return_value_policy::take_ownership
+                 : std::is_lvalue_reference<T>::value ? return_value_policy::copy
+                                                      : return_value_policy::move;
+    } else if (policy == return_value_policy::automatic_reference) {
+        policy = std::is_pointer<no_ref_T>::value     ? return_value_policy::reference
+                 : std::is_lvalue_reference<T>::value ? return_value_policy::copy
+                                                      : return_value_policy::move;
+    }
+    return reinterpret_steal<object>(
+        detail::make_caster<T>::cast(std::forward<T>(value), policy, parent));
+}
+
+template <typename T>
+T handle::cast() const {
+    return pybind11::cast<T>(*this);
+}
+template <>
+inline void handle::cast() const {
+    return;
+}
+
+template <typename T>
+detail::enable_if_t<!detail::move_never<T>::value, T> move(object &&obj) {
+    if (obj.ref_count() > 1) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        throw cast_error(
+            "Unable to cast Python " + str(type::handle_of(obj)).cast<std::string>()
+            + " instance to C++ rvalue: instance has multiple references"
+              " (#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+#else
+        throw cast_error("Unable to move from Python "
+                         + str(type::handle_of(obj)).cast<std::string>() + " instance to C++ "
+                         + type_id<T>() + " instance: instance has multiple references");
+#endif
+    }
+
+    // Move into a temporary and return that, because the reference may be a local value of `conv`
+    T ret = std::move(detail::load_type<T>(obj).operator T &());
+    return ret;
+}
+
+// Calling cast() on an rvalue calls pybind11::cast with the object rvalue, which does:
+// - If we have to move (because T has no copy constructor), do it.  This will fail if the moved
+//   object has multiple references, but trying to copy will fail to compile.
+// - If both movable and copyable, check ref count: if 1, move; otherwise copy
+// - Otherwise (not movable), copy.
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_always<T>::value, T>
+cast(object &&object) {
+    return move<T>(std::move(object));
+}
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_if_unreferenced<T>::value, T>
+cast(object &&object) {
+    if (object.ref_count() > 1) {
+        return cast<T>(object);
+    }
+    return move<T>(std::move(object));
+}
+template <typename T>
+detail::enable_if_t<!detail::is_pyobject<T>::value && detail::move_never<T>::value, T>
+cast(object &&object) {
+    return cast<T>(object);
+}
+
+// pytype rvalue -> pytype (calls converting constructor)
+template <typename T>
+detail::enable_if_t<detail::is_pyobject<T>::value, T> cast(object &&object) {
+    return T(std::move(object));
+}
+
+template <typename T>
+T object::cast() const & {
+    return pybind11::cast<T>(*this);
+}
+template <typename T>
+T object::cast() && {
+    return pybind11::cast<T>(std::move(*this));
+}
+template <>
+inline void object::cast() const & {
+    return;
+}
+template <>
+inline void object::cast() && {
+    return;
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Declared in pytypes.h:
+template <typename T, enable_if_t<!is_pyobject<T>::value, int>>
+object object_or_cast(T &&o) {
+    return pybind11::cast(std::forward<T>(o));
+}
+
+// Placeholder type for the unneeded (and dead code) static variable in the
+// PYBIND11_OVERRIDE_OVERRIDE macro
+struct override_unused {};
+template <typename ret_type>
+using override_caster_t = conditional_t<cast_is_temporary_value_reference<ret_type>::value,
+                                        make_caster<ret_type>,
+                                        override_unused>;
+
+// Trampoline use: for reference/pointer types to value-converted values, we do a value cast, then
+// store the result in the given variable.  For other types, this is a no-op.
+template <typename T>
+enable_if_t<cast_is_temporary_value_reference<T>::value, T> cast_ref(object &&o,
+                                                                     make_caster<T> &caster) {
+    return cast_op<T>(load_type(caster, o));
+}
+template <typename T>
+enable_if_t<!cast_is_temporary_value_reference<T>::value, T> cast_ref(object &&,
+                                                                      override_unused &) {
+    pybind11_fail("Internal error: cast_ref fallback invoked");
+}
+
+// Trampoline use: Having a pybind11::cast with an invalid reference type is going to
+// static_assert, even though if it's in dead code, so we provide a "trampoline" to pybind11::cast
+// that only does anything in cases where pybind11::cast is valid.
+template <typename T>
+enable_if_t<cast_is_temporary_value_reference<T>::value
+                && !detail::is_same_ignoring_cvref<T, PyObject *>::value,
+            T>
+cast_safe(object &&) {
+    pybind11_fail("Internal error: cast_safe fallback invoked");
+}
+template <typename T>
+enable_if_t<std::is_void<T>::value, void> cast_safe(object &&) {}
+template <typename T>
+enable_if_t<detail::is_same_ignoring_cvref<T, PyObject *>::value, PyObject *>
+cast_safe(object &&o) {
+    return o.release().ptr();
+}
+template <typename T>
+enable_if_t<detail::none_of<cast_is_temporary_value_reference<T>,
+                            detail::is_same_ignoring_cvref<T, PyObject *>,
+                            std::is_void<T>>::value,
+            T>
+cast_safe(object &&o) {
+    return pybind11::cast<T>(std::move(o));
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+// The overloads could coexist, i.e. the #if is not strictly speaking needed,
+// but it is an easy minor optimization.
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+inline cast_error cast_error_unable_to_convert_call_arg(const std::string &name) {
+    return cast_error("Unable to convert call argument '" + name
+                      + "' to Python object (#define "
+                        "PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+}
+#else
+inline cast_error cast_error_unable_to_convert_call_arg(const std::string &name,
+                                                        const std::string &type) {
+    return cast_error("Unable to convert call argument '" + name + "' of type '" + type
+                      + "' to Python object");
+}
+#endif
+
+template <return_value_policy policy = return_value_policy::automatic_reference>
+tuple make_tuple() {
+    return tuple(0);
+}
+
+template <return_value_policy policy = return_value_policy::automatic_reference, typename... Args>
+tuple make_tuple(Args &&...args_) {
+    constexpr size_t size = sizeof...(Args);
+    std::array<object, size> args{{reinterpret_steal<object>(
+        detail::make_caster<Args>::cast(std::forward<Args>(args_), policy, nullptr))...}};
+    for (size_t i = 0; i < args.size(); i++) {
+        if (!args[i]) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg(std::to_string(i));
+#else
+            std::array<std::string, size> argtypes{{type_id<Args>()...}};
+            throw cast_error_unable_to_convert_call_arg(std::to_string(i), argtypes[i]);
+#endif
+        }
+    }
+    tuple result(size);
+    int counter = 0;
+    for (auto &arg_value : args) {
+        PyTuple_SET_ITEM(result.ptr(), counter++, arg_value.release().ptr());
+    }
+    return result;
+}
+
+/// \ingroup annotations
+/// Annotation for arguments
+struct arg {
+    /// Constructs an argument with the name of the argument; if null or omitted, this is a
+    /// positional argument.
+    constexpr explicit arg(const char *name = nullptr)
+        : name(name), flag_noconvert(false), flag_none(true) {}
+    /// Assign a value to this argument
+    template <typename T>
+    arg_v operator=(T &&value) const;
+    /// Indicate that the type should not be converted in the type caster
+    arg &noconvert(bool flag = true) {
+        flag_noconvert = flag;
+        return *this;
+    }
+    /// Indicates that the argument should/shouldn't allow None (e.g. for nullable pointer args)
+    arg &none(bool flag = true) {
+        flag_none = flag;
+        return *this;
+    }
+
+    const char *name;        ///< If non-null, this is a named kwargs argument
+    bool flag_noconvert : 1; ///< If set, do not allow conversion (requires a supporting type
+                             ///< caster!)
+    bool flag_none : 1;      ///< If set (the default), allow None to be passed to this argument
+};
+
+/// \ingroup annotations
+/// Annotation for arguments with values
+struct arg_v : arg {
+private:
+    template <typename T>
+    arg_v(arg &&base, T &&x, const char *descr = nullptr)
+        : arg(base), value(reinterpret_steal<object>(detail::make_caster<T>::cast(
+                         std::forward<T>(x), return_value_policy::automatic, {}))),
+          descr(descr)
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+          ,
+          type(type_id<T>())
+#endif
+    {
+        // Workaround! See:
+        // https://github.com/pybind/pybind11/issues/2336
+        // https://github.com/pybind/pybind11/pull/2685#issuecomment-731286700
+        if (PyErr_Occurred()) {
+            PyErr_Clear();
+        }
+    }
+
+public:
+    /// Direct construction with name, default, and description
+    template <typename T>
+    arg_v(const char *name, T &&x, const char *descr = nullptr)
+        : arg_v(arg(name), std::forward<T>(x), descr) {}
+
+    /// Called internally when invoking `py::arg("a") = value`
+    template <typename T>
+    arg_v(const arg &base, T &&x, const char *descr = nullptr)
+        : arg_v(arg(base), std::forward<T>(x), descr) {}
+
+    /// Same as `arg::noconvert()`, but returns *this as arg_v&, not arg&
+    arg_v &noconvert(bool flag = true) {
+        arg::noconvert(flag);
+        return *this;
+    }
+
+    /// Same as `arg::nonone()`, but returns *this as arg_v&, not arg&
+    arg_v &none(bool flag = true) {
+        arg::none(flag);
+        return *this;
+    }
+
+    /// The default value
+    object value;
+    /// The (optional) description of the default value
+    const char *descr;
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    /// The C++ type name of the default value (only available when compiled in debug mode)
+    std::string type;
+#endif
+};
+
+/// \ingroup annotations
+/// Annotation indicating that all following arguments are keyword-only; the is the equivalent of
+/// an unnamed '*' argument
+struct kw_only {};
+
+/// \ingroup annotations
+/// Annotation indicating that all previous arguments are positional-only; the is the equivalent of
+/// an unnamed '/' argument (in Python 3.8)
+struct pos_only {};
+
+template <typename T>
+arg_v arg::operator=(T &&value) const {
+    return {*this, std::forward<T>(value)};
+}
+
+/// Alias for backward compatibility -- to be removed in version 2.0
+template <typename /*unused*/>
+using arg_t = arg_v;
+
+inline namespace literals {
+/** \rst
+    String literal version of `arg`
+ \endrst */
+constexpr arg
+#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ < 5
+operator"" _a // gcc 4.8.5 insists on having a space (hard error).
+#else
+operator""_a // clang 17 generates a deprecation warning if there is a space.
+#endif
+    (const char *name, size_t) {
+    return arg(name);
+}
+} // namespace literals
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T>
+using is_kw_only = std::is_same<intrinsic_t<T>, kw_only>;
+template <typename T>
+using is_pos_only = std::is_same<intrinsic_t<T>, pos_only>;
+
+// forward declaration (definition in attr.h)
+struct function_record;
+
+/// Internal data associated with a single function call
+struct function_call {
+    function_call(const function_record &f, handle p); // Implementation in attr.h
+
+    /// The function data:
+    const function_record &func;
+
+    /// Arguments passed to the function:
+    std::vector<handle> args;
+
+    /// The `convert` value the arguments should be loaded with
+    std::vector<bool> args_convert;
+
+    /// Extra references for the optional `py::args` and/or `py::kwargs` arguments (which, if
+    /// present, are also in `args` but without a reference).
+    object args_ref, kwargs_ref;
+
+    /// The parent, if any
+    handle parent;
+
+    /// If this is a call to an initializer, this argument contains `self`
+    handle init_self;
+};
+
+/// Helper class which loads arguments for C++ functions called from Python
+template <typename... Args>
+class argument_loader {
+    using indices = make_index_sequence<sizeof...(Args)>;
+
+    template <typename Arg>
+    using argument_is_args = std::is_same<intrinsic_t<Arg>, args>;
+    template <typename Arg>
+    using argument_is_kwargs = std::is_same<intrinsic_t<Arg>, kwargs>;
+    // Get kwargs argument position, or -1 if not present:
+    static constexpr auto kwargs_pos = constexpr_last<argument_is_kwargs, Args...>();
+
+    static_assert(kwargs_pos == -1 || kwargs_pos == (int) sizeof...(Args) - 1,
+                  "py::kwargs is only permitted as the last argument of a function");
+
+public:
+    static constexpr bool has_kwargs = kwargs_pos != -1;
+
+    // py::args argument position; -1 if not present.
+    static constexpr int args_pos = constexpr_last<argument_is_args, Args...>();
+
+    static_assert(args_pos == -1 || args_pos == constexpr_first<argument_is_args, Args...>(),
+                  "py::args cannot be specified more than once");
+
+    static constexpr auto arg_names
+        = ::pybind11::detail::concat(type_descr(make_caster<Args>::name)...);
+
+    bool load_args(function_call &call) { return load_impl_sequence(call, indices{}); }
+
+    template <typename Return, typename Guard, typename Func>
+    // NOLINTNEXTLINE(readability-const-return-type)
+    enable_if_t<!std::is_void<Return>::value, Return> call(Func &&f) && {
+        return std::move(*this).template call_impl<remove_cv_t<Return>>(
+            std::forward<Func>(f), indices{}, Guard{});
+    }
+
+    template <typename Return, typename Guard, typename Func>
+    enable_if_t<std::is_void<Return>::value, void_type> call(Func &&f) && {
+        std::move(*this).template call_impl<remove_cv_t<Return>>(
+            std::forward<Func>(f), indices{}, Guard{});
+        return void_type();
+    }
+
+private:
+    static bool load_impl_sequence(function_call &, index_sequence<>) { return true; }
+
+    template <size_t... Is>
+    bool load_impl_sequence(function_call &call, index_sequence<Is...>) {
+#ifdef __cpp_fold_expressions
+        if ((... || !std::get<Is>(argcasters).load(call.args[Is], call.args_convert[Is]))) {
+            return false;
+        }
+#else
+        for (bool r : {std::get<Is>(argcasters).load(call.args[Is], call.args_convert[Is])...}) {
+            if (!r) {
+                return false;
+            }
+        }
+#endif
+        return true;
+    }
+
+    template <typename Return, typename Func, size_t... Is, typename Guard>
+    Return call_impl(Func &&f, index_sequence<Is...>, Guard &&) && {
+        return std::forward<Func>(f)(cast_op<Args>(std::move(std::get<Is>(argcasters)))...);
+    }
+
+    std::tuple<make_caster<Args>...> argcasters;
+};
+
+/// Helper class which collects only positional arguments for a Python function call.
+/// A fancier version below can collect any argument, but this one is optimal for simple calls.
+template <return_value_policy policy>
+class simple_collector {
+public:
+    template <typename... Ts>
+    explicit simple_collector(Ts &&...values)
+        : m_args(pybind11::make_tuple<policy>(std::forward<Ts>(values)...)) {}
+
+    const tuple &args() const & { return m_args; }
+    dict kwargs() const { return {}; }
+
+    tuple args() && { return std::move(m_args); }
+
+    /// Call a Python function and pass the collected arguments
+    object call(PyObject *ptr) const {
+        PyObject *result = PyObject_CallObject(ptr, m_args.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+private:
+    tuple m_args;
+};
+
+/// Helper class which collects positional, keyword, * and ** arguments for a Python function call
+template <return_value_policy policy>
+class unpacking_collector {
+public:
+    template <typename... Ts>
+    explicit unpacking_collector(Ts &&...values) {
+        // Tuples aren't (easily) resizable so a list is needed for collection,
+        // but the actual function call strictly requires a tuple.
+        auto args_list = list();
+        using expander = int[];
+        (void) expander{0, (process(args_list, std::forward<Ts>(values)), 0)...};
+
+        m_args = std::move(args_list);
+    }
+
+    const tuple &args() const & { return m_args; }
+    const dict &kwargs() const & { return m_kwargs; }
+
+    tuple args() && { return std::move(m_args); }
+    dict kwargs() && { return std::move(m_kwargs); }
+
+    /// Call a Python function and pass the collected arguments
+    object call(PyObject *ptr) const {
+        PyObject *result = PyObject_Call(ptr, m_args.ptr(), m_kwargs.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+private:
+    template <typename T>
+    void process(list &args_list, T &&x) {
+        auto o = reinterpret_steal<object>(
+            detail::make_caster<T>::cast(std::forward<T>(x), policy, {}));
+        if (!o) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg(std::to_string(args_list.size()));
+#else
+            throw cast_error_unable_to_convert_call_arg(std::to_string(args_list.size()),
+                                                        type_id<T>());
+#endif
+        }
+        args_list.append(std::move(o));
+    }
+
+    void process(list &args_list, detail::args_proxy ap) {
+        for (auto a : ap) {
+            args_list.append(a);
+        }
+    }
+
+    void process(list & /*args_list*/, arg_v a) {
+        if (!a.name) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            nameless_argument_error();
+#else
+            nameless_argument_error(a.type);
+#endif
+        }
+        if (m_kwargs.contains(a.name)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            multiple_values_error();
+#else
+            multiple_values_error(a.name);
+#endif
+        }
+        if (!a.value) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            throw cast_error_unable_to_convert_call_arg(a.name);
+#else
+            throw cast_error_unable_to_convert_call_arg(a.name, a.type);
+#endif
+        }
+        m_kwargs[a.name] = std::move(a.value);
+    }
+
+    void process(list & /*args_list*/, detail::kwargs_proxy kp) {
+        if (!kp) {
+            return;
+        }
+        for (auto k : reinterpret_borrow<dict>(kp)) {
+            if (m_kwargs.contains(k.first)) {
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                multiple_values_error();
+#else
+                multiple_values_error(str(k.first));
+#endif
+            }
+            m_kwargs[k.first] = k.second;
+        }
+    }
+
+    [[noreturn]] static void nameless_argument_error() {
+        throw type_error(
+            "Got kwargs without a name; only named arguments "
+            "may be passed via py::arg() to a python function call. "
+            "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+    }
+    [[noreturn]] static void nameless_argument_error(const std::string &type) {
+        throw type_error("Got kwargs without a name of type '" + type
+                         + "'; only named "
+                           "arguments may be passed via py::arg() to a python function call. ");
+    }
+    [[noreturn]] static void multiple_values_error() {
+        throw type_error(
+            "Got multiple values for keyword argument "
+            "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for details)");
+    }
+
+    [[noreturn]] static void multiple_values_error(const std::string &name) {
+        throw type_error("Got multiple values for keyword argument '" + name + "'");
+    }
+
+private:
+    tuple m_args;
+    dict m_kwargs;
+};
+
+// [workaround(intel)] Separate function required here
+// We need to put this into a separate function because the Intel compiler
+// fails to compile enable_if_t<!all_of<is_positional<Args>...>::value>
+// (tested with ICC 2021.1 Beta 20200827).
+template <typename... Args>
+constexpr bool args_are_all_positional() {
+    return all_of<is_positional<Args>...>::value;
+}
+
+/// Collect only positional arguments for a Python function call
+template <return_value_policy policy,
+          typename... Args,
+          typename = enable_if_t<args_are_all_positional<Args...>()>>
+simple_collector<policy> collect_arguments(Args &&...args) {
+    return simple_collector<policy>(std::forward<Args>(args)...);
+}
+
+/// Collect all arguments, including keywords and unpacking (only instantiated when needed)
+template <return_value_policy policy,
+          typename... Args,
+          typename = enable_if_t<!args_are_all_positional<Args...>()>>
+unpacking_collector<policy> collect_arguments(Args &&...args) {
+    // Following argument order rules for generalized unpacking according to PEP 448
+    static_assert(constexpr_last<is_positional, Args...>()
+                          < constexpr_first<is_keyword_or_ds, Args...>()
+                      && constexpr_last<is_s_unpacking, Args...>()
+                             < constexpr_first<is_ds_unpacking, Args...>(),
+                  "Invalid function call: positional args must precede keywords and ** unpacking; "
+                  "* unpacking must precede ** unpacking");
+    return unpacking_collector<policy>(std::forward<Args>(args)...);
+}
+
+template <typename Derived>
+template <return_value_policy policy, typename... Args>
+object object_api<Derived>::operator()(Args &&...args) const {
+#ifndef NDEBUG
+    if (!PyGILState_Check()) {
+        pybind11_fail("pybind11::object_api<>::operator() PyGILState_Check() failure.");
+    }
+#endif
+    return detail::collect_arguments<policy>(std::forward<Args>(args)...).call(derived().ptr());
+}
+
+template <typename Derived>
+template <return_value_policy policy, typename... Args>
+object object_api<Derived>::call(Args &&...args) const {
+    return operator()<policy>(std::forward<Args>(args)...);
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename T>
+handle type::handle_of() {
+    static_assert(std::is_base_of<detail::type_caster_generic, detail::make_caster<T>>::value,
+                  "py::type::of<T> only supports the case where T is a registered C++ types.");
+
+    return detail::get_type_handle(typeid(T), true);
+}
+
+#define PYBIND11_MAKE_OPAQUE(...)                                                                 \
+    PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)                                                  \
+    namespace detail {                                                                            \
+    template <>                                                                                   \
+    class type_caster<__VA_ARGS__> : public type_caster_base<__VA_ARGS__> {};                     \
+    }                                                                                             \
+    PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
+
+/// Lets you pass a type containing a `,` through a macro parameter without needing a separate
+/// typedef, e.g.:
+/// `PYBIND11_OVERRIDE(PYBIND11_TYPE(ReturnType<A, B>), PYBIND11_TYPE(Parent<C, D>), f, arg)`
+#define PYBIND11_TYPE(...) __VA_ARGS__
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/chrono.h b/lib/python3.11/site-packages/torch/include/pybind11/chrono.h
new file mode 100644
index 0000000000000000000000000000000000000000..167ea0e3d1057c54a469c8ecd656d49eec495fe7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/chrono.h
@@ -0,0 +1,225 @@
+/*
+    pybind11/chrono.h: Transparent conversion between std::chrono and python's datetime
+
+    Copyright (c) 2016 Trent Houliston <trent@houliston.me> and
+                       Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <chrono>
+#include <cmath>
+#include <ctime>
+#include <datetime.h>
+#include <mutex>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename type>
+class duration_caster {
+public:
+    using rep = typename type::rep;
+    using period = typename type::period;
+
+    // signed 25 bits required by the standard.
+    using days = std::chrono::duration<int_least32_t, std::ratio<86400>>;
+
+    bool load(handle src, bool) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        if (!src) {
+            return false;
+        }
+        // If invoked with datetime.delta object
+        if (PyDelta_Check(src.ptr())) {
+            value = type(duration_cast<duration<rep, period>>(
+                days(PyDateTime_DELTA_GET_DAYS(src.ptr()))
+                + seconds(PyDateTime_DELTA_GET_SECONDS(src.ptr()))
+                + microseconds(PyDateTime_DELTA_GET_MICROSECONDS(src.ptr()))));
+            return true;
+        }
+        // If invoked with a float we assume it is seconds and convert
+        if (PyFloat_Check(src.ptr())) {
+            value = type(duration_cast<duration<rep, period>>(
+                duration<double>(PyFloat_AsDouble(src.ptr()))));
+            return true;
+        }
+        return false;
+    }
+
+    // If this is a duration just return it back
+    static const std::chrono::duration<rep, period> &
+    get_duration(const std::chrono::duration<rep, period> &src) {
+        return src;
+    }
+
+    // If this is a time_point get the time_since_epoch
+    template <typename Clock>
+    static std::chrono::duration<rep, period>
+    get_duration(const std::chrono::time_point<Clock, std::chrono::duration<rep, period>> &src) {
+        return src.time_since_epoch();
+    }
+
+    static handle cast(const type &src, return_value_policy /* policy */, handle /* parent */) {
+        using namespace std::chrono;
+
+        // Use overloaded function to get our duration from our source
+        // Works out if it is a duration or time_point and get the duration
+        auto d = get_duration(src);
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        // Declare these special duration types so the conversions happen with the correct
+        // primitive types (int)
+        using dd_t = duration<int, std::ratio<86400>>;
+        using ss_t = duration<int, std::ratio<1>>;
+        using us_t = duration<int, std::micro>;
+
+        auto dd = duration_cast<dd_t>(d);
+        auto subd = d - dd;
+        auto ss = duration_cast<ss_t>(subd);
+        auto us = duration_cast<us_t>(subd - ss);
+        return PyDelta_FromDSU(dd.count(), ss.count(), us.count());
+    }
+
+    PYBIND11_TYPE_CASTER(type, const_name("datetime.timedelta"));
+};
+
+inline std::tm *localtime_thread_safe(const std::time_t *time, std::tm *buf) {
+#if (defined(__STDC_LIB_EXT1__) && defined(__STDC_WANT_LIB_EXT1__)) || defined(_MSC_VER)
+    if (localtime_s(buf, time))
+        return nullptr;
+    return buf;
+#else
+    static std::mutex mtx;
+    std::lock_guard<std::mutex> lock(mtx);
+    std::tm *tm_ptr = std::localtime(time);
+    if (tm_ptr != nullptr) {
+        *buf = *tm_ptr;
+    }
+    return tm_ptr;
+#endif
+}
+
+// This is for casting times on the system clock into datetime.datetime instances
+template <typename Duration>
+class type_caster<std::chrono::time_point<std::chrono::system_clock, Duration>> {
+public:
+    using type = std::chrono::time_point<std::chrono::system_clock, Duration>;
+    bool load(handle src, bool) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        if (!src) {
+            return false;
+        }
+
+        std::tm cal;
+        microseconds msecs;
+
+        if (PyDateTime_Check(src.ptr())) {
+            cal.tm_sec = PyDateTime_DATE_GET_SECOND(src.ptr());
+            cal.tm_min = PyDateTime_DATE_GET_MINUTE(src.ptr());
+            cal.tm_hour = PyDateTime_DATE_GET_HOUR(src.ptr());
+            cal.tm_mday = PyDateTime_GET_DAY(src.ptr());
+            cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1;
+            cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900;
+            cal.tm_isdst = -1;
+            msecs = microseconds(PyDateTime_DATE_GET_MICROSECOND(src.ptr()));
+        } else if (PyDate_Check(src.ptr())) {
+            cal.tm_sec = 0;
+            cal.tm_min = 0;
+            cal.tm_hour = 0;
+            cal.tm_mday = PyDateTime_GET_DAY(src.ptr());
+            cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1;
+            cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900;
+            cal.tm_isdst = -1;
+            msecs = microseconds(0);
+        } else if (PyTime_Check(src.ptr())) {
+            cal.tm_sec = PyDateTime_TIME_GET_SECOND(src.ptr());
+            cal.tm_min = PyDateTime_TIME_GET_MINUTE(src.ptr());
+            cal.tm_hour = PyDateTime_TIME_GET_HOUR(src.ptr());
+            cal.tm_mday = 1;  // This date (day, month, year) = (1, 0, 70)
+            cal.tm_mon = 0;   // represents 1-Jan-1970, which is the first
+            cal.tm_year = 70; // earliest available date for Python's datetime
+            cal.tm_isdst = -1;
+            msecs = microseconds(PyDateTime_TIME_GET_MICROSECOND(src.ptr()));
+        } else {
+            return false;
+        }
+
+        value = time_point_cast<Duration>(system_clock::from_time_t(std::mktime(&cal)) + msecs);
+        return true;
+    }
+
+    static handle cast(const std::chrono::time_point<std::chrono::system_clock, Duration> &src,
+                       return_value_policy /* policy */,
+                       handle /* parent */) {
+        using namespace std::chrono;
+
+        // Lazy initialise the PyDateTime import
+        if (!PyDateTimeAPI) {
+            PyDateTime_IMPORT;
+        }
+
+        // Get out microseconds, and make sure they are positive, to avoid bug in eastern
+        // hemisphere time zones (cfr. https://github.com/pybind/pybind11/issues/2417)
+        using us_t = duration<int, std::micro>;
+        auto us = duration_cast<us_t>(src.time_since_epoch() % seconds(1));
+        if (us.count() < 0) {
+            us += seconds(1);
+        }
+
+        // Subtract microseconds BEFORE `system_clock::to_time_t`, because:
+        // > If std::time_t has lower precision, it is implementation-defined whether the value is
+        // rounded or truncated. (https://en.cppreference.com/w/cpp/chrono/system_clock/to_time_t)
+        std::time_t tt
+            = system_clock::to_time_t(time_point_cast<system_clock::duration>(src - us));
+
+        std::tm localtime;
+        std::tm *localtime_ptr = localtime_thread_safe(&tt, &localtime);
+        if (!localtime_ptr) {
+            throw cast_error("Unable to represent system_clock in local time");
+        }
+        return PyDateTime_FromDateAndTime(localtime.tm_year + 1900,
+                                          localtime.tm_mon + 1,
+                                          localtime.tm_mday,
+                                          localtime.tm_hour,
+                                          localtime.tm_min,
+                                          localtime.tm_sec,
+                                          us.count());
+    }
+    PYBIND11_TYPE_CASTER(type, const_name("datetime.datetime"));
+};
+
+// Other clocks that are not the system clock are not measured as datetime.datetime objects
+// since they are not measured on calendar time. So instead we just make them timedeltas
+// Or if they have passed us a time as a float we convert that
+template <typename Clock, typename Duration>
+class type_caster<std::chrono::time_point<Clock, Duration>>
+    : public duration_caster<std::chrono::time_point<Clock, Duration>> {};
+
+template <typename Rep, typename Period>
+class type_caster<std::chrono::duration<Rep, Period>>
+    : public duration_caster<std::chrono::duration<Rep, Period>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/common.h b/lib/python3.11/site-packages/torch/include/pybind11/common.h
new file mode 100644
index 0000000000000000000000000000000000000000..6c8a4f1e88e493ee08d24e668639c8d495fd49b1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/common.h
@@ -0,0 +1,2 @@
+#include "detail/common.h"
+#warning "Including 'common.h' is deprecated. It will be removed in v3.0. Use 'pybind11.h'."
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/complex.h b/lib/python3.11/site-packages/torch/include/pybind11/complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..8a831c12ce4307fa8f371caa4137ad3f278e81f7
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/complex.h
@@ -0,0 +1,74 @@
+/*
+    pybind11/complex.h: Complex number support
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <complex>
+
+/// glibc defines I as a macro which breaks things, e.g., boost template names
+#ifdef I
+#    undef I
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+template <typename T>
+struct format_descriptor<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr const char c = format_descriptor<T>::c;
+    static constexpr const char value[3] = {'Z', c, '\0'};
+    static std::string format() { return std::string(value); }
+};
+
+#ifndef PYBIND11_CPP17
+
+template <typename T>
+constexpr const char
+    format_descriptor<std::complex<T>,
+                      detail::enable_if_t<std::is_floating_point<T>::value>>::value[3];
+
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename T>
+struct is_fmt_numeric<std::complex<T>, detail::enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr bool value = true;
+    static constexpr int index = is_fmt_numeric<T>::index + 3;
+};
+
+template <typename T>
+class type_caster<std::complex<T>> {
+public:
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (!convert && !PyComplex_Check(src.ptr())) {
+            return false;
+        }
+        Py_complex result = PyComplex_AsCComplex(src.ptr());
+        if (result.real == -1.0 && PyErr_Occurred()) {
+            PyErr_Clear();
+            return false;
+        }
+        value = std::complex<T>((T) result.real, (T) result.imag);
+        return true;
+    }
+
+    static handle
+    cast(const std::complex<T> &src, return_value_policy /* policy */, handle /* parent */) {
+        return PyComplex_FromDoubles((double) src.real(), (double) src.imag());
+    }
+
+    PYBIND11_TYPE_CASTER(std::complex<T>, const_name("complex"));
+};
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/class.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/class.h
new file mode 100644
index 0000000000000000000000000000000000000000..b990507d629b4260d66d51e23a7f34a0fa465c9e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/class.h
@@ -0,0 +1,767 @@
+/*
+    pybind11/detail/class.h: Python C API implementation details for py::class_
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <pybind11/attr.h>
+#include <pybind11/options.h>
+
+#include "exception_translation.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if !defined(PYPY_VERSION)
+#    define PYBIND11_BUILTIN_QUALNAME
+#    define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj)
+#else
+// In PyPy, we still set __qualname__ so that we can produce reliable function type
+// signatures; in CPython this macro expands to nothing:
+#    define PYBIND11_SET_OLDPY_QUALNAME(obj, nameobj)                                             \
+        setattr((PyObject *) obj, "__qualname__", nameobj)
+#endif
+
+inline std::string get_fully_qualified_tp_name(PyTypeObject *type) {
+#if !defined(PYPY_VERSION)
+    return type->tp_name;
+#else
+    auto module_name = handle((PyObject *) type).attr("__module__").cast<std::string>();
+    if (module_name == PYBIND11_BUILTINS_MODULE)
+        return type->tp_name;
+    else
+        return std::move(module_name) + "." + type->tp_name;
+#endif
+}
+
+inline PyTypeObject *type_incref(PyTypeObject *type) {
+    Py_INCREF(type);
+    return type;
+}
+
+#if !defined(PYPY_VERSION)
+
+/// `pybind11_static_property.__get__()`: Always pass the class instead of the instance.
+extern "C" inline PyObject *pybind11_static_get(PyObject *self, PyObject * /*ob*/, PyObject *cls) {
+    return PyProperty_Type.tp_descr_get(self, cls, cls);
+}
+
+/// `pybind11_static_property.__set__()`: Just like the above `__get__()`.
+extern "C" inline int pybind11_static_set(PyObject *self, PyObject *obj, PyObject *value) {
+    PyObject *cls = PyType_Check(obj) ? obj : (PyObject *) Py_TYPE(obj);
+    return PyProperty_Type.tp_descr_set(self, cls, value);
+}
+
+// Forward declaration to use in `make_static_property_type()`
+inline void enable_dynamic_attributes(PyHeapTypeObject *heap_type);
+
+/** A `static_property` is the same as a `property` but the `__get__()` and `__set__()`
+    methods are modified to always use the object type instead of a concrete instance.
+    Return value: New reference. */
+inline PyTypeObject *make_static_property_type() {
+    constexpr auto *name = "pybind11_static_property";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
+    if (!heap_type) {
+        pybind11_fail("make_static_property_type(): error allocating type!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#    ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#    endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyProperty_Type);
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+    type->tp_descr_get = pybind11_static_get;
+    type->tp_descr_set = pybind11_static_set;
+
+#    if PY_VERSION_HEX >= 0x030C0000
+    // Since Python-3.12 property-derived types are required to
+    // have dynamic attributes (to set `__doc__`)
+    enable_dynamic_attributes(heap_type);
+#    endif
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("make_static_property_type(): failure in PyType_Ready()!");
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    return type;
+}
+
+#else // PYPY
+
+/** PyPy has some issues with the above C API, so we evaluate Python code instead.
+    This function will only be called once so performance isn't really a concern.
+    Return value: New reference. */
+inline PyTypeObject *make_static_property_type() {
+    auto d = dict();
+    PyObject *result = PyRun_String(R"(\
+class pybind11_static_property(property):
+    def __get__(self, obj, cls):
+        return property.__get__(self, cls, cls)
+
+    def __set__(self, obj, value):
+        cls = obj if isinstance(obj, type) else type(obj)
+        property.__set__(self, cls, value)
+)",
+                                    Py_file_input,
+                                    d.ptr(),
+                                    d.ptr());
+    if (result == nullptr)
+        throw error_already_set();
+    Py_DECREF(result);
+    return (PyTypeObject *) d["pybind11_static_property"].cast<object>().release().ptr();
+}
+
+#endif // PYPY
+
+/** Types with static properties need to handle `Type.static_prop = x` in a specific way.
+    By default, Python replaces the `static_property` itself, but for wrapped C++ types
+    we need to call `static_property.__set__()` in order to propagate the new value to
+    the underlying C++ data structure. */
+extern "C" inline int pybind11_meta_setattro(PyObject *obj, PyObject *name, PyObject *value) {
+    // Use `_PyType_Lookup()` instead of `PyObject_GetAttr()` in order to get the raw
+    // descriptor (`property`) instead of calling `tp_descr_get` (`property.__get__()`).
+    PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
+
+    // The following assignment combinations are possible:
+    //   1. `Type.static_prop = value`             --> descr_set: `Type.static_prop.__set__(value)`
+    //   2. `Type.static_prop = other_static_prop` --> setattro:  replace existing `static_prop`
+    //   3. `Type.regular_attribute = value`       --> setattro:  regular attribute assignment
+    auto *const static_prop = (PyObject *) get_internals().static_property_type;
+    const auto call_descr_set = (descr != nullptr) && (value != nullptr)
+                                && (PyObject_IsInstance(descr, static_prop) != 0)
+                                && (PyObject_IsInstance(value, static_prop) == 0);
+    if (call_descr_set) {
+        // Call `static_property.__set__()` instead of replacing the `static_property`.
+#if !defined(PYPY_VERSION)
+        return Py_TYPE(descr)->tp_descr_set(descr, obj, value);
+#else
+        if (PyObject *result = PyObject_CallMethod(descr, "__set__", "OO", obj, value)) {
+            Py_DECREF(result);
+            return 0;
+        } else {
+            return -1;
+        }
+#endif
+    } else {
+        // Replace existing attribute.
+        return PyType_Type.tp_setattro(obj, name, value);
+    }
+}
+
+/**
+ * Python 3's PyInstanceMethod_Type hides itself via its tp_descr_get, which prevents aliasing
+ * methods via cls.attr("m2") = cls.attr("m1"): instead the tp_descr_get returns a plain function,
+ * when called on a class, or a PyMethod, when called on an instance.  Override that behaviour here
+ * to do a special case bypass for PyInstanceMethod_Types.
+ */
+extern "C" inline PyObject *pybind11_meta_getattro(PyObject *obj, PyObject *name) {
+    PyObject *descr = _PyType_Lookup((PyTypeObject *) obj, name);
+    if (descr && PyInstanceMethod_Check(descr)) {
+        Py_INCREF(descr);
+        return descr;
+    }
+    return PyType_Type.tp_getattro(obj, name);
+}
+
+/// metaclass `__call__` function that is used to create all pybind11 objects.
+extern "C" inline PyObject *pybind11_meta_call(PyObject *type, PyObject *args, PyObject *kwargs) {
+
+    // use the default metaclass call to create/initialize the object
+    PyObject *self = PyType_Type.tp_call(type, args, kwargs);
+    if (self == nullptr) {
+        return nullptr;
+    }
+
+    // Ensure that the base __init__ function(s) were called
+    values_and_holders vhs(self);
+    for (const auto &vh : vhs) {
+        if (!vh.holder_constructed() && !vhs.is_redundant_value_and_holder(vh)) {
+            PyErr_Format(PyExc_TypeError,
+                         "%.200s.__init__() must be called when overriding __init__",
+                         get_fully_qualified_tp_name(vh.type->type).c_str());
+            Py_DECREF(self);
+            return nullptr;
+        }
+    }
+
+    return self;
+}
+
+/// Cleanup the type-info for a pybind11-registered type.
+extern "C" inline void pybind11_meta_dealloc(PyObject *obj) {
+    with_internals([obj](internals &internals) {
+        auto *type = (PyTypeObject *) obj;
+
+        // A pybind11-registered type will:
+        // 1) be found in internals.registered_types_py
+        // 2) have exactly one associated `detail::type_info`
+        auto found_type = internals.registered_types_py.find(type);
+        if (found_type != internals.registered_types_py.end() && found_type->second.size() == 1
+            && found_type->second[0]->type == type) {
+
+            auto *tinfo = found_type->second[0];
+            auto tindex = std::type_index(*tinfo->cpptype);
+            internals.direct_conversions.erase(tindex);
+
+            if (tinfo->module_local) {
+                get_local_internals().registered_types_cpp.erase(tindex);
+            } else {
+                internals.registered_types_cpp.erase(tindex);
+            }
+            internals.registered_types_py.erase(tinfo->type);
+
+            // Actually just `std::erase_if`, but that's only available in C++20
+            auto &cache = internals.inactive_override_cache;
+            for (auto it = cache.begin(), last = cache.end(); it != last;) {
+                if (it->first == (PyObject *) tinfo->type) {
+                    it = cache.erase(it);
+                } else {
+                    ++it;
+                }
+            }
+
+            delete tinfo;
+        }
+    });
+
+    PyType_Type.tp_dealloc(obj);
+}
+
+/** This metaclass is assigned by default to all pybind11 types and is required in order
+    for static properties to function correctly. Users may override this using `py::metaclass`.
+    Return value: New reference. */
+inline PyTypeObject *make_default_metaclass() {
+    constexpr auto *name = "pybind11_type";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) PyType_Type.tp_alloc(&PyType_Type, 0);
+    if (!heap_type) {
+        pybind11_fail("make_default_metaclass(): error allocating metaclass!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyType_Type);
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+
+    type->tp_call = pybind11_meta_call;
+
+    type->tp_setattro = pybind11_meta_setattro;
+    type->tp_getattro = pybind11_meta_getattro;
+
+    type->tp_dealloc = pybind11_meta_dealloc;
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("make_default_metaclass(): failure in PyType_Ready()!");
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    return type;
+}
+
+/// For multiple inheritance types we need to recursively register/deregister base pointers for any
+/// base classes with pointers that are difference from the instance value pointer so that we can
+/// correctly recognize an offset base class pointer. This calls a function with any offset base
+/// ptrs.
+inline void traverse_offset_bases(void *valueptr,
+                                  const detail::type_info *tinfo,
+                                  instance *self,
+                                  bool (*f)(void * /*parentptr*/, instance * /*self*/)) {
+    for (handle h : reinterpret_borrow<tuple>(tinfo->type->tp_bases)) {
+        if (auto *parent_tinfo = get_type_info((PyTypeObject *) h.ptr())) {
+            for (auto &c : parent_tinfo->implicit_casts) {
+                if (c.first == tinfo->cpptype) {
+                    auto *parentptr = c.second(valueptr);
+                    if (parentptr != valueptr) {
+                        f(parentptr, self);
+                    }
+                    traverse_offset_bases(parentptr, parent_tinfo, self, f);
+                    break;
+                }
+            }
+        }
+    }
+}
+
+inline bool register_instance_impl(void *ptr, instance *self) {
+    with_instance_map(ptr, [&](instance_map &instances) { instances.emplace(ptr, self); });
+    return true; // unused, but gives the same signature as the deregister func
+}
+inline bool deregister_instance_impl(void *ptr, instance *self) {
+    return with_instance_map(ptr, [&](instance_map &instances) {
+        auto range = instances.equal_range(ptr);
+        for (auto it = range.first; it != range.second; ++it) {
+            if (self == it->second) {
+                instances.erase(it);
+                return true;
+            }
+        }
+        return false;
+    });
+}
+
+inline void register_instance(instance *self, void *valptr, const type_info *tinfo) {
+    register_instance_impl(valptr, self);
+    if (!tinfo->simple_ancestors) {
+        traverse_offset_bases(valptr, tinfo, self, register_instance_impl);
+    }
+}
+
+inline bool deregister_instance(instance *self, void *valptr, const type_info *tinfo) {
+    bool ret = deregister_instance_impl(valptr, self);
+    if (!tinfo->simple_ancestors) {
+        traverse_offset_bases(valptr, tinfo, self, deregister_instance_impl);
+    }
+    return ret;
+}
+
+/// Instance creation function for all pybind11 types. It allocates the internal instance layout
+/// for holding C++ objects and holders.  Allocation is done lazily (the first time the instance is
+/// cast to a reference or pointer), and initialization is done by an `__init__` function.
+inline PyObject *make_new_instance(PyTypeObject *type) {
+#if defined(PYPY_VERSION)
+    // PyPy gets tp_basicsize wrong (issue 2482) under multiple inheritance when the first
+    // inherited object is a plain Python type (i.e. not derived from an extension type).  Fix it.
+    ssize_t instance_size = static_cast<ssize_t>(sizeof(instance));
+    if (type->tp_basicsize < instance_size) {
+        type->tp_basicsize = instance_size;
+    }
+#endif
+    PyObject *self = type->tp_alloc(type, 0);
+    auto *inst = reinterpret_cast<instance *>(self);
+    // Allocate the value/holder internals:
+    inst->allocate_layout();
+
+    return self;
+}
+
+/// Instance creation function for all pybind11 types. It only allocates space for the
+/// C++ object, but doesn't call the constructor -- an `__init__` function must do that.
+extern "C" inline PyObject *pybind11_object_new(PyTypeObject *type, PyObject *, PyObject *) {
+    return make_new_instance(type);
+}
+
+/// An `__init__` function constructs the C++ object. Users should provide at least one
+/// of these using `py::init` or directly with `.def(__init__, ...)`. Otherwise, the
+/// following default function will be used which simply throws an exception.
+extern "C" inline int pybind11_object_init(PyObject *self, PyObject *, PyObject *) {
+    PyTypeObject *type = Py_TYPE(self);
+    std::string msg = get_fully_qualified_tp_name(type) + ": No constructor defined!";
+    set_error(PyExc_TypeError, msg.c_str());
+    return -1;
+}
+
+inline void add_patient(PyObject *nurse, PyObject *patient) {
+    auto *instance = reinterpret_cast<detail::instance *>(nurse);
+    instance->has_patients = true;
+    Py_INCREF(patient);
+
+    with_internals([&](internals &internals) { internals.patients[nurse].push_back(patient); });
+}
+
+inline void clear_patients(PyObject *self) {
+    auto *instance = reinterpret_cast<detail::instance *>(self);
+    std::vector<PyObject *> patients;
+
+    with_internals([&](internals &internals) {
+        auto pos = internals.patients.find(self);
+
+        if (pos == internals.patients.end()) {
+            pybind11_fail(
+                "FATAL: Internal consistency check failed: Invalid clear_patients() call.");
+        }
+
+        // Clearing the patients can cause more Python code to run, which
+        // can invalidate the iterator. Extract the vector of patients
+        // from the unordered_map first.
+        patients = std::move(pos->second);
+        internals.patients.erase(pos);
+    });
+
+    instance->has_patients = false;
+    for (PyObject *&patient : patients) {
+        Py_CLEAR(patient);
+    }
+}
+
+/// Clears all internal data from the instance and removes it from registered instances in
+/// preparation for deallocation.
+inline void clear_instance(PyObject *self) {
+    auto *instance = reinterpret_cast<detail::instance *>(self);
+
+    // Deallocate any values/holders, if present:
+    for (auto &v_h : values_and_holders(instance)) {
+        if (v_h) {
+
+            // We have to deregister before we call dealloc because, for virtual MI types, we still
+            // need to be able to get the parent pointers.
+            if (v_h.instance_registered()
+                && !deregister_instance(instance, v_h.value_ptr(), v_h.type)) {
+                pybind11_fail(
+                    "pybind11_object_dealloc(): Tried to deallocate unregistered instance!");
+            }
+
+            if (instance->owned || v_h.holder_constructed()) {
+                v_h.type->dealloc(v_h);
+            }
+        }
+    }
+    // Deallocate the value/holder layout internals:
+    instance->deallocate_layout();
+
+    if (instance->weakrefs) {
+        PyObject_ClearWeakRefs(self);
+    }
+
+    PyObject **dict_ptr = _PyObject_GetDictPtr(self);
+    if (dict_ptr) {
+        Py_CLEAR(*dict_ptr);
+    }
+
+    if (instance->has_patients) {
+        clear_patients(self);
+    }
+}
+
+/// Instance destructor function for all pybind11 types. It calls `type_info.dealloc`
+/// to destroy the C++ object itself, while the rest is Python bookkeeping.
+extern "C" inline void pybind11_object_dealloc(PyObject *self) {
+    auto *type = Py_TYPE(self);
+
+    // If this is a GC tracked object, untrack it first
+    // Note that the track call is implicitly done by the
+    // default tp_alloc, which we never override.
+    if (PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC) != 0) {
+        PyObject_GC_UnTrack(self);
+    }
+
+    clear_instance(self);
+
+    type->tp_free(self);
+
+#if PY_VERSION_HEX < 0x03080000
+    // `type->tp_dealloc != pybind11_object_dealloc` means that we're being called
+    // as part of a derived type's dealloc, in which case we're not allowed to decref
+    // the type here. For cross-module compatibility, we shouldn't compare directly
+    // with `pybind11_object_dealloc`, but with the common one stashed in internals.
+    auto pybind11_object_type = (PyTypeObject *) get_internals().instance_base;
+    if (type->tp_dealloc == pybind11_object_type->tp_dealloc)
+        Py_DECREF(type);
+#else
+    // This was not needed before Python 3.8 (Python issue 35810)
+    // https://github.com/pybind/pybind11/issues/1946
+    Py_DECREF(type);
+#endif
+}
+
+std::string error_string();
+
+/** Create the type which can be used as a common base for all classes.  This is
+    needed in order to satisfy Python's requirements for multiple inheritance.
+    Return value: New reference. */
+inline PyObject *make_object_base_type(PyTypeObject *metaclass) {
+    constexpr auto *name = "pybind11_object";
+    auto name_obj = reinterpret_steal<object>(PYBIND11_FROM_STRING(name));
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
+    if (!heap_type) {
+        pybind11_fail("make_object_base_type(): error allocating type!");
+    }
+
+    heap_type->ht_name = name_obj.inc_ref().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = name_obj.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = name;
+    type->tp_base = type_incref(&PyBaseObject_Type);
+    type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
+    type->tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE;
+
+    type->tp_new = pybind11_object_new;
+    type->tp_init = pybind11_object_init;
+    type->tp_dealloc = pybind11_object_dealloc;
+
+    /* Support weak references (needed for the keep_alive feature) */
+    type->tp_weaklistoffset = offsetof(instance, weakrefs);
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail("PyType_Ready failed in make_object_base_type(): " + error_string());
+    }
+
+    setattr((PyObject *) type, "__module__", str("pybind11_builtins"));
+    PYBIND11_SET_OLDPY_QUALNAME(type, name_obj);
+
+    assert(!PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
+    return (PyObject *) heap_type;
+}
+
+/// dynamic_attr: Allow the garbage collector to traverse the internal instance `__dict__`.
+extern "C" inline int pybind11_traverse(PyObject *self, visitproc visit, void *arg) {
+#if PY_VERSION_HEX >= 0x030D0000
+    PyObject_VisitManagedDict(self, visit, arg);
+#else
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    Py_VISIT(dict);
+#endif
+// https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_traverse
+#if PY_VERSION_HEX >= 0x03090000
+    Py_VISIT(Py_TYPE(self));
+#endif
+    return 0;
+}
+
+/// dynamic_attr: Allow the GC to clear the dictionary.
+extern "C" inline int pybind11_clear(PyObject *self) {
+#if PY_VERSION_HEX >= 0x030D0000
+    PyObject_ClearManagedDict(self);
+#else
+    PyObject *&dict = *_PyObject_GetDictPtr(self);
+    Py_CLEAR(dict);
+#endif
+    return 0;
+}
+
+/// Give instances of this type a `__dict__` and opt into garbage collection.
+inline void enable_dynamic_attributes(PyHeapTypeObject *heap_type) {
+    auto *type = &heap_type->ht_type;
+    type->tp_flags |= Py_TPFLAGS_HAVE_GC;
+#if PY_VERSION_HEX < 0x030B0000
+    type->tp_dictoffset = type->tp_basicsize;           // place dict at the end
+    type->tp_basicsize += (ssize_t) sizeof(PyObject *); // and allocate enough space for it
+#else
+    type->tp_flags |= Py_TPFLAGS_MANAGED_DICT;
+#endif
+    type->tp_traverse = pybind11_traverse;
+    type->tp_clear = pybind11_clear;
+
+    static PyGetSetDef getset[]
+        = {{"__dict__", PyObject_GenericGetDict, PyObject_GenericSetDict, nullptr, nullptr},
+           {nullptr, nullptr, nullptr, nullptr, nullptr}};
+    type->tp_getset = getset;
+}
+
+/// buffer_protocol: Fill in the view as specified by flags.
+extern "C" inline int pybind11_getbuffer(PyObject *obj, Py_buffer *view, int flags) {
+    // Look for a `get_buffer` implementation in this type's info or any bases (following MRO).
+    type_info *tinfo = nullptr;
+    for (auto type : reinterpret_borrow<tuple>(Py_TYPE(obj)->tp_mro)) {
+        tinfo = get_type_info((PyTypeObject *) type.ptr());
+        if (tinfo && tinfo->get_buffer) {
+            break;
+        }
+    }
+    if (view == nullptr || !tinfo || !tinfo->get_buffer) {
+        if (view) {
+            view->obj = nullptr;
+        }
+        set_error(PyExc_BufferError, "pybind11_getbuffer(): Internal error");
+        return -1;
+    }
+    std::memset(view, 0, sizeof(Py_buffer));
+    buffer_info *info = nullptr;
+    try {
+        info = tinfo->get_buffer(obj, tinfo->get_buffer_data);
+    } catch (...) {
+        try_translate_exceptions();
+        raise_from(PyExc_BufferError, "Error getting buffer");
+        return -1;
+    }
+    if (info == nullptr) {
+        pybind11_fail("FATAL UNEXPECTED SITUATION: tinfo->get_buffer() returned nullptr.");
+    }
+
+    if ((flags & PyBUF_WRITABLE) == PyBUF_WRITABLE && info->readonly) {
+        delete info;
+        // view->obj = nullptr;  // Was just memset to 0, so not necessary
+        set_error(PyExc_BufferError, "Writable buffer requested for readonly storage");
+        return -1;
+    }
+    view->obj = obj;
+    view->ndim = 1;
+    view->internal = info;
+    view->buf = info->ptr;
+    view->itemsize = info->itemsize;
+    view->len = view->itemsize;
+    for (auto s : info->shape) {
+        view->len *= s;
+    }
+    view->readonly = static_cast<int>(info->readonly);
+    if ((flags & PyBUF_FORMAT) == PyBUF_FORMAT) {
+        view->format = const_cast<char *>(info->format.c_str());
+    }
+    if ((flags & PyBUF_STRIDES) == PyBUF_STRIDES) {
+        view->ndim = (int) info->ndim;
+        view->strides = info->strides.data();
+        view->shape = info->shape.data();
+    }
+    Py_INCREF(view->obj);
+    return 0;
+}
+
+/// buffer_protocol: Release the resources of the buffer.
+extern "C" inline void pybind11_releasebuffer(PyObject *, Py_buffer *view) {
+    delete (buffer_info *) view->internal;
+}
+
+/// Give this type a buffer interface.
+inline void enable_buffer_protocol(PyHeapTypeObject *heap_type) {
+    heap_type->ht_type.tp_as_buffer = &heap_type->as_buffer;
+
+    heap_type->as_buffer.bf_getbuffer = pybind11_getbuffer;
+    heap_type->as_buffer.bf_releasebuffer = pybind11_releasebuffer;
+}
+
+/** Create a brand new Python type according to the `type_record` specification.
+    Return value: New reference. */
+inline PyObject *make_new_python_type(const type_record &rec) {
+    auto name = reinterpret_steal<object>(PYBIND11_FROM_STRING(rec.name));
+
+    auto qualname = name;
+    if (rec.scope && !PyModule_Check(rec.scope.ptr()) && hasattr(rec.scope, "__qualname__")) {
+        qualname = reinterpret_steal<object>(
+            PyUnicode_FromFormat("%U.%U", rec.scope.attr("__qualname__").ptr(), name.ptr()));
+    }
+
+    object module_;
+    if (rec.scope) {
+        if (hasattr(rec.scope, "__module__")) {
+            module_ = rec.scope.attr("__module__");
+        } else if (hasattr(rec.scope, "__name__")) {
+            module_ = rec.scope.attr("__name__");
+        }
+    }
+
+    const auto *full_name = c_str(
+#if !defined(PYPY_VERSION)
+        module_ ? str(module_).cast<std::string>() + "." + rec.name :
+#endif
+                rec.name);
+
+    char *tp_doc = nullptr;
+    if (rec.doc && options::show_user_defined_docstrings()) {
+        /* Allocate memory for docstring (Python will free this later on) */
+        size_t size = std::strlen(rec.doc) + 1;
+#if PY_VERSION_HEX >= 0x030D0000
+        tp_doc = (char *) PyMem_MALLOC(size);
+#else
+        tp_doc = (char *) PyObject_MALLOC(size);
+#endif
+        std::memcpy((void *) tp_doc, rec.doc, size);
+    }
+
+    auto &internals = get_internals();
+    auto bases = tuple(rec.bases);
+    auto *base = (bases.empty()) ? internals.instance_base : bases[0].ptr();
+
+    /* Danger zone: from now (and until PyType_Ready), make sure to
+       issue no Python C API calls which could potentially invoke the
+       garbage collector (the GC will call type_traverse(), which will in
+       turn find the newly constructed type in an invalid state) */
+    auto *metaclass
+        = rec.metaclass.ptr() ? (PyTypeObject *) rec.metaclass.ptr() : internals.default_metaclass;
+
+    auto *heap_type = (PyHeapTypeObject *) metaclass->tp_alloc(metaclass, 0);
+    if (!heap_type) {
+        pybind11_fail(std::string(rec.name) + ": Unable to create type object!");
+    }
+
+    heap_type->ht_name = name.release().ptr();
+#ifdef PYBIND11_BUILTIN_QUALNAME
+    heap_type->ht_qualname = qualname.inc_ref().ptr();
+#endif
+
+    auto *type = &heap_type->ht_type;
+    type->tp_name = full_name;
+    type->tp_doc = tp_doc;
+    type->tp_base = type_incref((PyTypeObject *) base);
+    type->tp_basicsize = static_cast<ssize_t>(sizeof(instance));
+    if (!bases.empty()) {
+        type->tp_bases = bases.release().ptr();
+    }
+
+    /* Don't inherit base __init__ */
+    type->tp_init = pybind11_object_init;
+
+    /* Supported protocols */
+    type->tp_as_number = &heap_type->as_number;
+    type->tp_as_sequence = &heap_type->as_sequence;
+    type->tp_as_mapping = &heap_type->as_mapping;
+    type->tp_as_async = &heap_type->as_async;
+
+    /* Flags */
+    type->tp_flags |= Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HEAPTYPE;
+    if (!rec.is_final) {
+        type->tp_flags |= Py_TPFLAGS_BASETYPE;
+    }
+
+    if (rec.dynamic_attr) {
+        enable_dynamic_attributes(heap_type);
+    }
+
+    if (rec.buffer_protocol) {
+        enable_buffer_protocol(heap_type);
+    }
+
+    if (rec.custom_type_setup_callback) {
+        rec.custom_type_setup_callback(heap_type);
+    }
+
+    if (PyType_Ready(type) < 0) {
+        pybind11_fail(std::string(rec.name) + ": PyType_Ready failed: " + error_string());
+    }
+
+    assert(!rec.dynamic_attr || PyType_HasFeature(type, Py_TPFLAGS_HAVE_GC));
+
+    /* Register type with the parent scope */
+    if (rec.scope) {
+        setattr(rec.scope, rec.name, (PyObject *) type);
+    } else {
+        Py_INCREF(type); // Keep it alive forever (reference leak)
+    }
+
+    if (module_) { // Needed by pydoc
+        setattr((PyObject *) type, "__module__", module_);
+    }
+
+    PYBIND11_SET_OLDPY_QUALNAME(type, qualname);
+
+    return (PyObject *) type;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/common.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/common.h
new file mode 100644
index 0000000000000000000000000000000000000000..c51d1d60bc739bfd67efc64fb517f0f0c2d7592a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/common.h
@@ -0,0 +1,1287 @@
+/*
+    pybind11/detail/common.h -- Basic macros
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#define PYBIND11_VERSION_MAJOR 2
+#define PYBIND11_VERSION_MINOR 13
+#define PYBIND11_VERSION_PATCH 6
+
+// Similar to Python's convention: https://docs.python.org/3/c-api/apiabiversion.html
+// Additional convention: 0xD = dev
+#define PYBIND11_VERSION_HEX 0x020D0600
+
+// Define some generic pybind11 helper macros for warning management.
+//
+// Note that compiler-specific push/pop pairs are baked into the
+// PYBIND11_NAMESPACE_BEGIN/PYBIND11_NAMESPACE_END pair of macros. Therefore manual
+// PYBIND11_WARNING_PUSH/PYBIND11_WARNING_POP are usually only needed in `#include` sections.
+//
+// If you find you need to suppress a warning, please try to make the suppression as local as
+// possible using these macros. Please also be sure to push/pop with the pybind11 macros. Please
+// only use compiler specifics if you need to check specific versions, e.g. Apple Clang vs. vanilla
+// Clang.
+#if defined(_MSC_VER)
+#    define PYBIND11_COMPILER_MSVC
+#    define PYBIND11_PRAGMA(...) __pragma(__VA_ARGS__)
+#    define PYBIND11_WARNING_PUSH PYBIND11_PRAGMA(warning(push))
+#    define PYBIND11_WARNING_POP PYBIND11_PRAGMA(warning(pop))
+#elif defined(__INTEL_COMPILER)
+#    define PYBIND11_COMPILER_INTEL
+#    define PYBIND11_PRAGMA(...) _Pragma(#__VA_ARGS__)
+#    define PYBIND11_WARNING_PUSH PYBIND11_PRAGMA(warning push)
+#    define PYBIND11_WARNING_POP PYBIND11_PRAGMA(warning pop)
+#elif defined(__clang__)
+#    define PYBIND11_COMPILER_CLANG
+#    define PYBIND11_PRAGMA(...) _Pragma(#__VA_ARGS__)
+#    define PYBIND11_WARNING_PUSH PYBIND11_PRAGMA(clang diagnostic push)
+#    define PYBIND11_WARNING_POP PYBIND11_PRAGMA(clang diagnostic push)
+#elif defined(__GNUC__)
+#    define PYBIND11_COMPILER_GCC
+#    define PYBIND11_PRAGMA(...) _Pragma(#__VA_ARGS__)
+#    define PYBIND11_WARNING_PUSH PYBIND11_PRAGMA(GCC diagnostic push)
+#    define PYBIND11_WARNING_POP PYBIND11_PRAGMA(GCC diagnostic pop)
+#endif
+
+#ifdef PYBIND11_COMPILER_MSVC
+#    define PYBIND11_WARNING_DISABLE_MSVC(name) PYBIND11_PRAGMA(warning(disable : name))
+#else
+#    define PYBIND11_WARNING_DISABLE_MSVC(name)
+#endif
+
+#ifdef PYBIND11_COMPILER_CLANG
+#    define PYBIND11_WARNING_DISABLE_CLANG(name) PYBIND11_PRAGMA(clang diagnostic ignored name)
+#else
+#    define PYBIND11_WARNING_DISABLE_CLANG(name)
+#endif
+
+#ifdef PYBIND11_COMPILER_GCC
+#    define PYBIND11_WARNING_DISABLE_GCC(name) PYBIND11_PRAGMA(GCC diagnostic ignored name)
+#else
+#    define PYBIND11_WARNING_DISABLE_GCC(name)
+#endif
+
+#ifdef PYBIND11_COMPILER_INTEL
+#    define PYBIND11_WARNING_DISABLE_INTEL(name) PYBIND11_PRAGMA(warning disable name)
+#else
+#    define PYBIND11_WARNING_DISABLE_INTEL(name)
+#endif
+
+#define PYBIND11_NAMESPACE_BEGIN(name)                                                            \
+    namespace name {                                                                              \
+    PYBIND11_WARNING_PUSH
+
+#define PYBIND11_NAMESPACE_END(name)                                                              \
+    PYBIND11_WARNING_POP                                                                          \
+    }
+
+// Robust support for some features and loading modules compiled against different pybind versions
+// requires forcing hidden visibility on pybind code, so we enforce this by setting the attribute
+// on the main `pybind11` namespace.
+#if !defined(PYBIND11_NAMESPACE)
+#    ifdef __GNUG__
+#        define PYBIND11_NAMESPACE pybind11 __attribute__((visibility("hidden")))
+#    else
+#        define PYBIND11_NAMESPACE pybind11
+#    endif
+#endif
+
+#if !(defined(_MSC_VER) && __cplusplus == 199711L)
+#    if __cplusplus >= 201402L
+#        define PYBIND11_CPP14
+#        if __cplusplus >= 201703L
+#            define PYBIND11_CPP17
+#            if __cplusplus >= 202002L
+#                define PYBIND11_CPP20
+// Please update tests/pybind11_tests.cpp `cpp_std()` when adding a macro here.
+#            endif
+#        endif
+#    endif
+#elif defined(_MSC_VER) && __cplusplus == 199711L
+// MSVC sets _MSVC_LANG rather than __cplusplus (supposedly until the standard is fully
+// implemented). Unless you use the /Zc:__cplusplus flag on Visual Studio 2017 15.7 Preview 3
+// or newer.
+#    if _MSVC_LANG >= 201402L
+#        define PYBIND11_CPP14
+#        if _MSVC_LANG > 201402L
+#            define PYBIND11_CPP17
+#            if _MSVC_LANG >= 202002L
+#                define PYBIND11_CPP20
+#            endif
+#        endif
+#    endif
+#endif
+
+#if defined(PYBIND11_CPP20)
+#    define PYBIND11_CONSTINIT constinit
+#    define PYBIND11_DTOR_CONSTEXPR constexpr
+#else
+#    define PYBIND11_CONSTINIT
+#    define PYBIND11_DTOR_CONSTEXPR
+#endif
+
+// Compiler version assertions
+#if defined(__INTEL_COMPILER)
+#    if __INTEL_COMPILER < 1800
+#        error pybind11 requires Intel C++ compiler v18 or newer
+#    elif __INTEL_COMPILER < 1900 && defined(PYBIND11_CPP14)
+#        error pybind11 supports only C++11 with Intel C++ compiler v18. Use v19 or newer for C++14.
+#    endif
+/* The following pragma cannot be pop'ed:
+   https://community.intel.com/t5/Intel-C-Compiler/Inline-and-no-inline-warning/td-p/1216764 */
+#    pragma warning disable 2196 // warning #2196: routine is both "inline" and "noinline"
+#elif defined(__clang__) && !defined(__apple_build_version__)
+#    if __clang_major__ < 3 || (__clang_major__ == 3 && __clang_minor__ < 3)
+#        error pybind11 requires clang 3.3 or newer
+#    endif
+#elif defined(__clang__)
+// Apple changes clang version macros to its Xcode version; the first Xcode release based on
+// (upstream) clang 3.3 was Xcode 5:
+#    if __clang_major__ < 5
+#        error pybind11 requires Xcode/clang 5.0 or newer
+#    endif
+#elif defined(__GNUG__)
+#    if __GNUC__ < 4 || (__GNUC__ == 4 && __GNUC_MINOR__ < 8)
+#        error pybind11 requires gcc 4.8 or newer
+#    endif
+#elif defined(_MSC_VER)
+#    if _MSC_VER < 1910
+#        error pybind11 2.10+ requires MSVC 2017 or newer
+#    endif
+#endif
+
+#if !defined(PYBIND11_EXPORT)
+#    if defined(WIN32) || defined(_WIN32)
+#        define PYBIND11_EXPORT __declspec(dllexport)
+#    else
+#        define PYBIND11_EXPORT __attribute__((visibility("default")))
+#    endif
+#endif
+
+#if !defined(PYBIND11_EXPORT_EXCEPTION)
+#    if defined(__apple_build_version__)
+#        define PYBIND11_EXPORT_EXCEPTION PYBIND11_EXPORT
+#    else
+#        define PYBIND11_EXPORT_EXCEPTION
+#    endif
+#endif
+
+// For CUDA, GCC7, GCC8:
+// PYBIND11_NOINLINE_FORCED is incompatible with `-Wattributes -Werror`.
+// When defining PYBIND11_NOINLINE_FORCED, it is best to also use `-Wno-attributes`.
+// However, the measured shared-library size saving when using noinline are only
+// 1.7% for CUDA, -0.2% for GCC7, and 0.0% for GCC8 (using -DCMAKE_BUILD_TYPE=MinSizeRel,
+// the default under pybind11/tests).
+#if !defined(PYBIND11_NOINLINE_FORCED)                                                            \
+    && (defined(__CUDACC__) || (defined(__GNUC__) && (__GNUC__ == 7 || __GNUC__ == 8)))
+#    define PYBIND11_NOINLINE_DISABLED
+#endif
+
+// The PYBIND11_NOINLINE macro is for function DEFINITIONS.
+// In contrast, FORWARD DECLARATIONS should never use this macro:
+// https://stackoverflow.com/questions/9317473/forward-declaration-of-inline-functions
+#if defined(PYBIND11_NOINLINE_DISABLED) // Option for maximum portability and experimentation.
+#    define PYBIND11_NOINLINE inline
+#elif defined(_MSC_VER)
+#    define PYBIND11_NOINLINE __declspec(noinline) inline
+#else
+#    define PYBIND11_NOINLINE __attribute__((noinline)) inline
+#endif
+
+#if defined(__MINGW32__)
+// For unknown reasons all PYBIND11_DEPRECATED member trigger a warning when declared
+// whether it is used or not
+#    define PYBIND11_DEPRECATED(reason)
+#elif defined(PYBIND11_CPP14)
+#    define PYBIND11_DEPRECATED(reason) [[deprecated(reason)]]
+#else
+#    define PYBIND11_DEPRECATED(reason) __attribute__((deprecated(reason)))
+#endif
+
+#if defined(PYBIND11_CPP17)
+#    define PYBIND11_MAYBE_UNUSED [[maybe_unused]]
+#elif defined(_MSC_VER) && !defined(__clang__)
+#    define PYBIND11_MAYBE_UNUSED
+#else
+#    define PYBIND11_MAYBE_UNUSED __attribute__((__unused__))
+#endif
+
+/* Don't let Python.h #define (v)snprintf as macro because they are implemented
+   properly in Visual Studio since 2015. */
+#if defined(_MSC_VER)
+#    define HAVE_SNPRINTF 1
+#endif
+
+/// Include Python header, disable linking to pythonX_d.lib on Windows in debug mode
+#if defined(_MSC_VER)
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(4505)
+// C4505: 'PySlice_GetIndicesEx': unreferenced local function has been removed (PyPy only)
+#    if defined(_DEBUG) && !defined(Py_DEBUG)
+// Workaround for a VS 2022 issue.
+// NOTE: This workaround knowingly violates the Python.h include order requirement:
+// https://docs.python.org/3/c-api/intro.html#include-files
+// See https://github.com/pybind/pybind11/pull/3497 for full context.
+#        include <yvals.h>
+#        if _MSVC_STL_VERSION >= 143
+#            include <crtdefs.h>
+#        endif
+#        define PYBIND11_DEBUG_MARKER
+#        undef _DEBUG
+#    endif
+#endif
+
+// https://en.cppreference.com/w/c/chrono/localtime
+#if defined(__STDC_LIB_EXT1__) && !defined(__STDC_WANT_LIB_EXT1__)
+#    define __STDC_WANT_LIB_EXT1__
+#endif
+
+#ifdef __has_include
+// std::optional (but including it in c++14 mode isn't allowed)
+#    if defined(PYBIND11_CPP17) && __has_include(<optional>)
+#        define PYBIND11_HAS_OPTIONAL 1
+#    endif
+// std::experimental::optional (but not allowed in c++11 mode)
+#    if defined(PYBIND11_CPP14) && (__has_include(<experimental/optional>) && \
+                                 !__has_include(<optional>))
+#        define PYBIND11_HAS_EXP_OPTIONAL 1
+#    endif
+// std::variant
+#    if defined(PYBIND11_CPP17) && __has_include(<variant>)
+#        define PYBIND11_HAS_VARIANT 1
+#    endif
+#elif defined(_MSC_VER) && defined(PYBIND11_CPP17)
+#    define PYBIND11_HAS_OPTIONAL 1
+#    define PYBIND11_HAS_VARIANT 1
+#endif
+
+#if defined(PYBIND11_CPP17)
+#    if defined(__has_include)
+#        if __has_include(<string_view>)
+#            define PYBIND11_HAS_STRING_VIEW
+#        endif
+#    elif defined(_MSC_VER)
+#        define PYBIND11_HAS_STRING_VIEW
+#    endif
+#endif
+
+#include <Python.h>
+#if PY_VERSION_HEX < 0x03070000
+#    error "PYTHON < 3.7 IS UNSUPPORTED. pybind11 v2.12 was the last to support Python 3.6."
+#endif
+#include <frameobject.h>
+#include <pythread.h>
+
+/* Python #defines overrides on all sorts of core functions, which
+   tends to weak havok in C++ codebases that expect these to work
+   like regular functions (potentially with several overloads) */
+#if defined(isalnum)
+#    undef isalnum
+#    undef isalpha
+#    undef islower
+#    undef isspace
+#    undef isupper
+#    undef tolower
+#    undef toupper
+#endif
+
+#if defined(copysign)
+#    undef copysign
+#endif
+
+#if defined(PYBIND11_NUMPY_1_ONLY)
+#    define PYBIND11_INTERNAL_NUMPY_1_ONLY_DETECTED
+#endif
+
+#if defined(PYPY_VERSION) && !defined(PYBIND11_SIMPLE_GIL_MANAGEMENT)
+#    define PYBIND11_SIMPLE_GIL_MANAGEMENT
+#endif
+
+#if defined(_MSC_VER)
+#    if defined(PYBIND11_DEBUG_MARKER)
+#        define _DEBUG
+#        undef PYBIND11_DEBUG_MARKER
+#    endif
+PYBIND11_WARNING_POP
+#endif
+
+#include <cstddef>
+#include <cstring>
+#include <exception>
+#include <forward_list>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+#if defined(__has_include)
+#    if __has_include(<version>)
+#        include <version>
+#    endif
+#endif
+
+// Must be after including <version> or one of the other headers specified by the standard
+#if defined(__cpp_lib_char8_t) && __cpp_lib_char8_t >= 201811L
+#    define PYBIND11_HAS_U8STRING
+#endif
+
+// See description of PR #4246:
+#if !defined(PYBIND11_NO_ASSERT_GIL_HELD_INCREF_DECREF) && !defined(NDEBUG)                       \
+    && !defined(PYPY_VERSION) && !defined(PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF)
+#    define PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF
+#endif
+
+// #define PYBIND11_STR_LEGACY_PERMISSIVE
+// If DEFINED, pybind11::str can hold PyUnicodeObject or PyBytesObject
+//             (probably surprising and never documented, but this was the
+//             legacy behavior until and including v2.6.x). As a side-effect,
+//             pybind11::isinstance<str>() is true for both pybind11::str and
+//             pybind11::bytes.
+// If UNDEFINED, pybind11::str can only hold PyUnicodeObject, and
+//               pybind11::isinstance<str>() is true only for pybind11::str.
+//               However, for Python 2 only (!), the pybind11::str caster
+//               implicitly decoded bytes to PyUnicodeObject. This was to ease
+//               the transition from the legacy behavior to the non-permissive
+//               behavior.
+
+/// Compatibility macros for Python 2 / Python 3 versions TODO: remove
+#define PYBIND11_INSTANCE_METHOD_NEW(ptr, class_) PyInstanceMethod_New(ptr)
+#define PYBIND11_INSTANCE_METHOD_CHECK PyInstanceMethod_Check
+#define PYBIND11_INSTANCE_METHOD_GET_FUNCTION PyInstanceMethod_GET_FUNCTION
+#define PYBIND11_BYTES_CHECK PyBytes_Check
+#define PYBIND11_BYTES_FROM_STRING PyBytes_FromString
+#define PYBIND11_BYTES_FROM_STRING_AND_SIZE PyBytes_FromStringAndSize
+#define PYBIND11_BYTES_AS_STRING_AND_SIZE PyBytes_AsStringAndSize
+#define PYBIND11_BYTES_AS_STRING PyBytes_AsString
+#define PYBIND11_BYTES_SIZE PyBytes_Size
+#define PYBIND11_LONG_CHECK(o) PyLong_Check(o)
+#define PYBIND11_LONG_AS_LONGLONG(o) PyLong_AsLongLong(o)
+#define PYBIND11_LONG_FROM_SIGNED(o) PyLong_FromSsize_t((ssize_t) (o))
+#define PYBIND11_LONG_FROM_UNSIGNED(o) PyLong_FromSize_t((size_t) (o))
+#define PYBIND11_BYTES_NAME "bytes"
+#define PYBIND11_STRING_NAME "str"
+#define PYBIND11_SLICE_OBJECT PyObject
+#define PYBIND11_FROM_STRING PyUnicode_FromString
+#define PYBIND11_STR_TYPE ::pybind11::str
+#define PYBIND11_BOOL_ATTR "__bool__"
+#define PYBIND11_NB_BOOL(ptr) ((ptr)->nb_bool)
+#define PYBIND11_BUILTINS_MODULE "builtins"
+// Providing a separate declaration to make Clang's -Wmissing-prototypes happy.
+// See comment for PYBIND11_MODULE below for why this is marked "maybe unused".
+#define PYBIND11_PLUGIN_IMPL(name)                                                                \
+    extern "C" PYBIND11_MAYBE_UNUSED PYBIND11_EXPORT PyObject *PyInit_##name();                   \
+    extern "C" PYBIND11_EXPORT PyObject *PyInit_##name()
+
+#define PYBIND11_TRY_NEXT_OVERLOAD ((PyObject *) 1) // special failure return code
+#define PYBIND11_STRINGIFY(x) #x
+#define PYBIND11_TOSTRING(x) PYBIND11_STRINGIFY(x)
+#define PYBIND11_CONCAT(first, second) first##second
+#define PYBIND11_ENSURE_INTERNALS_READY pybind11::detail::get_internals();
+
+#define PYBIND11_CHECK_PYTHON_VERSION                                                             \
+    {                                                                                             \
+        const char *compiled_ver                                                                  \
+            = PYBIND11_TOSTRING(PY_MAJOR_VERSION) "." PYBIND11_TOSTRING(PY_MINOR_VERSION);        \
+        const char *runtime_ver = Py_GetVersion();                                                \
+        size_t len = std::strlen(compiled_ver);                                                   \
+        if (std::strncmp(runtime_ver, compiled_ver, len) != 0                                     \
+            || (runtime_ver[len] >= '0' && runtime_ver[len] <= '9')) {                            \
+            PyErr_Format(PyExc_ImportError,                                                       \
+                         "Python version mismatch: module was compiled for Python %s, "           \
+                         "but the interpreter version is incompatible: %s.",                      \
+                         compiled_ver,                                                            \
+                         runtime_ver);                                                            \
+            return nullptr;                                                                       \
+        }                                                                                         \
+    }
+
+#define PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    catch (pybind11::error_already_set & e) {                                                     \
+        pybind11::raise_from(e, PyExc_ImportError, "initialization failed");                      \
+        return nullptr;                                                                           \
+    }                                                                                             \
+    catch (const std::exception &e) {                                                             \
+        ::pybind11::set_error(PyExc_ImportError, e.what());                                       \
+        return nullptr;                                                                           \
+    }
+
+/** \rst
+    ***Deprecated in favor of PYBIND11_MODULE***
+
+    This macro creates the entry point that will be invoked when the Python interpreter
+    imports a plugin library. Please create a `module_` in the function body and return
+    the pointer to its underlying Python object at the end.
+
+    .. code-block:: cpp
+
+        PYBIND11_PLUGIN(example) {
+            pybind11::module_ m("example", "pybind11 example plugin");
+            /// Set up bindings here
+            return m.ptr();
+        }
+\endrst */
+#define PYBIND11_PLUGIN(name)                                                                     \
+    PYBIND11_DEPRECATED("PYBIND11_PLUGIN is deprecated, use PYBIND11_MODULE")                     \
+    static PyObject *pybind11_init();                                                             \
+    PYBIND11_PLUGIN_IMPL(name) {                                                                  \
+        PYBIND11_CHECK_PYTHON_VERSION                                                             \
+        PYBIND11_ENSURE_INTERNALS_READY                                                           \
+        try {                                                                                     \
+            return pybind11_init();                                                               \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    PyObject *pybind11_init()
+
+/** \rst
+    This macro creates the entry point that will be invoked when the Python interpreter
+    imports an extension module. The module name is given as the first argument and it
+    should not be in quotes. The second macro argument defines a variable of type
+    `py::module_` which can be used to initialize the module.
+
+    The entry point is marked as "maybe unused" to aid dead-code detection analysis:
+    since the entry point is typically only looked up at runtime and not referenced
+    during translation, it would otherwise appear as unused ("dead") code.
+
+    .. code-block:: cpp
+
+        PYBIND11_MODULE(example, m) {
+            m.doc() = "pybind11 example module";
+
+            // Add bindings here
+            m.def("foo", []() {
+                return "Hello, World!";
+            });
+        }
+
+    The third macro argument is optional (available since 2.13.0), and can be used to
+    mark the extension module as safe to run without the GIL under a free-threaded CPython
+    interpreter. Passing this argument has no effect on other interpreters.
+
+    .. code-block:: cpp
+
+        PYBIND11_MODULE(example, m, py::mod_gil_not_used()) {
+            m.doc() = "pybind11 example module safe to run without the GIL";
+
+            // Add bindings here
+            m.def("foo", []() {
+                return "Hello, Free-threaded World!";
+            });
+        }
+
+\endrst */
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_CLANG("-Wgnu-zero-variadic-macro-arguments")
+#define PYBIND11_MODULE(name, variable, ...)                                                      \
+    static ::pybind11::module_::module_def PYBIND11_CONCAT(pybind11_module_def_, name)            \
+        PYBIND11_MAYBE_UNUSED;                                                                    \
+    PYBIND11_MAYBE_UNUSED                                                                         \
+    static void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ &);                     \
+    PYBIND11_PLUGIN_IMPL(name) {                                                                  \
+        PYBIND11_CHECK_PYTHON_VERSION                                                             \
+        PYBIND11_ENSURE_INTERNALS_READY                                                           \
+        auto m = ::pybind11::module_::create_extension_module(                                    \
+            PYBIND11_TOSTRING(name),                                                              \
+            nullptr,                                                                              \
+            &PYBIND11_CONCAT(pybind11_module_def_, name),                                         \
+            ##__VA_ARGS__);                                                                       \
+        try {                                                                                     \
+            PYBIND11_CONCAT(pybind11_init_, name)(m);                                             \
+            return m.ptr();                                                                       \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ & (variable))
+PYBIND11_WARNING_POP
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+using ssize_t = Py_ssize_t;
+using size_t = std::size_t;
+
+template <typename IntType>
+inline ssize_t ssize_t_cast(const IntType &val) {
+    static_assert(sizeof(IntType) <= sizeof(ssize_t), "Implicit narrowing is not permitted.");
+    return static_cast<ssize_t>(val);
+}
+
+/// Approach used to cast a previously unknown C++ instance into a Python object
+enum class return_value_policy : uint8_t {
+    /** This is the default return value policy, which falls back to the policy
+        return_value_policy::take_ownership when the return value is a pointer.
+        Otherwise, it uses return_value::move or return_value::copy for rvalue
+        and lvalue references, respectively. See below for a description of what
+        all of these different policies do. */
+    automatic = 0,
+
+    /** As above, but use policy return_value_policy::reference when the return
+        value is a pointer. This is the default conversion policy for function
+        arguments when calling Python functions manually from C++ code (i.e. via
+        handle::operator()). You probably won't need to use this. */
+    automatic_reference,
+
+    /** Reference an existing object (i.e. do not create a new copy) and take
+        ownership. Python will call the destructor and delete operator when the
+        object's reference count reaches zero. Undefined behavior ensues when
+        the C++ side does the same.. */
+    take_ownership,
+
+    /** Create a new copy of the returned object, which will be owned by
+        Python. This policy is comparably safe because the lifetimes of the two
+        instances are decoupled. */
+    copy,
+
+    /** Use std::move to move the return value contents into a new instance
+        that will be owned by Python. This policy is comparably safe because the
+        lifetimes of the two instances (move source and destination) are
+        decoupled. */
+    move,
+
+    /** Reference an existing object, but do not take ownership. The C++ side
+        is responsible for managing the object's lifetime and deallocating it
+        when it is no longer used. Warning: undefined behavior will ensue when
+        the C++ side deletes an object that is still referenced and used by
+        Python. */
+    reference,
+
+    /** This policy only applies to methods and properties. It references the
+        object without taking ownership similar to the above
+        return_value_policy::reference policy. In contrast to that policy, the
+        function or property's implicit this argument (called the parent) is
+        considered to be the owner of the return value (the child).
+        pybind11 then couples the lifetime of the parent to the child via a
+        reference relationship that ensures that the parent cannot be garbage
+        collected while Python is still using the child. More advanced
+        variations of this scheme are also possible using combinations of
+        return_value_policy::reference and the keep_alive call policy */
+    reference_internal
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline static constexpr int log2(size_t n, int k = 0) {
+    return (n <= 1) ? k : log2(n >> 1, k + 1);
+}
+
+// Returns the size as a multiple of sizeof(void *), rounded up.
+inline static constexpr size_t size_in_ptrs(size_t s) {
+    return 1 + ((s - 1) >> log2(sizeof(void *)));
+}
+
+/**
+ * The space to allocate for simple layout instance holders (see below) in multiple of the size of
+ * a pointer (e.g.  2 means 16 bytes on 64-bit architectures).  The default is the minimum required
+ * to holder either a std::unique_ptr or std::shared_ptr (which is almost always
+ * sizeof(std::shared_ptr<T>)).
+ */
+constexpr size_t instance_simple_holder_in_ptrs() {
+    static_assert(sizeof(std::shared_ptr<int>) >= sizeof(std::unique_ptr<int>),
+                  "pybind assumes std::shared_ptrs are at least as big as std::unique_ptrs");
+    return size_in_ptrs(sizeof(std::shared_ptr<int>));
+}
+
+// Forward declarations
+struct type_info;
+struct value_and_holder;
+
+struct nonsimple_values_and_holders {
+    void **values_and_holders;
+    uint8_t *status;
+};
+
+/// The 'instance' type which needs to be standard layout (need to be able to use 'offsetof')
+struct instance {
+    PyObject_HEAD
+    /// Storage for pointers and holder; see simple_layout, below, for a description
+    union {
+        void *simple_value_holder[1 + instance_simple_holder_in_ptrs()];
+        nonsimple_values_and_holders nonsimple;
+    };
+    /// Weak references
+    PyObject *weakrefs;
+    /// If true, the pointer is owned which means we're free to manage it with a holder.
+    bool owned : 1;
+    /**
+     * An instance has two possible value/holder layouts.
+     *
+     * Simple layout (when this flag is true), means the `simple_value_holder` is set with a
+     * pointer and the holder object governing that pointer, i.e. [val1*][holder].  This layout is
+     * applied whenever there is no python-side multiple inheritance of bound C++ types *and* the
+     * type's holder will fit in the default space (which is large enough to hold either a
+     * std::unique_ptr or std::shared_ptr).
+     *
+     * Non-simple layout applies when using custom holders that require more space than
+     * `shared_ptr` (which is typically the size of two pointers), or when multiple inheritance is
+     * used on the python side.  Non-simple layout allocates the required amount of memory to have
+     * multiple bound C++ classes as parents.  Under this layout, `nonsimple.values_and_holders` is
+     * set to a pointer to allocated space of the required space to hold a sequence of value
+     * pointers and holders followed `status`, a set of bit flags (1 byte each), i.e.
+     * [val1*][holder1][val2*][holder2]...[bb...]  where each [block] is rounded up to a multiple
+     * of `sizeof(void *)`.  `nonsimple.status` is, for convenience, a pointer to the beginning of
+     * the [bb...] block (but not independently allocated).
+     *
+     * Status bits indicate whether the associated holder is constructed (&
+     * status_holder_constructed) and whether the value pointer is registered (&
+     * status_instance_registered) in `registered_instances`.
+     */
+    bool simple_layout : 1;
+    /// For simple layout, tracks whether the holder has been constructed
+    bool simple_holder_constructed : 1;
+    /// For simple layout, tracks whether the instance is registered in `registered_instances`
+    bool simple_instance_registered : 1;
+    /// If true, get_internals().patients has an entry for this object
+    bool has_patients : 1;
+
+    /// Initializes all of the above type/values/holders data (but not the instance values
+    /// themselves)
+    void allocate_layout();
+
+    /// Destroys/deallocates all of the above
+    void deallocate_layout();
+
+    /// Returns the value_and_holder wrapper for the given type (or the first, if `find_type`
+    /// omitted).  Returns a default-constructed (with `.inst = nullptr`) object on failure if
+    /// `throw_if_missing` is false.
+    value_and_holder get_value_and_holder(const type_info *find_type = nullptr,
+                                          bool throw_if_missing = true);
+
+    /// Bit values for the non-simple status flags
+    static constexpr uint8_t status_holder_constructed = 1;
+    static constexpr uint8_t status_instance_registered = 2;
+};
+
+static_assert(std::is_standard_layout<instance>::value,
+              "Internal error: `pybind11::detail::instance` is not standard layout!");
+
+/// from __cpp_future__ import (convenient aliases from C++14/17)
+#if defined(PYBIND11_CPP14)
+using std::conditional_t;
+using std::enable_if_t;
+using std::remove_cv_t;
+using std::remove_reference_t;
+#else
+template <bool B, typename T = void>
+using enable_if_t = typename std::enable_if<B, T>::type;
+template <bool B, typename T, typename F>
+using conditional_t = typename std::conditional<B, T, F>::type;
+template <typename T>
+using remove_cv_t = typename std::remove_cv<T>::type;
+template <typename T>
+using remove_reference_t = typename std::remove_reference<T>::type;
+#endif
+
+#if defined(PYBIND11_CPP20)
+using std::remove_cvref;
+using std::remove_cvref_t;
+#else
+template <class T>
+struct remove_cvref {
+    using type = remove_cv_t<remove_reference_t<T>>;
+};
+template <class T>
+using remove_cvref_t = typename remove_cvref<T>::type;
+#endif
+
+/// Example usage: is_same_ignoring_cvref<T, PyObject *>::value
+template <typename T, typename U>
+using is_same_ignoring_cvref = std::is_same<detail::remove_cvref_t<T>, U>;
+
+/// Index sequences
+#if defined(PYBIND11_CPP14)
+using std::index_sequence;
+using std::make_index_sequence;
+#else
+template <size_t...>
+struct index_sequence {};
+template <size_t N, size_t... S>
+struct make_index_sequence_impl : make_index_sequence_impl<N - 1, N - 1, S...> {};
+template <size_t... S>
+struct make_index_sequence_impl<0, S...> {
+    using type = index_sequence<S...>;
+};
+template <size_t N>
+using make_index_sequence = typename make_index_sequence_impl<N>::type;
+#endif
+
+/// Make an index sequence of the indices of true arguments
+template <typename ISeq, size_t, bool...>
+struct select_indices_impl {
+    using type = ISeq;
+};
+template <size_t... IPrev, size_t I, bool B, bool... Bs>
+struct select_indices_impl<index_sequence<IPrev...>, I, B, Bs...>
+    : select_indices_impl<conditional_t<B, index_sequence<IPrev..., I>, index_sequence<IPrev...>>,
+                          I + 1,
+                          Bs...> {};
+template <bool... Bs>
+using select_indices = typename select_indices_impl<index_sequence<>, 0, Bs...>::type;
+
+/// Backports of std::bool_constant and std::negation to accommodate older compilers
+template <bool B>
+using bool_constant = std::integral_constant<bool, B>;
+template <typename T>
+struct negation : bool_constant<!T::value> {};
+
+// PGI/Intel cannot detect operator delete with the "compatible" void_t impl, so
+// using the new one (C++14 defect, so generally works on newer compilers, even
+// if not in C++17 mode)
+#if defined(__PGIC__) || defined(__INTEL_COMPILER)
+template <typename...>
+using void_t = void;
+#else
+template <typename...>
+struct void_t_impl {
+    using type = void;
+};
+template <typename... Ts>
+using void_t = typename void_t_impl<Ts...>::type;
+#endif
+
+/// Compile-time all/any/none of that check the boolean value of all template types
+#if defined(__cpp_fold_expressions) && !(defined(_MSC_VER) && (_MSC_VER < 1916))
+template <class... Ts>
+using all_of = bool_constant<(Ts::value && ...)>;
+template <class... Ts>
+using any_of = bool_constant<(Ts::value || ...)>;
+#elif !defined(_MSC_VER)
+template <bool...>
+struct bools {};
+template <class... Ts>
+using all_of = std::is_same<bools<Ts::value..., true>, bools<true, Ts::value...>>;
+template <class... Ts>
+using any_of = negation<all_of<negation<Ts>...>>;
+#else
+// MSVC has trouble with the above, but supports std::conjunction, which we can use instead (albeit
+// at a slight loss of compilation efficiency).
+template <class... Ts>
+using all_of = std::conjunction<Ts...>;
+template <class... Ts>
+using any_of = std::disjunction<Ts...>;
+#endif
+template <class... Ts>
+using none_of = negation<any_of<Ts...>>;
+
+template <class T, template <class> class... Predicates>
+using satisfies_all_of = all_of<Predicates<T>...>;
+template <class T, template <class> class... Predicates>
+using satisfies_any_of = any_of<Predicates<T>...>;
+template <class T, template <class> class... Predicates>
+using satisfies_none_of = none_of<Predicates<T>...>;
+
+/// Strip the class from a method type
+template <typename T>
+struct remove_class {};
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...)> {
+    using type = R(A...);
+};
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...) const> {
+    using type = R(A...);
+};
+#ifdef __cpp_noexcept_function_type
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...) noexcept> {
+    using type = R(A...);
+};
+template <typename C, typename R, typename... A>
+struct remove_class<R (C::*)(A...) const noexcept> {
+    using type = R(A...);
+};
+#endif
+/// Helper template to strip away type modifiers
+template <typename T>
+struct intrinsic_type {
+    using type = T;
+};
+template <typename T>
+struct intrinsic_type<const T> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T *> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T &> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+struct intrinsic_type<T &&> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T, size_t N>
+struct intrinsic_type<const T[N]> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T, size_t N>
+struct intrinsic_type<T[N]> {
+    using type = typename intrinsic_type<T>::type;
+};
+template <typename T>
+using intrinsic_t = typename intrinsic_type<T>::type;
+
+/// Helper type to replace 'void' in some expressions
+struct void_type {};
+
+/// Helper template which holds a list of types
+template <typename...>
+struct type_list {};
+
+/// Compile-time integer sum
+#ifdef __cpp_fold_expressions
+template <typename... Ts>
+constexpr size_t constexpr_sum(Ts... ns) {
+    return (0 + ... + size_t{ns});
+}
+#else
+constexpr size_t constexpr_sum() { return 0; }
+template <typename T, typename... Ts>
+constexpr size_t constexpr_sum(T n, Ts... ns) {
+    return size_t{n} + constexpr_sum(ns...);
+}
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(constexpr_impl)
+/// Implementation details for constexpr functions
+constexpr int first(int i) { return i; }
+template <typename T, typename... Ts>
+constexpr int first(int i, T v, Ts... vs) {
+    return v ? i : first(i + 1, vs...);
+}
+
+constexpr int last(int /*i*/, int result) { return result; }
+template <typename T, typename... Ts>
+constexpr int last(int i, int result, T v, Ts... vs) {
+    return last(i + 1, v ? i : result, vs...);
+}
+PYBIND11_NAMESPACE_END(constexpr_impl)
+
+/// Return the index of the first type in Ts which satisfies Predicate<T>.
+/// Returns sizeof...(Ts) if none match.
+template <template <typename> class Predicate, typename... Ts>
+constexpr int constexpr_first() {
+    return constexpr_impl::first(0, Predicate<Ts>::value...);
+}
+
+/// Return the index of the last type in Ts which satisfies Predicate<T>, or -1 if none match.
+template <template <typename> class Predicate, typename... Ts>
+constexpr int constexpr_last() {
+    return constexpr_impl::last(0, -1, Predicate<Ts>::value...);
+}
+
+/// Return the Nth element from the parameter pack
+template <size_t N, typename T, typename... Ts>
+struct pack_element {
+    using type = typename pack_element<N - 1, Ts...>::type;
+};
+template <typename T, typename... Ts>
+struct pack_element<0, T, Ts...> {
+    using type = T;
+};
+
+/// Return the one and only type which matches the predicate, or Default if none match.
+/// If more than one type matches the predicate, fail at compile-time.
+template <template <typename> class Predicate, typename Default, typename... Ts>
+struct exactly_one {
+    static constexpr auto found = constexpr_sum(Predicate<Ts>::value...);
+    static_assert(found <= 1, "Found more than one type matching the predicate");
+
+    static constexpr auto index = found ? constexpr_first<Predicate, Ts...>() : 0;
+    using type = conditional_t<found, typename pack_element<index, Ts...>::type, Default>;
+};
+template <template <typename> class P, typename Default>
+struct exactly_one<P, Default> {
+    using type = Default;
+};
+
+template <template <typename> class Predicate, typename Default, typename... Ts>
+using exactly_one_t = typename exactly_one<Predicate, Default, Ts...>::type;
+
+/// Defer the evaluation of type T until types Us are instantiated
+template <typename T, typename... /*Us*/>
+struct deferred_type {
+    using type = T;
+};
+template <typename T, typename... Us>
+using deferred_t = typename deferred_type<T, Us...>::type;
+
+/// Like is_base_of, but requires a strict base (i.e. `is_strict_base_of<T, T>::value == false`,
+/// unlike `std::is_base_of`)
+template <typename Base, typename Derived>
+using is_strict_base_of
+    = bool_constant<std::is_base_of<Base, Derived>::value && !std::is_same<Base, Derived>::value>;
+
+/// Like is_base_of, but also requires that the base type is accessible (i.e. that a Derived
+/// pointer can be converted to a Base pointer) For unions, `is_base_of<T, T>::value` is False, so
+/// we need to check `is_same` as well.
+template <typename Base, typename Derived>
+using is_accessible_base_of
+    = bool_constant<(std::is_same<Base, Derived>::value || std::is_base_of<Base, Derived>::value)
+                    && std::is_convertible<Derived *, Base *>::value>;
+
+template <template <typename...> class Base>
+struct is_template_base_of_impl {
+    template <typename... Us>
+    static std::true_type check(Base<Us...> *);
+    static std::false_type check(...);
+};
+
+/// Check if a template is the base of a type. For example:
+/// `is_template_base_of<Base, T>` is true if `struct T : Base<U> {}` where U can be anything
+template <template <typename...> class Base, typename T>
+// Sadly, all MSVC versions incl. 2022 need the workaround, even in C++20 mode.
+// See also: https://github.com/pybind/pybind11/pull/3741
+#if !defined(_MSC_VER)
+using is_template_base_of
+    = decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T> *) nullptr));
+#else
+struct is_template_base_of
+    : decltype(is_template_base_of_impl<Base>::check((intrinsic_t<T> *) nullptr)){};
+#endif
+
+/// Check if T is an instantiation of the template `Class`. For example:
+/// `is_instantiation<shared_ptr, T>` is true if `T == shared_ptr<U>` where U can be anything.
+template <template <typename...> class Class, typename T>
+struct is_instantiation : std::false_type {};
+template <template <typename...> class Class, typename... Us>
+struct is_instantiation<Class, Class<Us...>> : std::true_type {};
+
+/// Check if T is std::shared_ptr<U> where U can be anything
+template <typename T>
+using is_shared_ptr = is_instantiation<std::shared_ptr, T>;
+
+/// Check if T looks like an input iterator
+template <typename T, typename = void>
+struct is_input_iterator : std::false_type {};
+template <typename T>
+struct is_input_iterator<T,
+                         void_t<decltype(*std::declval<T &>()), decltype(++std::declval<T &>())>>
+    : std::true_type {};
+
+template <typename T>
+using is_function_pointer
+    = bool_constant<std::is_pointer<T>::value
+                    && std::is_function<typename std::remove_pointer<T>::type>::value>;
+
+template <typename F>
+struct strip_function_object {
+    // If you are encountering an
+    // 'error: name followed by "::" must be a class or namespace name'
+    // with the Intel compiler and a noexcept function here,
+    // try to use noexcept(true) instead of plain noexcept.
+    using type = typename remove_class<decltype(&F::operator())>::type;
+};
+
+// Extracts the function signature from a function, function pointer or lambda.
+template <typename Function, typename F = remove_reference_t<Function>>
+using function_signature_t = conditional_t<
+    std::is_function<F>::value,
+    F,
+    typename conditional_t<std::is_pointer<F>::value || std::is_member_pointer<F>::value,
+                           std::remove_pointer<F>,
+                           strip_function_object<F>>::type>;
+
+/// Returns true if the type looks like a lambda: that is, isn't a function, pointer or member
+/// pointer.  Note that this can catch all sorts of other things, too; this is intended to be used
+/// in a place where passing a lambda makes sense.
+template <typename T>
+using is_lambda = satisfies_none_of<remove_reference_t<T>,
+                                    std::is_function,
+                                    std::is_pointer,
+                                    std::is_member_pointer>;
+
+// [workaround(intel)] Internal error on fold expression
+/// Apply a function over each element of a parameter pack
+#if defined(__cpp_fold_expressions) && !defined(__INTEL_COMPILER)
+// Intel compiler produces an internal error on this fold expression (tested with ICC 19.0.2)
+#    define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN) (((PATTERN), void()), ...)
+#else
+using expand_side_effects = bool[];
+#    define PYBIND11_EXPAND_SIDE_EFFECTS(PATTERN)                                                 \
+        (void) pybind11::detail::expand_side_effects { ((PATTERN), void(), false)..., false }
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// C++ bindings of builtin Python exceptions
+class PYBIND11_EXPORT_EXCEPTION builtin_exception : public std::runtime_error {
+public:
+    using std::runtime_error::runtime_error;
+    /// Set the error using the Python C API
+    virtual void set_error() const = 0;
+};
+
+#define PYBIND11_RUNTIME_EXCEPTION(name, type)                                                    \
+    class PYBIND11_EXPORT_EXCEPTION name : public builtin_exception {                             \
+    public:                                                                                       \
+        using builtin_exception::builtin_exception;                                               \
+        name() : name("") {}                                                                      \
+        void set_error() const override { PyErr_SetString(type, what()); }                        \
+    };
+
+PYBIND11_RUNTIME_EXCEPTION(stop_iteration, PyExc_StopIteration)
+PYBIND11_RUNTIME_EXCEPTION(index_error, PyExc_IndexError)
+PYBIND11_RUNTIME_EXCEPTION(key_error, PyExc_KeyError)
+PYBIND11_RUNTIME_EXCEPTION(value_error, PyExc_ValueError)
+PYBIND11_RUNTIME_EXCEPTION(type_error, PyExc_TypeError)
+PYBIND11_RUNTIME_EXCEPTION(buffer_error, PyExc_BufferError)
+PYBIND11_RUNTIME_EXCEPTION(import_error, PyExc_ImportError)
+PYBIND11_RUNTIME_EXCEPTION(attribute_error, PyExc_AttributeError)
+PYBIND11_RUNTIME_EXCEPTION(cast_error, PyExc_RuntimeError) /// Thrown when pybind11::cast or
+                                                           /// handle::call fail due to a type
+                                                           /// casting error
+PYBIND11_RUNTIME_EXCEPTION(reference_cast_error, PyExc_RuntimeError) /// Used internally
+
+[[noreturn]] PYBIND11_NOINLINE void pybind11_fail(const char *reason) {
+    assert(!PyErr_Occurred());
+    throw std::runtime_error(reason);
+}
+[[noreturn]] PYBIND11_NOINLINE void pybind11_fail(const std::string &reason) {
+    assert(!PyErr_Occurred());
+    throw std::runtime_error(reason);
+}
+
+template <typename T, typename SFINAE = void>
+struct format_descriptor {};
+
+template <typename T>
+struct format_descriptor<
+    T,
+    detail::enable_if_t<detail::is_same_ignoring_cvref<T, PyObject *>::value>> {
+    static constexpr const char c = 'O';
+    static constexpr const char value[2] = {c, '\0'};
+    static std::string format() { return std::string(1, c); }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+// Returns the index of the given type in the type char array below, and in the list in numpy.h
+// The order here is: bool; 8 ints ((signed,unsigned)x(8,16,32,64)bits); float,double,long double;
+// complex float,double,long double.  Note that the long double types only participate when long
+// double is actually longer than double (it isn't under MSVC).
+// NB: not only the string below but also complex.h and numpy.h rely on this order.
+template <typename T, typename SFINAE = void>
+struct is_fmt_numeric {
+    static constexpr bool value = false;
+};
+template <typename T>
+struct is_fmt_numeric<T, enable_if_t<std::is_arithmetic<T>::value>> {
+    static constexpr bool value = true;
+    static constexpr int index
+        = std::is_same<T, bool>::value
+              ? 0
+              : 1
+                    + (std::is_integral<T>::value
+                           ? detail::log2(sizeof(T)) * 2 + std::is_unsigned<T>::value
+                           : 8
+                                 + (std::is_same<T, double>::value        ? 1
+                                    : std::is_same<T, long double>::value ? 2
+                                                                          : 0));
+};
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>> {
+    static constexpr const char c = "?bBhHiIqQfdg"[detail::is_fmt_numeric<T>::index];
+    static constexpr const char value[2] = {c, '\0'};
+    static std::string format() { return std::string(1, c); }
+};
+
+#if !defined(PYBIND11_CPP17)
+
+template <typename T>
+constexpr const char
+    format_descriptor<T, detail::enable_if_t<std::is_arithmetic<T>::value>>::value[2];
+
+#endif
+
+/// RAII wrapper that temporarily clears any Python error state
+struct error_scope {
+    PyObject *type, *value, *trace;
+    error_scope() { PyErr_Fetch(&type, &value, &trace); }
+    error_scope(const error_scope &) = delete;
+    error_scope &operator=(const error_scope &) = delete;
+    ~error_scope() { PyErr_Restore(type, value, trace); }
+};
+
+/// Dummy destructor wrapper that can be used to expose classes with a private destructor
+struct nodelete {
+    template <typename T>
+    void operator()(T *) {}
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename... Args>
+struct overload_cast_impl {
+    template <typename Return>
+    constexpr auto operator()(Return (*pf)(Args...)) const noexcept -> decltype(pf) {
+        return pf;
+    }
+
+    template <typename Return, typename Class>
+    constexpr auto operator()(Return (Class::*pmf)(Args...),
+                              std::false_type = {}) const noexcept -> decltype(pmf) {
+        return pmf;
+    }
+
+    template <typename Return, typename Class>
+    constexpr auto operator()(Return (Class::*pmf)(Args...) const,
+                              std::true_type) const noexcept -> decltype(pmf) {
+        return pmf;
+    }
+};
+PYBIND11_NAMESPACE_END(detail)
+
+// overload_cast requires variable templates: C++14
+#if defined(PYBIND11_CPP14)
+#    define PYBIND11_OVERLOAD_CAST 1
+/// Syntax sugar for resolving overloaded function pointers:
+///  - regular: static_cast<Return (Class::*)(Arg0, Arg1, Arg2)>(&Class::func)
+///  - sweet:   overload_cast<Arg0, Arg1, Arg2>(&Class::func)
+template <typename... Args>
+static constexpr detail::overload_cast_impl<Args...> overload_cast{};
+#endif
+
+/// Const member function selector for overload_cast
+///  - regular: static_cast<Return (Class::*)(Arg) const>(&Class::func)
+///  - sweet:   overload_cast<Arg>(&Class::func, const_)
+static constexpr auto const_ = std::true_type{};
+
+#if !defined(PYBIND11_CPP14) // no overload_cast: providing something that static_assert-fails:
+template <typename... Args>
+struct overload_cast {
+    static_assert(detail::deferred_t<std::false_type, Args...>::value,
+                  "pybind11::overload_cast<...> requires compiling in C++14 mode");
+};
+#endif // overload_cast
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Adaptor for converting arbitrary container arguments into a vector; implicitly convertible from
+// any standard container (or C-style array) supporting std::begin/std::end, any singleton
+// arithmetic type (if T is arithmetic), or explicitly constructible from an iterator pair.
+template <typename T>
+class any_container {
+    std::vector<T> v;
+
+public:
+    any_container() = default;
+
+    // Can construct from a pair of iterators
+    template <typename It, typename = enable_if_t<is_input_iterator<It>::value>>
+    any_container(It first, It last) : v(first, last) {}
+
+    // Implicit conversion constructor from any arbitrary container type
+    // with values convertible to T
+    template <typename Container,
+              typename = enable_if_t<
+                  std::is_convertible<decltype(*std::begin(std::declval<const Container &>())),
+                                      T>::value>>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    any_container(const Container &c) : any_container(std::begin(c), std::end(c)) {}
+
+    // initializer_list's aren't deducible, so don't get matched by the above template;
+    // we need this to explicitly allow implicit conversion from one:
+    template <typename TIn, typename = enable_if_t<std::is_convertible<TIn, T>::value>>
+    any_container(const std::initializer_list<TIn> &c) : any_container(c.begin(), c.end()) {}
+
+    // Avoid copying if given an rvalue vector of the correct type.
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    any_container(std::vector<T> &&v) : v(std::move(v)) {}
+
+    // Moves the vector out of an rvalue any_container
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::vector<T> &&() && { return std::move(v); }
+
+    // Dereferencing obtains a reference to the underlying vector
+    std::vector<T> &operator*() { return v; }
+    const std::vector<T> &operator*() const { return v; }
+
+    // -> lets you call methods on the underlying vector
+    std::vector<T> *operator->() { return &v; }
+    const std::vector<T> *operator->() const { return &v; }
+};
+
+// Forward-declaration; see detail/class.h
+std::string get_fully_qualified_tp_name(PyTypeObject *);
+
+template <typename T>
+inline static std::shared_ptr<T>
+try_get_shared_from_this(std::enable_shared_from_this<T> *holder_value_ptr) {
+// Pre C++17, this code path exploits undefined behavior, but is known to work on many platforms.
+// Use at your own risk!
+// See also https://en.cppreference.com/w/cpp/memory/enable_shared_from_this, and in particular
+// the `std::shared_ptr<Good> gp1 = not_so_good.getptr();` and `try`-`catch` parts of the example.
+#if defined(__cpp_lib_enable_shared_from_this) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+    return holder_value_ptr->weak_from_this().lock();
+#else
+    try {
+        return holder_value_ptr->shared_from_this();
+    } catch (const std::bad_weak_ptr &) {
+        return nullptr;
+    }
+#endif
+}
+
+// For silencing "unused" compiler warnings in special situations.
+template <typename... Args>
+#if defined(_MSC_VER) && _MSC_VER < 1920 // MSVC 2017
+constexpr
+#endif
+    inline void
+    silence_unused_warnings(Args &&...) {
+}
+
+// MSVC warning C4100: Unreferenced formal parameter
+#if defined(_MSC_VER) && _MSC_VER <= 1916
+#    define PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(...)                                         \
+        detail::silence_unused_warnings(__VA_ARGS__)
+#else
+#    define PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(...)
+#endif
+
+// GCC -Wunused-but-set-parameter  All GCC versions (as of July 2021).
+#if defined(__GNUG__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#    define PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(...)                       \
+        detail::silence_unused_warnings(__VA_ARGS__)
+#else
+#    define PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(...)
+#endif
+
+#if defined(__clang__)                                                                            \
+    && (defined(__apple_build_version__) /* AppleClang 13.0.0.13000029 was the only data point    \
+                                            available. */                                         \
+        || (__clang_major__ >= 7                                                                  \
+            && __clang_major__ <= 12) /* Clang 3, 5, 13, 14, 15 do not generate the warning. */   \
+    )
+#    define PYBIND11_DETECTED_CLANG_WITH_MISLEADING_CALL_STD_MOVE_EXPLICITLY_WARNING
+// Example:
+// tests/test_kwargs_and_defaults.cpp:46:68: error: local variable 'args' will be copied despite
+// being returned by name [-Werror,-Wreturn-std-move]
+//     m.def("args_function", [](py::args args) -> py::tuple { return args; });
+//                                                                    ^~~~
+// test_kwargs_and_defaults.cpp:46:68: note: call 'std::move' explicitly to avoid copying
+//     m.def("args_function", [](py::args args) -> py::tuple { return args; });
+//                                                                    ^~~~
+//                                                                    std::move(args)
+#endif
+
+// Pybind offers detailed error messages by default for all builts that are debug (through the
+// negation of NDEBUG). This can also be manually enabled by users, for any builds, through
+// defining PYBIND11_DETAILED_ERROR_MESSAGES. This information is primarily useful for those
+// who are writing (as opposed to merely using) libraries that use pybind11.
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES) && !defined(NDEBUG)
+#    define PYBIND11_DETAILED_ERROR_MESSAGES
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/cpp_conduit.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/cpp_conduit.h
new file mode 100644
index 0000000000000000000000000000000000000000..b66c2d39c0019dcfc8a5fd137bbf37d2515a97b2
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/cpp_conduit.h
@@ -0,0 +1,77 @@
+// Copyright (c) 2024 The pybind Community.
+
+#pragma once
+
+#include <pybind11/pytypes.h>
+
+#include "common.h"
+#include "internals.h"
+
+#include <typeinfo>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Forward declaration needed here: Refactoring opportunity.
+extern "C" inline PyObject *pybind11_object_new(PyTypeObject *type, PyObject *, PyObject *);
+
+inline bool type_is_managed_by_our_internals(PyTypeObject *type_obj) {
+#if defined(PYPY_VERSION)
+    auto &internals = get_internals();
+    return bool(internals.registered_types_py.find(type_obj)
+                != internals.registered_types_py.end());
+#else
+    return bool(type_obj->tp_new == pybind11_object_new);
+#endif
+}
+
+inline bool is_instance_method_of_type(PyTypeObject *type_obj, PyObject *attr_name) {
+    PyObject *descr = _PyType_Lookup(type_obj, attr_name);
+    return bool((descr != nullptr) && PyInstanceMethod_Check(descr));
+}
+
+inline object try_get_cpp_conduit_method(PyObject *obj) {
+    if (PyType_Check(obj)) {
+        return object();
+    }
+    PyTypeObject *type_obj = Py_TYPE(obj);
+    str attr_name("_pybind11_conduit_v1_");
+    bool assumed_to_be_callable = false;
+    if (type_is_managed_by_our_internals(type_obj)) {
+        if (!is_instance_method_of_type(type_obj, attr_name.ptr())) {
+            return object();
+        }
+        assumed_to_be_callable = true;
+    }
+    PyObject *method = PyObject_GetAttr(obj, attr_name.ptr());
+    if (method == nullptr) {
+        PyErr_Clear();
+        return object();
+    }
+    if (!assumed_to_be_callable && PyCallable_Check(method) == 0) {
+        Py_DECREF(method);
+        return object();
+    }
+    return reinterpret_steal<object>(method);
+}
+
+inline void *try_raw_pointer_ephemeral_from_cpp_conduit(handle src,
+                                                        const std::type_info *cpp_type_info) {
+    object method = try_get_cpp_conduit_method(src.ptr());
+    if (method) {
+        capsule cpp_type_info_capsule(const_cast<void *>(static_cast<const void *>(cpp_type_info)),
+                                      typeid(std::type_info).name());
+        object cpp_conduit = method(bytes(PYBIND11_PLATFORM_ABI_ID),
+                                    cpp_type_info_capsule,
+                                    bytes("raw_pointer_ephemeral"));
+        if (isinstance<capsule>(cpp_conduit)) {
+            return reinterpret_borrow<capsule>(cpp_conduit).get_pointer();
+        }
+    }
+    return nullptr;
+}
+
+#define PYBIND11_HAS_CPP_CONDUIT 1
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/descr.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/descr.h
new file mode 100644
index 0000000000000000000000000000000000000000..7d546311e78f76f92af13d745b84c3664228fdc9
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/descr.h
@@ -0,0 +1,172 @@
+/*
+    pybind11/detail/descr.h: Helper type for concatenating type signatures at compile time
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if !defined(_MSC_VER)
+#    define PYBIND11_DESCR_CONSTEXPR static constexpr
+#else
+#    define PYBIND11_DESCR_CONSTEXPR const
+#endif
+
+/* Concatenate type signatures at compile time */
+template <size_t N, typename... Ts>
+struct descr {
+    char text[N + 1]{'\0'};
+
+    constexpr descr() = default;
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    constexpr descr(char const (&s)[N + 1]) : descr(s, make_index_sequence<N>()) {}
+
+    template <size_t... Is>
+    constexpr descr(char const (&s)[N + 1], index_sequence<Is...>) : text{s[Is]..., '\0'} {}
+
+    template <typename... Chars>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    constexpr descr(char c, Chars... cs) : text{c, static_cast<char>(cs)..., '\0'} {}
+
+    static constexpr std::array<const std::type_info *, sizeof...(Ts) + 1> types() {
+        return {{&typeid(Ts)..., nullptr}};
+    }
+};
+
+template <size_t N1, size_t N2, typename... Ts1, typename... Ts2, size_t... Is1, size_t... Is2>
+constexpr descr<N1 + N2, Ts1..., Ts2...> plus_impl(const descr<N1, Ts1...> &a,
+                                                   const descr<N2, Ts2...> &b,
+                                                   index_sequence<Is1...>,
+                                                   index_sequence<Is2...>) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(b);
+    return {a.text[Is1]..., b.text[Is2]...};
+}
+
+template <size_t N1, size_t N2, typename... Ts1, typename... Ts2>
+constexpr descr<N1 + N2, Ts1..., Ts2...> operator+(const descr<N1, Ts1...> &a,
+                                                   const descr<N2, Ts2...> &b) {
+    return plus_impl(a, b, make_index_sequence<N1>(), make_index_sequence<N2>());
+}
+
+template <size_t N>
+constexpr descr<N - 1> const_name(char const (&text)[N]) {
+    return descr<N - 1>(text);
+}
+constexpr descr<0> const_name(char const (&)[1]) { return {}; }
+
+template <size_t Rem, size_t... Digits>
+struct int_to_str : int_to_str<Rem / 10, Rem % 10, Digits...> {};
+template <size_t... Digits>
+struct int_to_str<0, Digits...> {
+    // WARNING: This only works with C++17 or higher.
+    static constexpr auto digits = descr<sizeof...(Digits)>(('0' + Digits)...);
+};
+
+// Ternary description (like std::conditional)
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<B, descr<N1 - 1>> const_name(char const (&text1)[N1], char const (&)[N2]) {
+    return const_name(text1);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<!B, descr<N2 - 1>> const_name(char const (&)[N1], char const (&text2)[N2]) {
+    return const_name(text2);
+}
+
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<B, T1> const_name(const T1 &d, const T2 &) {
+    return d;
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<!B, T2> const_name(const T1 &, const T2 &d) {
+    return d;
+}
+
+template <size_t Size>
+auto constexpr const_name() -> remove_cv_t<decltype(int_to_str<Size / 10, Size % 10>::digits)> {
+    return int_to_str<Size / 10, Size % 10>::digits;
+}
+
+template <typename Type>
+constexpr descr<1, Type> const_name() {
+    return {'%'};
+}
+
+// If "_" is defined as a macro, py::detail::_ cannot be provided.
+// It is therefore best to use py::detail::const_name universally.
+// This block is for backward compatibility only.
+// (The const_name code is repeated to avoid introducing a "_" #define ourselves.)
+#ifndef _
+#    define PYBIND11_DETAIL_UNDERSCORE_BACKWARD_COMPATIBILITY
+template <size_t N>
+constexpr descr<N - 1> _(char const (&text)[N]) {
+    return const_name<N>(text);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<B, descr<N1 - 1>> _(char const (&text1)[N1], char const (&text2)[N2]) {
+    return const_name<B, N1, N2>(text1, text2);
+}
+template <bool B, size_t N1, size_t N2>
+constexpr enable_if_t<!B, descr<N2 - 1>> _(char const (&text1)[N1], char const (&text2)[N2]) {
+    return const_name<B, N1, N2>(text1, text2);
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<B, T1> _(const T1 &d1, const T2 &d2) {
+    return const_name<B, T1, T2>(d1, d2);
+}
+template <bool B, typename T1, typename T2>
+constexpr enable_if_t<!B, T2> _(const T1 &d1, const T2 &d2) {
+    return const_name<B, T1, T2>(d1, d2);
+}
+
+template <size_t Size>
+auto constexpr _() -> remove_cv_t<decltype(int_to_str<Size / 10, Size % 10>::digits)> {
+    return const_name<Size>();
+}
+template <typename Type>
+constexpr descr<1, Type> _() {
+    return const_name<Type>();
+}
+#endif // #ifndef _
+
+constexpr descr<0> concat() { return {}; }
+
+template <size_t N, typename... Ts>
+constexpr descr<N, Ts...> concat(const descr<N, Ts...> &descr) {
+    return descr;
+}
+
+#ifdef __cpp_fold_expressions
+template <size_t N1, size_t N2, typename... Ts1, typename... Ts2>
+constexpr descr<N1 + N2 + 2, Ts1..., Ts2...> operator,(const descr<N1, Ts1...> &a,
+                                                       const descr<N2, Ts2...> &b) {
+    return a + const_name(", ") + b;
+}
+
+template <size_t N, typename... Ts, typename... Args>
+constexpr auto concat(const descr<N, Ts...> &d, const Args &...args) {
+    return (d, ..., args);
+}
+#else
+template <size_t N, typename... Ts, typename... Args>
+constexpr auto concat(const descr<N, Ts...> &d,
+                      const Args &...args) -> decltype(std::declval<descr<N + 2, Ts...>>()
+                                                       + concat(args...)) {
+    return d + const_name(", ") + concat(args...);
+}
+#endif
+
+template <size_t N, typename... Ts>
+constexpr descr<N + 2, Ts...> type_descr(const descr<N, Ts...> &descr) {
+    return const_name("{") + descr + const_name("}");
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/exception_translation.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/exception_translation.h
new file mode 100644
index 0000000000000000000000000000000000000000..2764180bb078c4ed2b9648de9d6752040256d295
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/exception_translation.h
@@ -0,0 +1,71 @@
+/*
+    pybind11/detail/exception_translation.h: means to translate C++ exceptions to Python exceptions
+
+    Copyright (c) 2024 The Pybind Development Team.
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "common.h"
+#include "internals.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Apply all the extensions translators from a list
+// Return true if one of the translators completed without raising an exception
+// itself. Return of false indicates that if there are other translators
+// available, they should be tried.
+inline bool apply_exception_translators(std::forward_list<ExceptionTranslator> &translators) {
+    auto last_exception = std::current_exception();
+
+    for (auto &translator : translators) {
+        try {
+            translator(last_exception);
+            return true;
+        } catch (...) {
+            last_exception = std::current_exception();
+        }
+    }
+    return false;
+}
+
+inline void try_translate_exceptions() {
+    /* When an exception is caught, give each registered exception
+        translator a chance to translate it to a Python exception. First
+        all module-local translators will be tried in reverse order of
+        registration. If none of the module-locale translators handle
+        the exception (or there are no module-locale translators) then
+        the global translators will be tried, also in reverse order of
+        registration.
+
+        A translator may choose to do one of the following:
+
+        - catch the exception and call py::set_error()
+            to set a standard (or custom) Python exception, or
+        - do nothing and let the exception fall through to the next translator, or
+        - delegate translation to the next translator by throwing a new type of exception.
+        */
+
+    bool handled = with_internals([&](internals &internals) {
+        auto &local_exception_translators = get_local_internals().registered_exception_translators;
+        if (detail::apply_exception_translators(local_exception_translators)) {
+            return true;
+        }
+        auto &exception_translators = internals.registered_exception_translators;
+        if (detail::apply_exception_translators(exception_translators)) {
+            return true;
+        }
+        return false;
+    });
+
+    if (!handled) {
+        set_error(PyExc_SystemError, "Exception escaped from default exception translator!");
+    }
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/init.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/init.h
new file mode 100644
index 0000000000000000000000000000000000000000..79cc930c8df6f4bc99383bc8c0208d0b610325fd
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/init.h
@@ -0,0 +1,436 @@
+/*
+    pybind11/detail/init.h: init factory function implementation and support code.
+
+    Copyright (c) 2017 Jason Rhinelander <jason@imaginary.ca>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "class.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+class type_caster<value_and_holder> {
+public:
+    bool load(handle h, bool) {
+        value = reinterpret_cast<value_and_holder *>(h.ptr());
+        return true;
+    }
+
+    template <typename>
+    using cast_op_type = value_and_holder &;
+    explicit operator value_and_holder &() { return *value; }
+    static constexpr auto name = const_name<value_and_holder>();
+
+private:
+    value_and_holder *value = nullptr;
+};
+
+PYBIND11_NAMESPACE_BEGIN(initimpl)
+
+inline void no_nullptr(void *ptr) {
+    if (!ptr) {
+        throw type_error("pybind11::init(): factory function returned nullptr");
+    }
+}
+
+// Implementing functions for all forms of py::init<...> and py::init(...)
+template <typename Class>
+using Cpp = typename Class::type;
+template <typename Class>
+using Alias = typename Class::type_alias;
+template <typename Class>
+using Holder = typename Class::holder_type;
+
+template <typename Class>
+using is_alias_constructible = std::is_constructible<Alias<Class>, Cpp<Class> &&>;
+
+// Takes a Cpp pointer and returns true if it actually is a polymorphic Alias instance.
+template <typename Class, enable_if_t<Class::has_alias, int> = 0>
+bool is_alias(Cpp<Class> *ptr) {
+    return dynamic_cast<Alias<Class> *>(ptr) != nullptr;
+}
+// Failing fallback version of the above for a no-alias class (always returns false)
+template <typename /*Class*/>
+constexpr bool is_alias(void *) {
+    return false;
+}
+
+// Constructs and returns a new object; if the given arguments don't map to a constructor, we fall
+// back to brace aggregate initialization so that for aggregate initialization can be used with
+// py::init, e.g.  `py::init<int, int>` to initialize a `struct T { int a; int b; }`.  For
+// non-aggregate types, we need to use an ordinary T(...) constructor (invoking as `T{...}` usually
+// works, but will not do the expected thing when `T` has an `initializer_list<T>` constructor).
+template <typename Class,
+          typename... Args,
+          detail::enable_if_t<std::is_constructible<Class, Args...>::value, int> = 0>
+inline Class *construct_or_initialize(Args &&...args) {
+    return new Class(std::forward<Args>(args)...);
+}
+template <typename Class,
+          typename... Args,
+          detail::enable_if_t<!std::is_constructible<Class, Args...>::value, int> = 0>
+inline Class *construct_or_initialize(Args &&...args) {
+    return new Class{std::forward<Args>(args)...};
+}
+
+// Attempts to constructs an alias using a `Alias(Cpp &&)` constructor.  This allows types with
+// an alias to provide only a single Cpp factory function as long as the Alias can be
+// constructed from an rvalue reference of the base Cpp type.  This means that Alias classes
+// can, when appropriate, simply define a `Alias(Cpp &&)` constructor rather than needing to
+// inherit all the base class constructors.
+template <typename Class>
+void construct_alias_from_cpp(std::true_type /*is_alias_constructible*/,
+                              value_and_holder &v_h,
+                              Cpp<Class> &&base) {
+    v_h.value_ptr() = new Alias<Class>(std::move(base));
+}
+template <typename Class>
+[[noreturn]] void construct_alias_from_cpp(std::false_type /*!is_alias_constructible*/,
+                                           value_and_holder &,
+                                           Cpp<Class> &&) {
+    throw type_error("pybind11::init(): unable to convert returned instance to required "
+                     "alias class: no `Alias<Class>(Class &&)` constructor available");
+}
+
+// Error-generating fallback for factories that don't match one of the below construction
+// mechanisms.
+template <typename Class>
+void construct(...) {
+    static_assert(!std::is_same<Class, Class>::value /* always false */,
+                  "pybind11::init(): init function must return a compatible pointer, "
+                  "holder, or value");
+}
+
+// Pointer return v1: the factory function returns a class pointer for a registered class.
+// If we don't need an alias (because this class doesn't have one, or because the final type is
+// inherited on the Python side) we can simply take over ownership.  Otherwise we need to try to
+// construct an Alias from the returned base instance.
+template <typename Class>
+void construct(value_and_holder &v_h, Cpp<Class> *ptr, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    no_nullptr(ptr);
+    if (Class::has_alias && need_alias && !is_alias<Class>(ptr)) {
+        // We're going to try to construct an alias by moving the cpp type.  Whether or not
+        // that succeeds, we still need to destroy the original cpp pointer (either the
+        // moved away leftover, if the alias construction works, or the value itself if we
+        // throw an error), but we can't just call `delete ptr`: it might have a special
+        // deleter, or might be shared_from_this.  So we construct a holder around it as if
+        // it was a normal instance, then steal the holder away into a local variable; thus
+        // the holder and destruction happens when we leave the C++ scope, and the holder
+        // class gets to handle the destruction however it likes.
+        v_h.value_ptr() = ptr;
+        v_h.set_instance_registered(true); // Trick to prevent init_instance from registering it
+        // DANGER ZONE BEGIN: exceptions will leave v_h in an invalid state.
+        v_h.type->init_instance(v_h.inst, nullptr);                        // Set up the holder
+        Holder<Class> temp_holder(std::move(v_h.holder<Holder<Class>>())); // Steal the holder
+        v_h.type->dealloc(v_h); // Destroys the moved-out holder remains, resets value ptr to null
+        v_h.set_instance_registered(false);
+        // DANGER ZONE END.
+
+        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(*ptr));
+    } else {
+        // Otherwise the type isn't inherited, so we don't need an Alias
+        v_h.value_ptr() = ptr;
+    }
+}
+
+// Pointer return v2: a factory that always returns an alias instance ptr.  We simply take over
+// ownership of the pointer.
+template <typename Class, enable_if_t<Class::has_alias, int> = 0>
+void construct(value_and_holder &v_h, Alias<Class> *alias_ptr, bool) {
+    no_nullptr(alias_ptr);
+    v_h.value_ptr() = static_cast<Cpp<Class> *>(alias_ptr);
+}
+
+// Holder return: copy its pointer, and move or copy the returned holder into the new instance's
+// holder.  This also handles types like std::shared_ptr<T> and std::unique_ptr<T> where T is a
+// derived type (through those holder's implicit conversion from derived class holder
+// constructors).
+template <typename Class>
+void construct(value_and_holder &v_h, Holder<Class> holder, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    auto *ptr = holder_helper<Holder<Class>>::get(holder);
+    no_nullptr(ptr);
+    // If we need an alias, check that the held pointer is actually an alias instance
+    if (Class::has_alias && need_alias && !is_alias<Class>(ptr)) {
+        throw type_error("pybind11::init(): construction failed: returned holder-wrapped instance "
+                         "is not an alias instance");
+    }
+
+    v_h.value_ptr() = ptr;
+    v_h.type->init_instance(v_h.inst, &holder);
+}
+
+// return-by-value version 1: returning a cpp class by value.  If the class has an alias and an
+// alias is required the alias must have an `Alias(Cpp &&)` constructor so that we can construct
+// the alias from the base when needed (i.e. because of Python-side inheritance).  When we don't
+// need it, we simply move-construct the cpp value into a new instance.
+template <typename Class>
+void construct(value_and_holder &v_h, Cpp<Class> &&result, bool need_alias) {
+    PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(need_alias);
+    static_assert(is_move_constructible<Cpp<Class>>::value,
+                  "pybind11::init() return-by-value factory function requires a movable class");
+    if (Class::has_alias && need_alias) {
+        construct_alias_from_cpp<Class>(is_alias_constructible<Class>{}, v_h, std::move(result));
+    } else {
+        v_h.value_ptr() = new Cpp<Class>(std::move(result));
+    }
+}
+
+// return-by-value version 2: returning a value of the alias type itself.  We move-construct an
+// Alias instance (even if no the python-side inheritance is involved).  The is intended for
+// cases where Alias initialization is always desired.
+template <typename Class>
+void construct(value_and_holder &v_h, Alias<Class> &&result, bool) {
+    static_assert(
+        is_move_constructible<Alias<Class>>::value,
+        "pybind11::init() return-by-alias-value factory function requires a movable alias class");
+    v_h.value_ptr() = new Alias<Class>(std::move(result));
+}
+
+// Implementing class for py::init<...>()
+template <typename... Args>
+struct constructor {
+    template <typename Class, typename... Extra, enable_if_t<!Class::has_alias, int> = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr() = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+
+    template <
+        typename Class,
+        typename... Extra,
+        enable_if_t<Class::has_alias && std::is_constructible<Cpp<Class>, Args...>::value, int>
+        = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                if (Py_TYPE(v_h.inst) == v_h.type->type) {
+                    v_h.value_ptr()
+                        = construct_or_initialize<Cpp<Class>>(std::forward<Args>(args)...);
+                } else {
+                    v_h.value_ptr()
+                        = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+                }
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+
+    template <
+        typename Class,
+        typename... Extra,
+        enable_if_t<Class::has_alias && !std::is_constructible<Cpp<Class>, Args...>::value, int>
+        = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr()
+                    = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Implementing class for py::init_alias<...>()
+template <typename... Args>
+struct alias_constructor {
+    template <
+        typename Class,
+        typename... Extra,
+        enable_if_t<Class::has_alias && std::is_constructible<Alias<Class>, Args...>::value, int>
+        = 0>
+    static void execute(Class &cl, const Extra &...extra) {
+        cl.def(
+            "__init__",
+            [](value_and_holder &v_h, Args... args) {
+                v_h.value_ptr()
+                    = construct_or_initialize<Alias<Class>>(std::forward<Args>(args)...);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Implementation class for py::init(Func) and py::init(Func, AliasFunc)
+template <typename CFunc,
+          typename AFunc = void_type (*)(),
+          typename = function_signature_t<CFunc>,
+          typename = function_signature_t<AFunc>>
+struct factory;
+
+// Specialization for py::init(Func)
+template <typename Func, typename Return, typename... Args>
+struct factory<Func, void_type (*)(), Return(Args...)> {
+    remove_reference_t<Func> class_factory;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    factory(Func &&f) : class_factory(std::forward<Func>(f)) {}
+
+    // The given class either has no alias or has no separate alias factory;
+    // this always constructs the class itself.  If the class is registered with an alias
+    // type and an alias instance is needed (i.e. because the final type is a Python class
+    // inheriting from the C++ type) the returned value needs to either already be an alias
+    // instance, or the alias needs to be constructible from a `Class &&` argument.
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__init__",
+            [func = std::move(class_factory)]
+#else
+        auto &func = class_factory;
+        cl.def(
+            "__init__",
+            [func]
+#endif
+            (value_and_holder &v_h, Args... args) {
+                construct<Class>(
+                    v_h, func(std::forward<Args>(args)...), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+// Specialization for py::init(Func, AliasFunc)
+template <typename CFunc,
+          typename AFunc,
+          typename CReturn,
+          typename... CArgs,
+          typename AReturn,
+          typename... AArgs>
+struct factory<CFunc, AFunc, CReturn(CArgs...), AReturn(AArgs...)> {
+    static_assert(sizeof...(CArgs) == sizeof...(AArgs),
+                  "pybind11::init(class_factory, alias_factory): class and alias factories "
+                  "must have identical argument signatures");
+    static_assert(all_of<std::is_same<CArgs, AArgs>...>::value,
+                  "pybind11::init(class_factory, alias_factory): class and alias factories "
+                  "must have identical argument signatures");
+
+    remove_reference_t<CFunc> class_factory;
+    remove_reference_t<AFunc> alias_factory;
+
+    factory(CFunc &&c, AFunc &&a)
+        : class_factory(std::forward<CFunc>(c)), alias_factory(std::forward<AFunc>(a)) {}
+
+    // The class factory is called when the `self` type passed to `__init__` is the direct
+    // class (i.e. not inherited), the alias factory when `self` is a Python-side subtype.
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+        static_assert(Class::has_alias,
+                      "The two-argument version of `py::init()` can "
+                      "only be used if the class has an alias");
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__init__",
+            [class_func = std::move(class_factory), alias_func = std::move(alias_factory)]
+#else
+        auto &class_func = class_factory;
+        auto &alias_func = alias_factory;
+        cl.def(
+            "__init__",
+            [class_func, alias_func]
+#endif
+            (value_and_holder &v_h, CArgs... args) {
+                if (Py_TYPE(v_h.inst) == v_h.type->type) {
+                    // If the instance type equals the registered type we don't have inheritance,
+                    // so don't need the alias and can construct using the class function:
+                    construct<Class>(v_h, class_func(std::forward<CArgs>(args)...), false);
+                } else {
+                    construct<Class>(v_h, alias_func(std::forward<CArgs>(args)...), true);
+                }
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+/// Set just the C++ state. Same as `__init__`.
+template <typename Class, typename T>
+void setstate(value_and_holder &v_h, T &&result, bool need_alias) {
+    construct<Class>(v_h, std::forward<T>(result), need_alias);
+}
+
+/// Set both the C++ and Python states
+template <typename Class,
+          typename T,
+          typename O,
+          enable_if_t<std::is_convertible<O, handle>::value, int> = 0>
+void setstate(value_and_holder &v_h, std::pair<T, O> &&result, bool need_alias) {
+    construct<Class>(v_h, std::move(result.first), need_alias);
+    auto d = handle(result.second);
+    if (PyDict_Check(d.ptr()) && PyDict_Size(d.ptr()) == 0) {
+        // Skipping setattr below, to not force use of py::dynamic_attr() for Class unnecessarily.
+        // See PR #2972 for details.
+        return;
+    }
+    setattr((PyObject *) v_h.inst, "__dict__", d);
+}
+
+/// Implementation for py::pickle(GetState, SetState)
+template <typename Get,
+          typename Set,
+          typename = function_signature_t<Get>,
+          typename = function_signature_t<Set>>
+struct pickle_factory;
+
+template <typename Get,
+          typename Set,
+          typename RetState,
+          typename Self,
+          typename NewInstance,
+          typename ArgState>
+struct pickle_factory<Get, Set, RetState(Self), NewInstance(ArgState)> {
+    static_assert(std::is_same<intrinsic_t<RetState>, intrinsic_t<ArgState>>::value,
+                  "The type returned by `__getstate__` must be the same "
+                  "as the argument accepted by `__setstate__`");
+
+    remove_reference_t<Get> get;
+    remove_reference_t<Set> set;
+
+    pickle_factory(Get get, Set set) : get(std::forward<Get>(get)), set(std::forward<Set>(set)) {}
+
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) && {
+        cl.def("__getstate__", std::move(get));
+
+#if defined(PYBIND11_CPP14)
+        cl.def(
+            "__setstate__",
+            [func = std::move(set)]
+#else
+        auto &func = set;
+        cl.def(
+            "__setstate__",
+            [func]
+#endif
+            (value_and_holder &v_h, ArgState state) {
+                setstate<Class>(
+                    v_h, func(std::forward<ArgState>(state)), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            is_new_style_constructor(),
+            extra...);
+    }
+};
+
+PYBIND11_NAMESPACE_END(initimpl)
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/internals.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/internals.h
new file mode 100644
index 0000000000000000000000000000000000000000..232bc32d8233af46eab0ef1ae09d29e0bf9fcd9e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/internals.h
@@ -0,0 +1,766 @@
+/*
+    pybind11/detail/internals.h: Internal data structure and related functions
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "common.h"
+
+#if defined(PYBIND11_SIMPLE_GIL_MANAGEMENT)
+#    include <pybind11/gil.h>
+#endif
+
+#include <pybind11/pytypes.h>
+
+#include <exception>
+#include <mutex>
+#include <thread>
+
+/// Tracks the `internals` and `type_info` ABI version independent of the main library version.
+///
+/// Some portions of the code use an ABI that is conditional depending on this
+/// version number.  That allows ABI-breaking changes to be "pre-implemented".
+/// Once the default version number is incremented, the conditional logic that
+/// no longer applies can be removed.  Additionally, users that need not
+/// maintain ABI compatibility can increase the version number in order to take
+/// advantage of any functionality/efficiency improvements that depend on the
+/// newer ABI.
+///
+/// WARNING: If you choose to manually increase the ABI version, note that
+/// pybind11 may not be tested as thoroughly with a non-default ABI version, and
+/// further ABI-incompatible changes may be made before the ABI is officially
+/// changed to the new version.
+#ifndef PYBIND11_INTERNALS_VERSION
+#    if PY_VERSION_HEX >= 0x030C0000 || defined(_MSC_VER)
+// Version bump for Python 3.12+, before first 3.12 beta release.
+// Version bump for MSVC piggy-backed on PR #4779. See comments there.
+#        define PYBIND11_INTERNALS_VERSION 5
+#    else
+#        define PYBIND11_INTERNALS_VERSION 4
+#    endif
+#endif
+
+// This requirement is mainly to reduce the support burden (see PR #4570).
+static_assert(PY_VERSION_HEX < 0x030C0000 || PYBIND11_INTERNALS_VERSION >= 5,
+              "pybind11 ABI version 5 is the minimum for Python 3.12+");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+using ExceptionTranslator = void (*)(std::exception_ptr);
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+constexpr const char *internals_function_record_capsule_name = "pybind11_function_record_capsule";
+
+// Forward declarations
+inline PyTypeObject *make_static_property_type();
+inline PyTypeObject *make_default_metaclass();
+inline PyObject *make_object_base_type(PyTypeObject *metaclass);
+
+// The old Python Thread Local Storage (TLS) API is deprecated in Python 3.7 in favor of the new
+// Thread Specific Storage (TSS) API.
+// Avoid unnecessary allocation of `Py_tss_t`, since we cannot use
+// `Py_LIMITED_API` anyway.
+#if PYBIND11_INTERNALS_VERSION > 4
+#    define PYBIND11_TLS_KEY_REF Py_tss_t &
+#    if defined(__clang__)
+#        define PYBIND11_TLS_KEY_INIT(var)                                                        \
+            _Pragma("clang diagnostic push")                                         /**/         \
+                _Pragma("clang diagnostic ignored \"-Wmissing-field-initializers\"") /**/         \
+                Py_tss_t var                                                                      \
+                = Py_tss_NEEDS_INIT;                                                              \
+            _Pragma("clang diagnostic pop")
+#    elif defined(__GNUC__) && !defined(__INTEL_COMPILER)
+#        define PYBIND11_TLS_KEY_INIT(var)                                                        \
+            _Pragma("GCC diagnostic push")                                         /**/           \
+                _Pragma("GCC diagnostic ignored \"-Wmissing-field-initializers\"") /**/           \
+                Py_tss_t var                                                                      \
+                = Py_tss_NEEDS_INIT;                                                              \
+            _Pragma("GCC diagnostic pop")
+#    else
+#        define PYBIND11_TLS_KEY_INIT(var) Py_tss_t var = Py_tss_NEEDS_INIT;
+#    endif
+#    define PYBIND11_TLS_KEY_CREATE(var) (PyThread_tss_create(&(var)) == 0)
+#    define PYBIND11_TLS_GET_VALUE(key) PyThread_tss_get(&(key))
+#    define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_tss_set(&(key), (value))
+#    define PYBIND11_TLS_DELETE_VALUE(key) PyThread_tss_set(&(key), nullptr)
+#    define PYBIND11_TLS_FREE(key) PyThread_tss_delete(&(key))
+#else
+#    define PYBIND11_TLS_KEY_REF Py_tss_t *
+#    define PYBIND11_TLS_KEY_INIT(var) Py_tss_t *var = nullptr;
+#    define PYBIND11_TLS_KEY_CREATE(var)                                                          \
+        (((var) = PyThread_tss_alloc()) != nullptr && (PyThread_tss_create((var)) == 0))
+#    define PYBIND11_TLS_GET_VALUE(key) PyThread_tss_get((key))
+#    define PYBIND11_TLS_REPLACE_VALUE(key, value) PyThread_tss_set((key), (value))
+#    define PYBIND11_TLS_DELETE_VALUE(key) PyThread_tss_set((key), nullptr)
+#    define PYBIND11_TLS_FREE(key) PyThread_tss_free(key)
+#endif
+
+// Python loads modules by default with dlopen with the RTLD_LOCAL flag; under libc++ and possibly
+// other STLs, this means `typeid(A)` from one module won't equal `typeid(A)` from another module
+// even when `A` is the same, non-hidden-visibility type (e.g. from a common include).  Under
+// libstdc++, this doesn't happen: equality and the type_index hash are based on the type name,
+// which works.  If not under a known-good stl, provide our own name-based hash and equality
+// functions that use the type name.
+#if (PYBIND11_INTERNALS_VERSION <= 4 && defined(__GLIBCXX__))                                     \
+    || (PYBIND11_INTERNALS_VERSION >= 5 && !defined(_LIBCPP_VERSION))
+inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) { return lhs == rhs; }
+using type_hash = std::hash<std::type_index>;
+using type_equal_to = std::equal_to<std::type_index>;
+#else
+inline bool same_type(const std::type_info &lhs, const std::type_info &rhs) {
+    return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
+}
+
+struct type_hash {
+    size_t operator()(const std::type_index &t) const {
+        size_t hash = 5381;
+        const char *ptr = t.name();
+        while (auto c = static_cast<unsigned char>(*ptr++)) {
+            hash = (hash * 33) ^ c;
+        }
+        return hash;
+    }
+};
+
+struct type_equal_to {
+    bool operator()(const std::type_index &lhs, const std::type_index &rhs) const {
+        return lhs.name() == rhs.name() || std::strcmp(lhs.name(), rhs.name()) == 0;
+    }
+};
+#endif
+
+template <typename value_type>
+using type_map = std::unordered_map<std::type_index, value_type, type_hash, type_equal_to>;
+
+struct override_hash {
+    inline size_t operator()(const std::pair<const PyObject *, const char *> &v) const {
+        size_t value = std::hash<const void *>()(v.first);
+        value ^= std::hash<const void *>()(v.second) + 0x9e3779b9 + (value << 6) + (value >> 2);
+        return value;
+    }
+};
+
+using instance_map = std::unordered_multimap<const void *, instance *>;
+
+#ifdef Py_GIL_DISABLED
+// Wrapper around PyMutex to provide BasicLockable semantics
+class pymutex {
+    PyMutex mutex;
+
+public:
+    pymutex() : mutex({}) {}
+    void lock() { PyMutex_Lock(&mutex); }
+    void unlock() { PyMutex_Unlock(&mutex); }
+};
+
+// Instance map shards are used to reduce mutex contention in free-threaded Python.
+struct instance_map_shard {
+    instance_map registered_instances;
+    pymutex mutex;
+    // alignas(64) would be better, but causes compile errors in macOS before 10.14 (see #5200)
+    char padding[64 - (sizeof(instance_map) + sizeof(pymutex)) % 64];
+};
+
+static_assert(sizeof(instance_map_shard) % 64 == 0,
+              "instance_map_shard size is not a multiple of 64 bytes");
+#endif
+
+/// Internal data structure used to track registered instances and types.
+/// Whenever binary incompatible changes are made to this structure,
+/// `PYBIND11_INTERNALS_VERSION` must be incremented.
+struct internals {
+#ifdef Py_GIL_DISABLED
+    pymutex mutex;
+#endif
+    // std::type_index -> pybind11's type information
+    type_map<type_info *> registered_types_cpp;
+    // PyTypeObject* -> base type_info(s)
+    std::unordered_map<PyTypeObject *, std::vector<type_info *>> registered_types_py;
+#ifdef Py_GIL_DISABLED
+    std::unique_ptr<instance_map_shard[]> instance_shards; // void * -> instance*
+    size_t instance_shards_mask;
+#else
+    instance_map registered_instances; // void * -> instance*
+#endif
+    std::unordered_set<std::pair<const PyObject *, const char *>, override_hash>
+        inactive_override_cache;
+    type_map<std::vector<bool (*)(PyObject *, void *&)>> direct_conversions;
+    std::unordered_map<const PyObject *, std::vector<PyObject *>> patients;
+    std::forward_list<ExceptionTranslator> registered_exception_translators;
+    std::unordered_map<std::string, void *> shared_data; // Custom data to be shared across
+                                                         // extensions
+#if PYBIND11_INTERNALS_VERSION == 4
+    std::vector<PyObject *> unused_loader_patient_stack_remove_at_v5;
+#endif
+    std::forward_list<std::string> static_strings; // Stores the std::strings backing
+                                                   // detail::c_str()
+    PyTypeObject *static_property_type;
+    PyTypeObject *default_metaclass;
+    PyObject *instance_base;
+    // Unused if PYBIND11_SIMPLE_GIL_MANAGEMENT is defined:
+    PYBIND11_TLS_KEY_INIT(tstate)
+#if PYBIND11_INTERNALS_VERSION > 4
+    PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+#endif // PYBIND11_INTERNALS_VERSION > 4
+    // Unused if PYBIND11_SIMPLE_GIL_MANAGEMENT is defined:
+    PyInterpreterState *istate = nullptr;
+
+#if PYBIND11_INTERNALS_VERSION > 4
+    // Note that we have to use a std::string to allocate memory to ensure a unique address
+    // We want unique addresses since we use pointer equality to compare function records
+    std::string function_record_capsule_name = internals_function_record_capsule_name;
+#endif
+
+    internals() = default;
+    internals(const internals &other) = delete;
+    internals &operator=(const internals &other) = delete;
+    ~internals() {
+#if PYBIND11_INTERNALS_VERSION > 4
+        PYBIND11_TLS_FREE(loader_life_support_tls_key);
+#endif // PYBIND11_INTERNALS_VERSION > 4
+
+        // This destructor is called *after* Py_Finalize() in finalize_interpreter().
+        // That *SHOULD BE* fine. The following details what happens when PyThread_tss_free is
+        // called. PYBIND11_TLS_FREE is PyThread_tss_free on python 3.7+. On older python, it does
+        // nothing. PyThread_tss_free calls PyThread_tss_delete and PyMem_RawFree.
+        // PyThread_tss_delete just calls TlsFree (on Windows) or pthread_key_delete (on *NIX).
+        // Neither of those have anything to do with CPython internals. PyMem_RawFree *requires*
+        // that the `tstate` be allocated with the CPython allocator.
+        PYBIND11_TLS_FREE(tstate);
+    }
+};
+
+/// Additional type information which does not fit into the PyTypeObject.
+/// Changes to this struct also require bumping `PYBIND11_INTERNALS_VERSION`.
+struct type_info {
+    PyTypeObject *type;
+    const std::type_info *cpptype;
+    size_t type_size, type_align, holder_size_in_ptrs;
+    void *(*operator_new)(size_t);
+    void (*init_instance)(instance *, const void *);
+    void (*dealloc)(value_and_holder &v_h);
+    std::vector<PyObject *(*) (PyObject *, PyTypeObject *)> implicit_conversions;
+    std::vector<std::pair<const std::type_info *, void *(*) (void *)>> implicit_casts;
+    std::vector<bool (*)(PyObject *, void *&)> *direct_conversions;
+    buffer_info *(*get_buffer)(PyObject *, void *) = nullptr;
+    void *get_buffer_data = nullptr;
+    void *(*module_local_load)(PyObject *, const type_info *) = nullptr;
+    /* A simple type never occurs as a (direct or indirect) parent
+     * of a class that makes use of multiple inheritance.
+     * A type can be simple even if it has non-simple ancestors as long as it has no descendants.
+     */
+    bool simple_type : 1;
+    /* True if there is no multiple inheritance in this type's inheritance tree */
+    bool simple_ancestors : 1;
+    /* for base vs derived holder_type checks */
+    bool default_holder : 1;
+    /* true if this is a type registered with py::module_local */
+    bool module_local : 1;
+};
+
+/// On MSVC, debug and release builds are not ABI-compatible!
+#if defined(_MSC_VER) && defined(_DEBUG)
+#    define PYBIND11_BUILD_TYPE "_debug"
+#else
+#    define PYBIND11_BUILD_TYPE ""
+#endif
+
+/// Let's assume that different compilers are ABI-incompatible.
+/// A user can manually set this string if they know their
+/// compiler is compatible.
+#ifndef PYBIND11_COMPILER_TYPE
+#    if defined(_MSC_VER)
+#        define PYBIND11_COMPILER_TYPE "_msvc"
+#    elif defined(__INTEL_COMPILER)
+#        define PYBIND11_COMPILER_TYPE "_icc"
+#    elif defined(__clang__)
+#        define PYBIND11_COMPILER_TYPE "_clang"
+#    elif defined(__PGI)
+#        define PYBIND11_COMPILER_TYPE "_pgi"
+#    elif defined(__MINGW32__)
+#        define PYBIND11_COMPILER_TYPE "_mingw"
+#    elif defined(__CYGWIN__)
+#        define PYBIND11_COMPILER_TYPE "_gcc_cygwin"
+#    elif defined(__GNUC__)
+#        define PYBIND11_COMPILER_TYPE "_gcc"
+#    else
+#        define PYBIND11_COMPILER_TYPE "_unknown"
+#    endif
+#endif
+
+/// Also standard libs
+#ifndef PYBIND11_STDLIB
+#    if defined(_LIBCPP_VERSION)
+#        define PYBIND11_STDLIB "_libcpp"
+#    elif defined(__GLIBCXX__) || defined(__GLIBCPP__)
+#        define PYBIND11_STDLIB "_libstdcpp"
+#    else
+#        define PYBIND11_STDLIB ""
+#    endif
+#endif
+
+/// On Linux/OSX, changes in __GXX_ABI_VERSION__ indicate ABI incompatibility.
+/// On MSVC, changes in _MSC_VER may indicate ABI incompatibility (#2898).
+#ifndef PYBIND11_BUILD_ABI
+#    if defined(__GXX_ABI_VERSION)
+#        define PYBIND11_BUILD_ABI "_cxxabi" PYBIND11_TOSTRING(__GXX_ABI_VERSION)
+#    elif defined(_MSC_VER)
+#        define PYBIND11_BUILD_ABI "_mscver" PYBIND11_TOSTRING(_MSC_VER)
+#    else
+#        define PYBIND11_BUILD_ABI ""
+#    endif
+#endif
+
+#ifndef PYBIND11_INTERNALS_KIND
+#    define PYBIND11_INTERNALS_KIND ""
+#endif
+
+#define PYBIND11_PLATFORM_ABI_ID                                                                  \
+    PYBIND11_INTERNALS_KIND PYBIND11_COMPILER_TYPE PYBIND11_STDLIB PYBIND11_BUILD_ABI             \
+        PYBIND11_BUILD_TYPE
+
+#define PYBIND11_INTERNALS_ID                                                                     \
+    "__pybind11_internals_v" PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION)                        \
+        PYBIND11_PLATFORM_ABI_ID "__"
+
+#define PYBIND11_MODULE_LOCAL_ID                                                                  \
+    "__pybind11_module_local_v" PYBIND11_TOSTRING(PYBIND11_INTERNALS_VERSION)                     \
+        PYBIND11_PLATFORM_ABI_ID "__"
+
+/// Each module locally stores a pointer to the `internals` data. The data
+/// itself is shared among modules with the same `PYBIND11_INTERNALS_ID`.
+inline internals **&get_internals_pp() {
+    static internals **internals_pp = nullptr;
+    return internals_pp;
+}
+
+// forward decl
+inline void translate_exception(std::exception_ptr);
+
+template <class T,
+          enable_if_t<std::is_same<std::nested_exception, remove_cvref_t<T>>::value, int> = 0>
+bool handle_nested_exception(const T &exc, const std::exception_ptr &p) {
+    std::exception_ptr nested = exc.nested_ptr();
+    if (nested != nullptr && nested != p) {
+        translate_exception(nested);
+        return true;
+    }
+    return false;
+}
+
+template <class T,
+          enable_if_t<!std::is_same<std::nested_exception, remove_cvref_t<T>>::value, int> = 0>
+bool handle_nested_exception(const T &exc, const std::exception_ptr &p) {
+    if (const auto *nep = dynamic_cast<const std::nested_exception *>(std::addressof(exc))) {
+        return handle_nested_exception(*nep, p);
+    }
+    return false;
+}
+
+inline bool raise_err(PyObject *exc_type, const char *msg) {
+    if (PyErr_Occurred()) {
+        raise_from(exc_type, msg);
+        return true;
+    }
+    set_error(exc_type, msg);
+    return false;
+}
+
+inline void translate_exception(std::exception_ptr p) {
+    if (!p) {
+        return;
+    }
+    try {
+        std::rethrow_exception(p);
+    } catch (error_already_set &e) {
+        handle_nested_exception(e, p);
+        e.restore();
+        return;
+    } catch (const builtin_exception &e) {
+        // Could not use template since it's an abstract class.
+        if (const auto *nep = dynamic_cast<const std::nested_exception *>(std::addressof(e))) {
+            handle_nested_exception(*nep, p);
+        }
+        e.set_error();
+        return;
+    } catch (const std::bad_alloc &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_MemoryError, e.what());
+        return;
+    } catch (const std::domain_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::invalid_argument &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::length_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::out_of_range &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_IndexError, e.what());
+        return;
+    } catch (const std::range_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_ValueError, e.what());
+        return;
+    } catch (const std::overflow_error &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_OverflowError, e.what());
+        return;
+    } catch (const std::exception &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_RuntimeError, e.what());
+        return;
+    } catch (const std::nested_exception &e) {
+        handle_nested_exception(e, p);
+        raise_err(PyExc_RuntimeError, "Caught an unknown nested exception!");
+        return;
+    } catch (...) {
+        raise_err(PyExc_RuntimeError, "Caught an unknown exception!");
+        return;
+    }
+}
+
+#if !defined(__GLIBCXX__)
+inline void translate_local_exception(std::exception_ptr p) {
+    try {
+        if (p) {
+            std::rethrow_exception(p);
+        }
+    } catch (error_already_set &e) {
+        e.restore();
+        return;
+    } catch (const builtin_exception &e) {
+        e.set_error();
+        return;
+    }
+}
+#endif
+
+inline object get_python_state_dict() {
+    object state_dict;
+#if PYBIND11_INTERNALS_VERSION <= 4 || PY_VERSION_HEX < 0x03080000 || defined(PYPY_VERSION)
+    state_dict = reinterpret_borrow<object>(PyEval_GetBuiltins());
+#else
+#    if PY_VERSION_HEX < 0x03090000
+    PyInterpreterState *istate = _PyInterpreterState_Get();
+#    else
+    PyInterpreterState *istate = PyInterpreterState_Get();
+#    endif
+    if (istate) {
+        state_dict = reinterpret_borrow<object>(PyInterpreterState_GetDict(istate));
+    }
+#endif
+    if (!state_dict) {
+        raise_from(PyExc_SystemError, "pybind11::detail::get_python_state_dict() FAILED");
+        throw error_already_set();
+    }
+    return state_dict;
+}
+
+inline object get_internals_obj_from_state_dict(handle state_dict) {
+    return reinterpret_steal<object>(
+        dict_getitemstringref(state_dict.ptr(), PYBIND11_INTERNALS_ID));
+}
+
+inline internals **get_internals_pp_from_capsule(handle obj) {
+    void *raw_ptr = PyCapsule_GetPointer(obj.ptr(), /*name=*/nullptr);
+    if (raw_ptr == nullptr) {
+        raise_from(PyExc_SystemError, "pybind11::detail::get_internals_pp_from_capsule() FAILED");
+        throw error_already_set();
+    }
+    return static_cast<internals **>(raw_ptr);
+}
+
+inline uint64_t round_up_to_next_pow2(uint64_t x) {
+    // Round-up to the next power of two.
+    // See https://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2
+    x--;
+    x |= (x >> 1);
+    x |= (x >> 2);
+    x |= (x >> 4);
+    x |= (x >> 8);
+    x |= (x >> 16);
+    x |= (x >> 32);
+    x++;
+    return x;
+}
+
+/// Return a reference to the current `internals` data
+PYBIND11_NOINLINE internals &get_internals() {
+    auto **&internals_pp = get_internals_pp();
+    if (internals_pp && *internals_pp) {
+        return **internals_pp;
+    }
+
+#if defined(PYBIND11_SIMPLE_GIL_MANAGEMENT)
+    gil_scoped_acquire gil;
+#else
+    // Ensure that the GIL is held since we will need to make Python calls.
+    // Cannot use py::gil_scoped_acquire here since that constructor calls get_internals.
+    struct gil_scoped_acquire_local {
+        gil_scoped_acquire_local() : state(PyGILState_Ensure()) {}
+        gil_scoped_acquire_local(const gil_scoped_acquire_local &) = delete;
+        gil_scoped_acquire_local &operator=(const gil_scoped_acquire_local &) = delete;
+        ~gil_scoped_acquire_local() { PyGILState_Release(state); }
+        const PyGILState_STATE state;
+    } gil;
+#endif
+    error_scope err_scope;
+
+    dict state_dict = get_python_state_dict();
+    if (object internals_obj = get_internals_obj_from_state_dict(state_dict)) {
+        internals_pp = get_internals_pp_from_capsule(internals_obj);
+    }
+    if (internals_pp && *internals_pp) {
+        // We loaded the internals through `state_dict`, which means that our `error_already_set`
+        // and `builtin_exception` may be different local classes than the ones set up in the
+        // initial exception translator, below, so add another for our local exception classes.
+        //
+        // libstdc++ doesn't require this (types there are identified only by name)
+        // libc++ with CPython doesn't require this (types are explicitly exported)
+        // libc++ with PyPy still need it, awaiting further investigation
+#if !defined(__GLIBCXX__)
+        (*internals_pp)->registered_exception_translators.push_front(&translate_local_exception);
+#endif
+    } else {
+        if (!internals_pp) {
+            internals_pp = new internals *();
+        }
+        auto *&internals_ptr = *internals_pp;
+        internals_ptr = new internals();
+
+        PyThreadState *tstate = PyThreadState_Get();
+        // NOLINTNEXTLINE(bugprone-assignment-in-if-condition)
+        if (!PYBIND11_TLS_KEY_CREATE(internals_ptr->tstate)) {
+            pybind11_fail("get_internals: could not successfully initialize the tstate TSS key!");
+        }
+        PYBIND11_TLS_REPLACE_VALUE(internals_ptr->tstate, tstate);
+
+#if PYBIND11_INTERNALS_VERSION > 4
+        // NOLINTNEXTLINE(bugprone-assignment-in-if-condition)
+        if (!PYBIND11_TLS_KEY_CREATE(internals_ptr->loader_life_support_tls_key)) {
+            pybind11_fail("get_internals: could not successfully initialize the "
+                          "loader_life_support TSS key!");
+        }
+#endif
+        internals_ptr->istate = tstate->interp;
+        state_dict[PYBIND11_INTERNALS_ID] = capsule(reinterpret_cast<void *>(internals_pp));
+        internals_ptr->registered_exception_translators.push_front(&translate_exception);
+        internals_ptr->static_property_type = make_static_property_type();
+        internals_ptr->default_metaclass = make_default_metaclass();
+        internals_ptr->instance_base = make_object_base_type(internals_ptr->default_metaclass);
+#ifdef Py_GIL_DISABLED
+        // Scale proportional to the number of cores. 2x is a heuristic to reduce contention.
+        auto num_shards
+            = static_cast<size_t>(round_up_to_next_pow2(2 * std::thread::hardware_concurrency()));
+        if (num_shards == 0) {
+            num_shards = 1;
+        }
+        internals_ptr->instance_shards.reset(new instance_map_shard[num_shards]);
+        internals_ptr->instance_shards_mask = num_shards - 1;
+#endif // Py_GIL_DISABLED
+    }
+    return **internals_pp;
+}
+
+// the internals struct (above) is shared between all the modules. local_internals are only
+// for a single module. Any changes made to internals may require an update to
+// PYBIND11_INTERNALS_VERSION, breaking backwards compatibility. local_internals is, by design,
+// restricted to a single module. Whether a module has local internals or not should not
+// impact any other modules, because the only things accessing the local internals is the
+// module that contains them.
+struct local_internals {
+    type_map<type_info *> registered_types_cpp;
+    std::forward_list<ExceptionTranslator> registered_exception_translators;
+#if PYBIND11_INTERNALS_VERSION == 4
+
+    // For ABI compatibility, we can't store the loader_life_support TLS key in
+    // the `internals` struct directly.  Instead, we store it in `shared_data` and
+    // cache a copy in `local_internals`.  If we allocated a separate TLS key for
+    // each instance of `local_internals`, we could end up allocating hundreds of
+    // TLS keys if hundreds of different pybind11 modules are loaded (which is a
+    // plausible number).
+    PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+
+    // Holds the shared TLS key for the loader_life_support stack.
+    struct shared_loader_life_support_data {
+        PYBIND11_TLS_KEY_INIT(loader_life_support_tls_key)
+        shared_loader_life_support_data() {
+            // NOLINTNEXTLINE(bugprone-assignment-in-if-condition)
+            if (!PYBIND11_TLS_KEY_CREATE(loader_life_support_tls_key)) {
+                pybind11_fail("local_internals: could not successfully initialize the "
+                              "loader_life_support TLS key!");
+            }
+        }
+        // We can't help but leak the TLS key, because Python never unloads extension modules.
+    };
+
+    local_internals() {
+        auto &internals = get_internals();
+        // Get or create the `loader_life_support_stack_key`.
+        auto &ptr = internals.shared_data["_life_support"];
+        if (!ptr) {
+            ptr = new shared_loader_life_support_data;
+        }
+        loader_life_support_tls_key
+            = static_cast<shared_loader_life_support_data *>(ptr)->loader_life_support_tls_key;
+    }
+#endif //  PYBIND11_INTERNALS_VERSION == 4
+};
+
+/// Works like `get_internals`, but for things which are locally registered.
+inline local_internals &get_local_internals() {
+    // Current static can be created in the interpreter finalization routine. If the later will be
+    // destroyed in another static variable destructor, creation of this static there will cause
+    // static deinitialization fiasco. In order to avoid it we avoid destruction of the
+    // local_internals static. One can read more about the problem and current solution here:
+    // https://google.github.io/styleguide/cppguide.html#Static_and_Global_Variables
+    static auto *locals = new local_internals();
+    return *locals;
+}
+
+#ifdef Py_GIL_DISABLED
+#    define PYBIND11_LOCK_INTERNALS(internals) std::unique_lock<pymutex> lock((internals).mutex)
+#else
+#    define PYBIND11_LOCK_INTERNALS(internals)
+#endif
+
+template <typename F>
+inline auto with_internals(const F &cb) -> decltype(cb(get_internals())) {
+    auto &internals = get_internals();
+    PYBIND11_LOCK_INTERNALS(internals);
+    return cb(internals);
+}
+
+inline std::uint64_t mix64(std::uint64_t z) {
+    // David Stafford's variant 13 of the MurmurHash3 finalizer popularized
+    // by the SplitMix PRNG.
+    // https://zimbry.blogspot.com/2011/09/better-bit-mixing-improving-on.html
+    z = (z ^ (z >> 30)) * 0xbf58476d1ce4e5b9;
+    z = (z ^ (z >> 27)) * 0x94d049bb133111eb;
+    return z ^ (z >> 31);
+}
+
+template <typename F>
+inline auto with_instance_map(const void *ptr,
+                              const F &cb) -> decltype(cb(std::declval<instance_map &>())) {
+    auto &internals = get_internals();
+
+#ifdef Py_GIL_DISABLED
+    // Hash address to compute shard, but ignore low bits. We'd like allocations
+    // from the same thread/core to map to the same shard and allocations from
+    // other threads/cores to map to other shards. Using the high bits is a good
+    // heuristic because memory allocators often have a per-thread
+    // arena/superblock/segment from which smaller allocations are served.
+    auto addr = reinterpret_cast<std::uintptr_t>(ptr);
+    auto hash = mix64(static_cast<std::uint64_t>(addr >> 20));
+    auto idx = static_cast<size_t>(hash & internals.instance_shards_mask);
+
+    auto &shard = internals.instance_shards[idx];
+    std::unique_lock<pymutex> lock(shard.mutex);
+    return cb(shard.registered_instances);
+#else
+    (void) ptr;
+    return cb(internals.registered_instances);
+#endif
+}
+
+// Returns the number of registered instances for testing purposes.  The result may not be
+// consistent if other threads are registering or unregistering instances concurrently.
+inline size_t num_registered_instances() {
+    auto &internals = get_internals();
+#ifdef Py_GIL_DISABLED
+    size_t count = 0;
+    for (size_t i = 0; i <= internals.instance_shards_mask; ++i) {
+        auto &shard = internals.instance_shards[i];
+        std::unique_lock<pymutex> lock(shard.mutex);
+        count += shard.registered_instances.size();
+    }
+    return count;
+#else
+    return internals.registered_instances.size();
+#endif
+}
+
+/// Constructs a std::string with the given arguments, stores it in `internals`, and returns its
+/// `c_str()`.  Such strings objects have a long storage duration -- the internal strings are only
+/// cleared when the program exits or after interpreter shutdown (when embedding), and so are
+/// suitable for c-style strings needed by Python internals (such as PyTypeObject's tp_name).
+template <typename... Args>
+const char *c_str(Args &&...args) {
+    // GCC 4.8 doesn't like parameter unpack within lambda capture, so use
+    // PYBIND11_LOCK_INTERNALS.
+    auto &internals = get_internals();
+    PYBIND11_LOCK_INTERNALS(internals);
+    auto &strings = internals.static_strings;
+    strings.emplace_front(std::forward<Args>(args)...);
+    return strings.front().c_str();
+}
+
+inline const char *get_function_record_capsule_name() {
+#if PYBIND11_INTERNALS_VERSION > 4
+    return get_internals().function_record_capsule_name.c_str();
+#else
+    return nullptr;
+#endif
+}
+
+// Determine whether or not the following capsule contains a pybind11 function record.
+// Note that we use `internals` to make sure that only ABI compatible records are touched.
+//
+// This check is currently used in two places:
+// - An important optimization in functional.h to avoid overhead in C++ -> Python -> C++
+// - The sibling feature of cpp_function to allow overloads
+inline bool is_function_record_capsule(const capsule &cap) {
+    // Pointer equality as we rely on internals() to ensure unique pointers
+    return cap.name() == get_function_record_capsule_name();
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Returns a named pointer that is shared among all extension modules (using the same
+/// pybind11 version) running in the current interpreter. Names starting with underscores
+/// are reserved for internal usage. Returns `nullptr` if no matching entry was found.
+PYBIND11_NOINLINE void *get_shared_data(const std::string &name) {
+    return detail::with_internals([&](detail::internals &internals) {
+        auto it = internals.shared_data.find(name);
+        return it != internals.shared_data.end() ? it->second : nullptr;
+    });
+}
+
+/// Set the shared data that can be later recovered by `get_shared_data()`.
+PYBIND11_NOINLINE void *set_shared_data(const std::string &name, void *data) {
+    return detail::with_internals([&](detail::internals &internals) {
+        internals.shared_data[name] = data;
+        return data;
+    });
+}
+
+/// Returns a typed reference to a shared data entry (by using `get_shared_data()`) if
+/// such entry exists. Otherwise, a new object of default-constructible type `T` is
+/// added to the shared data under the given name and a reference to it is returned.
+template <typename T>
+T &get_or_create_shared_data(const std::string &name) {
+    return *detail::with_internals([&](detail::internals &internals) {
+        auto it = internals.shared_data.find(name);
+        T *ptr = (T *) (it != internals.shared_data.end() ? it->second : nullptr);
+        if (!ptr) {
+            ptr = new T();
+            internals.shared_data[name] = ptr;
+        }
+        return ptr;
+    });
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/type_caster_base.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/type_caster_base.h
new file mode 100644
index 0000000000000000000000000000000000000000..e40e44ba6cc9a92d0e5a697a2478477f78320386
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/type_caster_base.h
@@ -0,0 +1,1195 @@
+/*
+    pybind11/detail/type_caster_base.h (originally first part of pybind11/cast.h)
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <pybind11/pytypes.h>
+
+#include "common.h"
+#include "cpp_conduit.h"
+#include "descr.h"
+#include "internals.h"
+#include "typeid.h"
+#include "value_and_holder.h"
+
+#include <cstdint>
+#include <cstring>
+#include <iterator>
+#include <new>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <typeinfo>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// A life support system for temporary objects created by `type_caster::load()`.
+/// Adding a patient will keep it alive up until the enclosing function returns.
+class loader_life_support {
+private:
+    loader_life_support *parent = nullptr;
+    std::unordered_set<PyObject *> keep_alive;
+
+    // Store stack pointer in thread-local storage.
+    static PYBIND11_TLS_KEY_REF get_stack_tls_key() {
+#if PYBIND11_INTERNALS_VERSION == 4
+        return get_local_internals().loader_life_support_tls_key;
+#else
+        return get_internals().loader_life_support_tls_key;
+#endif
+    }
+    static loader_life_support *get_stack_top() {
+        return static_cast<loader_life_support *>(PYBIND11_TLS_GET_VALUE(get_stack_tls_key()));
+    }
+    static void set_stack_top(loader_life_support *value) {
+        PYBIND11_TLS_REPLACE_VALUE(get_stack_tls_key(), value);
+    }
+
+public:
+    /// A new patient frame is created when a function is entered
+    loader_life_support() : parent{get_stack_top()} { set_stack_top(this); }
+
+    /// ... and destroyed after it returns
+    ~loader_life_support() {
+        if (get_stack_top() != this) {
+            pybind11_fail("loader_life_support: internal error");
+        }
+        set_stack_top(parent);
+        for (auto *item : keep_alive) {
+            Py_DECREF(item);
+        }
+    }
+
+    /// This can only be used inside a pybind11-bound function, either by `argument_loader`
+    /// at argument preparation time or by `py::cast()` at execution time.
+    PYBIND11_NOINLINE static void add_patient(handle h) {
+        loader_life_support *frame = get_stack_top();
+        if (!frame) {
+            // NOTE: It would be nice to include the stack frames here, as this indicates
+            // use of pybind11::cast<> outside the normal call framework, finding such
+            // a location is challenging. Developers could consider printing out
+            // stack frame addresses here using something like __builtin_frame_address(0)
+            throw cast_error("When called outside a bound function, py::cast() cannot "
+                             "do Python -> C++ conversions which require the creation "
+                             "of temporary values");
+        }
+
+        if (frame->keep_alive.insert(h.ptr()).second) {
+            Py_INCREF(h.ptr());
+        }
+    }
+};
+
+// Gets the cache entry for the given type, creating it if necessary.  The return value is the pair
+// returned by emplace, i.e. an iterator for the entry and a bool set to `true` if the entry was
+// just created.
+inline std::pair<decltype(internals::registered_types_py)::iterator, bool>
+all_type_info_get_cache(PyTypeObject *type);
+
+// Band-aid workaround to fix a subtle but serious bug in a minimalistic fashion. See PR #4762.
+inline void all_type_info_add_base_most_derived_first(std::vector<type_info *> &bases,
+                                                      type_info *addl_base) {
+    for (auto it = bases.begin(); it != bases.end(); it++) {
+        type_info *existing_base = *it;
+        if (PyType_IsSubtype(addl_base->type, existing_base->type) != 0) {
+            bases.insert(it, addl_base);
+            return;
+        }
+    }
+    bases.push_back(addl_base);
+}
+
+// Populates a just-created cache entry.
+PYBIND11_NOINLINE void all_type_info_populate(PyTypeObject *t, std::vector<type_info *> &bases) {
+    assert(bases.empty());
+    std::vector<PyTypeObject *> check;
+    for (handle parent : reinterpret_borrow<tuple>(t->tp_bases)) {
+        check.push_back((PyTypeObject *) parent.ptr());
+    }
+
+    auto const &type_dict = get_internals().registered_types_py;
+    for (size_t i = 0; i < check.size(); i++) {
+        auto *type = check[i];
+        // Ignore Python2 old-style class super type:
+        if (!PyType_Check((PyObject *) type)) {
+            continue;
+        }
+
+        // Check `type` in the current set of registered python types:
+        auto it = type_dict.find(type);
+        if (it != type_dict.end()) {
+            // We found a cache entry for it, so it's either pybind-registered or has pre-computed
+            // pybind bases, but we have to make sure we haven't already seen the type(s) before:
+            // we want to follow Python/virtual C++ rules that there should only be one instance of
+            // a common base.
+            for (auto *tinfo : it->second) {
+                // NB: Could use a second set here, rather than doing a linear search, but since
+                // having a large number of immediate pybind11-registered types seems fairly
+                // unlikely, that probably isn't worthwhile.
+                bool found = false;
+                for (auto *known : bases) {
+                    if (known == tinfo) {
+                        found = true;
+                        break;
+                    }
+                }
+                if (!found) {
+                    all_type_info_add_base_most_derived_first(bases, tinfo);
+                }
+            }
+        } else if (type->tp_bases) {
+            // It's some python type, so keep follow its bases classes to look for one or more
+            // registered types
+            if (i + 1 == check.size()) {
+                // When we're at the end, we can pop off the current element to avoid growing
+                // `check` when adding just one base (which is typical--i.e. when there is no
+                // multiple inheritance)
+                check.pop_back();
+                i--;
+            }
+            for (handle parent : reinterpret_borrow<tuple>(type->tp_bases)) {
+                check.push_back((PyTypeObject *) parent.ptr());
+            }
+        }
+    }
+}
+
+/**
+ * Extracts vector of type_info pointers of pybind-registered roots of the given Python type.  Will
+ * be just 1 pybind type for the Python type of a pybind-registered class, or for any Python-side
+ * derived class that uses single inheritance.  Will contain as many types as required for a Python
+ * class that uses multiple inheritance to inherit (directly or indirectly) from multiple
+ * pybind-registered classes.  Will be empty if neither the type nor any base classes are
+ * pybind-registered.
+ *
+ * The value is cached for the lifetime of the Python type.
+ */
+inline const std::vector<detail::type_info *> &all_type_info(PyTypeObject *type) {
+    auto ins = all_type_info_get_cache(type);
+    if (ins.second) {
+        // New cache entry: populate it
+        all_type_info_populate(type, ins.first->second);
+    }
+
+    return ins.first->second;
+}
+
+/**
+ * Gets a single pybind11 type info for a python type.  Returns nullptr if neither the type nor any
+ * ancestors are pybind11-registered.  Throws an exception if there are multiple bases--use
+ * `all_type_info` instead if you want to support multiple bases.
+ */
+PYBIND11_NOINLINE detail::type_info *get_type_info(PyTypeObject *type) {
+    const auto &bases = all_type_info(type);
+    if (bases.empty()) {
+        return nullptr;
+    }
+    if (bases.size() > 1) {
+        pybind11_fail(
+            "pybind11::detail::get_type_info: type has multiple pybind11-registered bases");
+    }
+    return bases.front();
+}
+
+inline detail::type_info *get_local_type_info(const std::type_index &tp) {
+    auto &locals = get_local_internals().registered_types_cpp;
+    auto it = locals.find(tp);
+    if (it != locals.end()) {
+        return it->second;
+    }
+    return nullptr;
+}
+
+inline detail::type_info *get_global_type_info(const std::type_index &tp) {
+    return with_internals([&](internals &internals) {
+        detail::type_info *type_info = nullptr;
+        auto &types = internals.registered_types_cpp;
+        auto it = types.find(tp);
+        if (it != types.end()) {
+            type_info = it->second;
+        }
+        return type_info;
+    });
+}
+
+/// Return the type info for a given C++ type; on lookup failure can either throw or return
+/// nullptr.
+PYBIND11_NOINLINE detail::type_info *get_type_info(const std::type_index &tp,
+                                                   bool throw_if_missing = false) {
+    if (auto *ltype = get_local_type_info(tp)) {
+        return ltype;
+    }
+    if (auto *gtype = get_global_type_info(tp)) {
+        return gtype;
+    }
+
+    if (throw_if_missing) {
+        std::string tname = tp.name();
+        detail::clean_type_id(tname);
+        pybind11_fail("pybind11::detail::get_type_info: unable to find type info for \""
+                      + std::move(tname) + '"');
+    }
+    return nullptr;
+}
+
+PYBIND11_NOINLINE handle get_type_handle(const std::type_info &tp, bool throw_if_missing) {
+    detail::type_info *type_info = get_type_info(tp, throw_if_missing);
+    return handle(type_info ? ((PyObject *) type_info->type) : nullptr);
+}
+
+// Searches the inheritance graph for a registered Python instance, using all_type_info().
+PYBIND11_NOINLINE handle find_registered_python_instance(void *src,
+                                                         const detail::type_info *tinfo) {
+    return with_instance_map(src, [&](instance_map &instances) {
+        auto it_instances = instances.equal_range(src);
+        for (auto it_i = it_instances.first; it_i != it_instances.second; ++it_i) {
+            for (auto *instance_type : detail::all_type_info(Py_TYPE(it_i->second))) {
+                if (instance_type && same_type(*instance_type->cpptype, *tinfo->cpptype)) {
+                    return handle((PyObject *) it_i->second).inc_ref();
+                }
+            }
+        }
+        return handle();
+    });
+}
+
+// Container for accessing and iterating over an instance's values/holders
+struct values_and_holders {
+private:
+    instance *inst;
+    using type_vec = std::vector<detail::type_info *>;
+    const type_vec &tinfo;
+
+public:
+    explicit values_and_holders(instance *inst)
+        : inst{inst}, tinfo(all_type_info(Py_TYPE(inst))) {}
+
+    explicit values_and_holders(PyObject *obj)
+        : inst{nullptr}, tinfo(all_type_info(Py_TYPE(obj))) {
+        if (!tinfo.empty()) {
+            inst = reinterpret_cast<instance *>(obj);
+        }
+    }
+
+    struct iterator {
+    private:
+        instance *inst = nullptr;
+        const type_vec *types = nullptr;
+        value_and_holder curr;
+        friend struct values_and_holders;
+        iterator(instance *inst, const type_vec *tinfo) : inst{inst}, types{tinfo} {
+            if (inst != nullptr) {
+                assert(!types->empty());
+                curr = value_and_holder(
+                    inst /* instance */,
+                    (*types)[0] /* type info */,
+                    0, /* vpos: (non-simple types only): the first vptr comes first */
+                    0 /* index */);
+            }
+        }
+        // Past-the-end iterator:
+        explicit iterator(size_t end) : curr(end) {}
+
+    public:
+        bool operator==(const iterator &other) const { return curr.index == other.curr.index; }
+        bool operator!=(const iterator &other) const { return curr.index != other.curr.index; }
+        iterator &operator++() {
+            if (!inst->simple_layout) {
+                curr.vh += 1 + (*types)[curr.index]->holder_size_in_ptrs;
+            }
+            ++curr.index;
+            curr.type = curr.index < types->size() ? (*types)[curr.index] : nullptr;
+            return *this;
+        }
+        value_and_holder &operator*() { return curr; }
+        value_and_holder *operator->() { return &curr; }
+    };
+
+    iterator begin() { return iterator(inst, &tinfo); }
+    iterator end() { return iterator(tinfo.size()); }
+
+    iterator find(const type_info *find_type) {
+        auto it = begin(), endit = end();
+        while (it != endit && it->type != find_type) {
+            ++it;
+        }
+        return it;
+    }
+
+    size_t size() { return tinfo.size(); }
+
+    // Band-aid workaround to fix a subtle but serious bug in a minimalistic fashion. See PR #4762.
+    bool is_redundant_value_and_holder(const value_and_holder &vh) {
+        for (size_t i = 0; i < vh.index; i++) {
+            if (PyType_IsSubtype(tinfo[i]->type, tinfo[vh.index]->type) != 0) {
+                return true;
+            }
+        }
+        return false;
+    }
+};
+
+/**
+ * Extracts C++ value and holder pointer references from an instance (which may contain multiple
+ * values/holders for python-side multiple inheritance) that match the given type.  Throws an error
+ * if the given type (or ValueType, if omitted) is not a pybind11 base of the given instance.  If
+ * `find_type` is omitted (or explicitly specified as nullptr) the first value/holder are returned,
+ * regardless of type (and the resulting .type will be nullptr).
+ *
+ * The returned object should be short-lived: in particular, it must not outlive the called-upon
+ * instance.
+ */
+PYBIND11_NOINLINE value_and_holder
+instance::get_value_and_holder(const type_info *find_type /*= nullptr default in common.h*/,
+                               bool throw_if_missing /*= true in common.h*/) {
+    // Optimize common case:
+    if (!find_type || Py_TYPE(this) == find_type->type) {
+        return value_and_holder(this, find_type, 0, 0);
+    }
+
+    detail::values_and_holders vhs(this);
+    auto it = vhs.find(find_type);
+    if (it != vhs.end()) {
+        return *it;
+    }
+
+    if (!throw_if_missing) {
+        return value_and_holder();
+    }
+
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+    pybind11_fail("pybind11::detail::instance::get_value_and_holder: `"
+                  + get_fully_qualified_tp_name(find_type->type)
+                  + "' is not a pybind11 base of the given `"
+                  + get_fully_qualified_tp_name(Py_TYPE(this)) + "' instance");
+#else
+    pybind11_fail(
+        "pybind11::detail::instance::get_value_and_holder: "
+        "type is not a pybind11 base of the given instance "
+        "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for type details)");
+#endif
+}
+
+PYBIND11_NOINLINE void instance::allocate_layout() {
+    const auto &tinfo = all_type_info(Py_TYPE(this));
+
+    const size_t n_types = tinfo.size();
+
+    if (n_types == 0) {
+        pybind11_fail(
+            "instance allocation failed: new instance has no pybind11-registered base types");
+    }
+
+    simple_layout
+        = n_types == 1 && tinfo.front()->holder_size_in_ptrs <= instance_simple_holder_in_ptrs();
+
+    // Simple path: no python-side multiple inheritance, and a small-enough holder
+    if (simple_layout) {
+        simple_value_holder[0] = nullptr;
+        simple_holder_constructed = false;
+        simple_instance_registered = false;
+    } else { // multiple base types or a too-large holder
+        // Allocate space to hold: [v1*][h1][v2*][h2]...[bb...] where [vN*] is a value pointer,
+        // [hN] is the (uninitialized) holder instance for value N, and [bb...] is a set of bool
+        // values that tracks whether each associated holder has been initialized.  Each [block] is
+        // padded, if necessary, to an integer multiple of sizeof(void *).
+        size_t space = 0;
+        for (auto *t : tinfo) {
+            space += 1;                      // value pointer
+            space += t->holder_size_in_ptrs; // holder instance
+        }
+        size_t flags_at = space;
+        space += size_in_ptrs(n_types); // status bytes (holder_constructed and
+                                        // instance_registered)
+
+        // Allocate space for flags, values, and holders, and initialize it to 0 (flags and values,
+        // in particular, need to be 0).  Use Python's memory allocation
+        // functions: Python is using pymalloc, which is designed to be
+        // efficient for small allocations like the one we're doing here;
+        // for larger allocations they are just wrappers around malloc.
+        // TODO: is this still true for pure Python 3.6?
+        nonsimple.values_and_holders = (void **) PyMem_Calloc(space, sizeof(void *));
+        if (!nonsimple.values_and_holders) {
+            throw std::bad_alloc();
+        }
+        nonsimple.status
+            = reinterpret_cast<std::uint8_t *>(&nonsimple.values_and_holders[flags_at]);
+    }
+    owned = true;
+}
+
+// NOLINTNEXTLINE(readability-make-member-function-const)
+PYBIND11_NOINLINE void instance::deallocate_layout() {
+    if (!simple_layout) {
+        PyMem_Free(reinterpret_cast<void *>(nonsimple.values_and_holders));
+    }
+}
+
+PYBIND11_NOINLINE bool isinstance_generic(handle obj, const std::type_info &tp) {
+    handle type = detail::get_type_handle(tp, false);
+    if (!type) {
+        return false;
+    }
+    return isinstance(obj, type);
+}
+
+PYBIND11_NOINLINE handle get_object_handle(const void *ptr, const detail::type_info *type) {
+    return with_instance_map(ptr, [&](instance_map &instances) {
+        auto range = instances.equal_range(ptr);
+        for (auto it = range.first; it != range.second; ++it) {
+            for (const auto &vh : values_and_holders(it->second)) {
+                if (vh.type == type) {
+                    return handle((PyObject *) it->second);
+                }
+            }
+        }
+        return handle();
+    });
+}
+
+inline PyThreadState *get_thread_state_unchecked() {
+#if defined(PYPY_VERSION)
+    return PyThreadState_GET();
+#elif PY_VERSION_HEX < 0x030D0000
+    return _PyThreadState_UncheckedGet();
+#else
+    return PyThreadState_GetUnchecked();
+#endif
+}
+
+// Forward declarations
+void keep_alive_impl(handle nurse, handle patient);
+inline PyObject *make_new_instance(PyTypeObject *type);
+
+class type_caster_generic {
+public:
+    PYBIND11_NOINLINE explicit type_caster_generic(const std::type_info &type_info)
+        : typeinfo(get_type_info(type_info)), cpptype(&type_info) {}
+
+    explicit type_caster_generic(const type_info *typeinfo)
+        : typeinfo(typeinfo), cpptype(typeinfo ? typeinfo->cpptype : nullptr) {}
+
+    bool load(handle src, bool convert) { return load_impl<type_caster_generic>(src, convert); }
+
+    PYBIND11_NOINLINE static handle cast(const void *_src,
+                                         return_value_policy policy,
+                                         handle parent,
+                                         const detail::type_info *tinfo,
+                                         void *(*copy_constructor)(const void *),
+                                         void *(*move_constructor)(const void *),
+                                         const void *existing_holder = nullptr) {
+        if (!tinfo) { // no type info: error will be set already
+            return handle();
+        }
+
+        void *src = const_cast<void *>(_src);
+        if (src == nullptr) {
+            return none().release();
+        }
+
+        if (handle registered_inst = find_registered_python_instance(src, tinfo)) {
+            return registered_inst;
+        }
+
+        auto inst = reinterpret_steal<object>(make_new_instance(tinfo->type));
+        auto *wrapper = reinterpret_cast<instance *>(inst.ptr());
+        wrapper->owned = false;
+        void *&valueptr = values_and_holders(wrapper).begin()->value_ptr();
+
+        switch (policy) {
+            case return_value_policy::automatic:
+            case return_value_policy::take_ownership:
+                valueptr = src;
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::automatic_reference:
+            case return_value_policy::reference:
+                valueptr = src;
+                wrapper->owned = false;
+                break;
+
+            case return_value_policy::copy:
+                if (copy_constructor) {
+                    valueptr = copy_constructor(src);
+                } else {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    std::string type_name(tinfo->cpptype->name());
+                    detail::clean_type_id(type_name);
+                    throw cast_error("return_value_policy = copy, but type " + type_name
+                                     + " is non-copyable!");
+#else
+                    throw cast_error("return_value_policy = copy, but type is "
+                                     "non-copyable! (#define PYBIND11_DETAILED_ERROR_MESSAGES or "
+                                     "compile in debug mode for details)");
+#endif
+                }
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::move:
+                if (move_constructor) {
+                    valueptr = move_constructor(src);
+                } else if (copy_constructor) {
+                    valueptr = copy_constructor(src);
+                } else {
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    std::string type_name(tinfo->cpptype->name());
+                    detail::clean_type_id(type_name);
+                    throw cast_error("return_value_policy = move, but type " + type_name
+                                     + " is neither movable nor copyable!");
+#else
+                    throw cast_error("return_value_policy = move, but type is neither "
+                                     "movable nor copyable! "
+                                     "(#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in "
+                                     "debug mode for details)");
+#endif
+                }
+                wrapper->owned = true;
+                break;
+
+            case return_value_policy::reference_internal:
+                valueptr = src;
+                wrapper->owned = false;
+                keep_alive_impl(inst, parent);
+                break;
+
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        }
+
+        tinfo->init_instance(wrapper, existing_holder);
+
+        return inst.release();
+    }
+
+    // Base methods for generic caster; there are overridden in copyable_holder_caster
+    void load_value(value_and_holder &&v_h) {
+        auto *&vptr = v_h.value_ptr();
+        // Lazy allocation for unallocated values:
+        if (vptr == nullptr) {
+            const auto *type = v_h.type ? v_h.type : typeinfo;
+            if (type->operator_new) {
+                vptr = type->operator_new(type->type_size);
+            } else {
+#if defined(__cpp_aligned_new) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+                if (type->type_align > __STDCPP_DEFAULT_NEW_ALIGNMENT__) {
+                    vptr = ::operator new(type->type_size, std::align_val_t(type->type_align));
+                } else {
+                    vptr = ::operator new(type->type_size);
+                }
+#else
+                vptr = ::operator new(type->type_size);
+#endif
+            }
+        }
+        value = vptr;
+    }
+    bool try_implicit_casts(handle src, bool convert) {
+        for (const auto &cast : typeinfo->implicit_casts) {
+            type_caster_generic sub_caster(*cast.first);
+            if (sub_caster.load(src, convert)) {
+                value = cast.second(sub_caster.value);
+                return true;
+            }
+        }
+        return false;
+    }
+    bool try_direct_conversions(handle src) {
+        for (auto &converter : *typeinfo->direct_conversions) {
+            if (converter(src.ptr(), value)) {
+                return true;
+            }
+        }
+        return false;
+    }
+    bool try_cpp_conduit(handle src) {
+        value = try_raw_pointer_ephemeral_from_cpp_conduit(src, cpptype);
+        if (value != nullptr) {
+            return true;
+        }
+        return false;
+    }
+    void check_holder_compat() {}
+
+    PYBIND11_NOINLINE static void *local_load(PyObject *src, const type_info *ti) {
+        auto caster = type_caster_generic(ti);
+        if (caster.load(src, false)) {
+            return caster.value;
+        }
+        return nullptr;
+    }
+
+    /// Try to load with foreign typeinfo, if available. Used when there is no
+    /// native typeinfo, or when the native one wasn't able to produce a value.
+    PYBIND11_NOINLINE bool try_load_foreign_module_local(handle src) {
+        constexpr auto *local_key = PYBIND11_MODULE_LOCAL_ID;
+        const auto pytype = type::handle_of(src);
+        if (!hasattr(pytype, local_key)) {
+            return false;
+        }
+
+        type_info *foreign_typeinfo = reinterpret_borrow<capsule>(getattr(pytype, local_key));
+        // Only consider this foreign loader if actually foreign and is a loader of the correct cpp
+        // type
+        if (foreign_typeinfo->module_local_load == &local_load
+            || (cpptype && !same_type(*cpptype, *foreign_typeinfo->cpptype))) {
+            return false;
+        }
+
+        if (auto *result = foreign_typeinfo->module_local_load(src.ptr(), foreign_typeinfo)) {
+            value = result;
+            return true;
+        }
+        return false;
+    }
+
+    // Implementation of `load`; this takes the type of `this` so that it can dispatch the relevant
+    // bits of code between here and copyable_holder_caster where the two classes need different
+    // logic (without having to resort to virtual inheritance).
+    template <typename ThisT>
+    PYBIND11_NOINLINE bool load_impl(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (!typeinfo) {
+            return try_load_foreign_module_local(src);
+        }
+
+        auto &this_ = static_cast<ThisT &>(*this);
+        this_.check_holder_compat();
+
+        PyTypeObject *srctype = Py_TYPE(src.ptr());
+
+        // Case 1: If src is an exact type match for the target type then we can reinterpret_cast
+        // the instance's value pointer to the target type:
+        if (srctype == typeinfo->type) {
+            this_.load_value(reinterpret_cast<instance *>(src.ptr())->get_value_and_holder());
+            return true;
+        }
+        // Case 2: We have a derived class
+        if (PyType_IsSubtype(srctype, typeinfo->type)) {
+            const auto &bases = all_type_info(srctype);
+            bool no_cpp_mi = typeinfo->simple_type;
+
+            // Case 2a: the python type is a Python-inherited derived class that inherits from just
+            // one simple (no MI) pybind11 class, or is an exact match, so the C++ instance is of
+            // the right type and we can use reinterpret_cast.
+            // (This is essentially the same as case 2b, but because not using multiple inheritance
+            // is extremely common, we handle it specially to avoid the loop iterator and type
+            // pointer lookup overhead)
+            if (bases.size() == 1 && (no_cpp_mi || bases.front()->type == typeinfo->type)) {
+                this_.load_value(reinterpret_cast<instance *>(src.ptr())->get_value_and_holder());
+                return true;
+            }
+            // Case 2b: the python type inherits from multiple C++ bases.  Check the bases to see
+            // if we can find an exact match (or, for a simple C++ type, an inherited match); if
+            // so, we can safely reinterpret_cast to the relevant pointer.
+            if (bases.size() > 1) {
+                for (auto *base : bases) {
+                    if (no_cpp_mi ? PyType_IsSubtype(base->type, typeinfo->type)
+                                  : base->type == typeinfo->type) {
+                        this_.load_value(
+                            reinterpret_cast<instance *>(src.ptr())->get_value_and_holder(base));
+                        return true;
+                    }
+                }
+            }
+
+            // Case 2c: C++ multiple inheritance is involved and we couldn't find an exact type
+            // match in the registered bases, above, so try implicit casting (needed for proper C++
+            // casting when MI is involved).
+            if (this_.try_implicit_casts(src, convert)) {
+                return true;
+            }
+        }
+
+        // Perform an implicit conversion
+        if (convert) {
+            for (const auto &converter : typeinfo->implicit_conversions) {
+                auto temp = reinterpret_steal<object>(converter(src.ptr(), typeinfo->type));
+                if (load_impl<ThisT>(temp, false)) {
+                    loader_life_support::add_patient(temp);
+                    return true;
+                }
+            }
+            if (this_.try_direct_conversions(src)) {
+                return true;
+            }
+        }
+
+        // Failed to match local typeinfo. Try again with global.
+        if (typeinfo->module_local) {
+            if (auto *gtype = get_global_type_info(*typeinfo->cpptype)) {
+                typeinfo = gtype;
+                return load(src, false);
+            }
+        }
+
+        // Global typeinfo has precedence over foreign module_local
+        if (try_load_foreign_module_local(src)) {
+            return true;
+        }
+
+        // Custom converters didn't take None, now we convert None to nullptr.
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            value = nullptr;
+            return true;
+        }
+
+        if (convert && cpptype && this_.try_cpp_conduit(src)) {
+            return true;
+        }
+
+        return false;
+    }
+
+    // Called to do type lookup and wrap the pointer and type in a pair when a dynamic_cast
+    // isn't needed or can't be used.  If the type is unknown, sets the error and returns a pair
+    // with .second = nullptr.  (p.first = nullptr is not an error: it becomes None).
+    PYBIND11_NOINLINE static std::pair<const void *, const type_info *>
+    src_and_type(const void *src,
+                 const std::type_info &cast_type,
+                 const std::type_info *rtti_type = nullptr) {
+        if (auto *tpi = get_type_info(cast_type)) {
+            return {src, const_cast<const type_info *>(tpi)};
+        }
+
+        // Not found, set error:
+        std::string tname = rtti_type ? rtti_type->name() : cast_type.name();
+        detail::clean_type_id(tname);
+        std::string msg = "Unregistered type : " + tname;
+        set_error(PyExc_TypeError, msg.c_str());
+        return {nullptr, nullptr};
+    }
+
+    const type_info *typeinfo = nullptr;
+    const std::type_info *cpptype = nullptr;
+    void *value = nullptr;
+};
+
+inline object cpp_conduit_method(handle self,
+                                 const bytes &pybind11_platform_abi_id,
+                                 const capsule &cpp_type_info_capsule,
+                                 const bytes &pointer_kind) {
+#ifdef PYBIND11_HAS_STRING_VIEW
+    using cpp_str = std::string_view;
+#else
+    using cpp_str = std::string;
+#endif
+    if (cpp_str(pybind11_platform_abi_id) != PYBIND11_PLATFORM_ABI_ID) {
+        return none();
+    }
+    if (std::strcmp(cpp_type_info_capsule.name(), typeid(std::type_info).name()) != 0) {
+        return none();
+    }
+    if (cpp_str(pointer_kind) != "raw_pointer_ephemeral") {
+        throw std::runtime_error("Invalid pointer_kind: \"" + std::string(pointer_kind) + "\"");
+    }
+    const auto *cpp_type_info = cpp_type_info_capsule.get_pointer<const std::type_info>();
+    type_caster_generic caster(*cpp_type_info);
+    if (!caster.load(self, false)) {
+        return none();
+    }
+    return capsule(caster.value, cpp_type_info->name());
+}
+
+/**
+ * Determine suitable casting operator for pointer-or-lvalue-casting type casters.  The type caster
+ * needs to provide `operator T*()` and `operator T&()` operators.
+ *
+ * If the type supports moving the value away via an `operator T&&() &&` method, it should use
+ * `movable_cast_op_type` instead.
+ */
+template <typename T>
+using cast_op_type = conditional_t<std::is_pointer<remove_reference_t<T>>::value,
+                                   typename std::add_pointer<intrinsic_t<T>>::type,
+                                   typename std::add_lvalue_reference<intrinsic_t<T>>::type>;
+
+/**
+ * Determine suitable casting operator for a type caster with a movable value.  Such a type caster
+ * needs to provide `operator T*()`, `operator T&()`, and `operator T&&() &&`.  The latter will be
+ * called in appropriate contexts where the value can be moved rather than copied.
+ *
+ * These operator are automatically provided when using the PYBIND11_TYPE_CASTER macro.
+ */
+template <typename T>
+using movable_cast_op_type
+    = conditional_t<std::is_pointer<typename std::remove_reference<T>::type>::value,
+                    typename std::add_pointer<intrinsic_t<T>>::type,
+                    conditional_t<std::is_rvalue_reference<T>::value,
+                                  typename std::add_rvalue_reference<intrinsic_t<T>>::type,
+                                  typename std::add_lvalue_reference<intrinsic_t<T>>::type>>;
+
+// Does the container have a mapped type and is it recursive?
+// Implemented by specializations below.
+template <typename Container, typename SFINAE = void>
+struct container_mapped_type_traits {
+    static constexpr bool has_mapped_type = false;
+    static constexpr bool has_recursive_mapped_type = false;
+};
+
+template <typename Container>
+struct container_mapped_type_traits<
+    Container,
+    typename std::enable_if<
+        std::is_same<typename Container::mapped_type, Container>::value>::type> {
+    static constexpr bool has_mapped_type = true;
+    static constexpr bool has_recursive_mapped_type = true;
+};
+
+template <typename Container>
+struct container_mapped_type_traits<
+    Container,
+    typename std::enable_if<
+        negation<std::is_same<typename Container::mapped_type, Container>>::value>::type> {
+    static constexpr bool has_mapped_type = true;
+    static constexpr bool has_recursive_mapped_type = false;
+};
+
+// Does the container have a value type and is it recursive?
+// Implemented by specializations below.
+template <typename Container, typename SFINAE = void>
+struct container_value_type_traits : std::false_type {
+    static constexpr bool has_value_type = false;
+    static constexpr bool has_recursive_value_type = false;
+};
+
+template <typename Container>
+struct container_value_type_traits<
+    Container,
+    typename std::enable_if<
+        std::is_same<typename Container::value_type, Container>::value>::type> {
+    static constexpr bool has_value_type = true;
+    static constexpr bool has_recursive_value_type = true;
+};
+
+template <typename Container>
+struct container_value_type_traits<
+    Container,
+    typename std::enable_if<
+        negation<std::is_same<typename Container::value_type, Container>>::value>::type> {
+    static constexpr bool has_value_type = true;
+    static constexpr bool has_recursive_value_type = false;
+};
+
+/*
+ * Tag to be used for representing the bottom of recursively defined types.
+ * Define this tag so we don't have to use void.
+ */
+struct recursive_bottom {};
+
+/*
+ * Implementation detail of `recursive_container_traits` below.
+ * `T` is the `value_type` of the container, which might need to be modified to
+ * avoid recursive types and const types.
+ */
+template <typename T, bool is_this_a_map>
+struct impl_type_to_check_recursively {
+    /*
+     * If the container is recursive, then no further recursion should be done.
+     */
+    using if_recursive = recursive_bottom;
+    /*
+     * Otherwise yield `T` unchanged.
+     */
+    using if_not_recursive = T;
+};
+
+/*
+ * For pairs - only as value type of a map -, the first type should remove the `const`.
+ * Also, if the map is recursive, then the recursive checking should consider
+ * the first type only.
+ */
+template <typename A, typename B>
+struct impl_type_to_check_recursively<std::pair<A, B>, /* is_this_a_map = */ true> {
+    using if_recursive = typename std::remove_const<A>::type;
+    using if_not_recursive = std::pair<typename std::remove_const<A>::type, B>;
+};
+
+/*
+ * Implementation of `recursive_container_traits` below.
+ */
+template <typename Container, typename SFINAE = void>
+struct impl_recursive_container_traits {
+    using type_to_check_recursively = recursive_bottom;
+};
+
+template <typename Container>
+struct impl_recursive_container_traits<
+    Container,
+    typename std::enable_if<container_value_type_traits<Container>::has_value_type>::type> {
+    static constexpr bool is_recursive
+        = container_mapped_type_traits<Container>::has_recursive_mapped_type
+          || container_value_type_traits<Container>::has_recursive_value_type;
+    /*
+     * This member dictates which type Pybind11 should check recursively in traits
+     * such as `is_move_constructible`, `is_copy_constructible`, `is_move_assignable`, ...
+     * Direct access to `value_type` should be avoided:
+     * 1. `value_type` might recursively contain the type again
+     * 2. `value_type` of STL map types is `std::pair<A const, B>`, the `const`
+     *    should be removed.
+     *
+     */
+    using type_to_check_recursively = typename std::conditional<
+        is_recursive,
+        typename impl_type_to_check_recursively<
+            typename Container::value_type,
+            container_mapped_type_traits<Container>::has_mapped_type>::if_recursive,
+        typename impl_type_to_check_recursively<
+            typename Container::value_type,
+            container_mapped_type_traits<Container>::has_mapped_type>::if_not_recursive>::type;
+};
+
+/*
+ * This trait defines the `type_to_check_recursively` which is needed to properly
+ * handle recursively defined traits such as `is_move_constructible` without going
+ * into an infinite recursion.
+ * Should be used instead of directly accessing the `value_type`.
+ * It cancels the recursion by returning the `recursive_bottom` tag.
+ *
+ * The default definition of `type_to_check_recursively` is as follows:
+ *
+ * 1. By default, it is `recursive_bottom`, so that the recursion is canceled.
+ * 2. If the type is non-recursive and defines a `value_type`, then the `value_type` is used.
+ *    If the `value_type` is a pair and a `mapped_type` is defined,
+ *    then the `const` is removed from the first type.
+ * 3. If the type is recursive and `value_type` is not a pair, then `recursive_bottom` is returned.
+ * 4. If the type is recursive and `value_type` is a pair and a `mapped_type` is defined,
+ *    then `const` is removed from the first type and the first type is returned.
+ *
+ * This behavior can be extended by the user as seen in test_stl_binders.cpp.
+ *
+ * This struct is exactly the same as impl_recursive_container_traits.
+ * The duplication achieves that user-defined specializations don't compete
+ * with internal specializations, but take precedence.
+ */
+template <typename Container, typename SFINAE = void>
+struct recursive_container_traits : impl_recursive_container_traits<Container> {};
+
+template <typename T>
+struct is_move_constructible
+    : all_of<std::is_move_constructible<T>,
+             is_move_constructible<
+                 typename recursive_container_traits<T>::type_to_check_recursively>> {};
+
+template <>
+struct is_move_constructible<recursive_bottom> : std::true_type {};
+
+// Likewise for std::pair
+// (after C++17 it is mandatory that the move constructor not exist when the two types aren't
+// themselves move constructible, but this can not be relied upon when T1 or T2 are themselves
+// containers).
+template <typename T1, typename T2>
+struct is_move_constructible<std::pair<T1, T2>>
+    : all_of<is_move_constructible<T1>, is_move_constructible<T2>> {};
+
+// std::is_copy_constructible isn't quite enough: it lets std::vector<T> (and similar) through when
+// T is non-copyable, but code containing such a copy constructor fails to actually compile.
+template <typename T>
+struct is_copy_constructible
+    : all_of<std::is_copy_constructible<T>,
+             is_copy_constructible<
+                 typename recursive_container_traits<T>::type_to_check_recursively>> {};
+
+template <>
+struct is_copy_constructible<recursive_bottom> : std::true_type {};
+
+// Likewise for std::pair
+// (after C++17 it is mandatory that the copy constructor not exist when the two types aren't
+// themselves copy constructible, but this can not be relied upon when T1 or T2 are themselves
+// containers).
+template <typename T1, typename T2>
+struct is_copy_constructible<std::pair<T1, T2>>
+    : all_of<is_copy_constructible<T1>, is_copy_constructible<T2>> {};
+
+// The same problems arise with std::is_copy_assignable, so we use the same workaround.
+template <typename T>
+struct is_copy_assignable
+    : all_of<
+          std::is_copy_assignable<T>,
+          is_copy_assignable<typename recursive_container_traits<T>::type_to_check_recursively>> {
+};
+
+template <>
+struct is_copy_assignable<recursive_bottom> : std::true_type {};
+
+template <typename T1, typename T2>
+struct is_copy_assignable<std::pair<T1, T2>>
+    : all_of<is_copy_assignable<T1>, is_copy_assignable<T2>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// polymorphic_type_hook<itype>::get(src, tinfo) determines whether the object pointed
+// to by `src` actually is an instance of some class derived from `itype`.
+// If so, it sets `tinfo` to point to the std::type_info representing that derived
+// type, and returns a pointer to the start of the most-derived object of that type
+// (in which `src` is a subobject; this will be the same address as `src` in most
+// single inheritance cases). If not, or if `src` is nullptr, it simply returns `src`
+// and leaves `tinfo` at its default value of nullptr.
+//
+// The default polymorphic_type_hook just returns src. A specialization for polymorphic
+// types determines the runtime type of the passed object and adjusts the this-pointer
+// appropriately via dynamic_cast<void*>. This is what enables a C++ Animal* to appear
+// to Python as a Dog (if Dog inherits from Animal, Animal is polymorphic, Dog is
+// registered with pybind11, and this Animal is in fact a Dog).
+//
+// You may specialize polymorphic_type_hook yourself for types that want to appear
+// polymorphic to Python but do not use C++ RTTI. (This is a not uncommon pattern
+// in performance-sensitive applications, used most notably in LLVM.)
+//
+// polymorphic_type_hook_base allows users to specialize polymorphic_type_hook with
+// std::enable_if. User provided specializations will always have higher priority than
+// the default implementation and specialization provided in polymorphic_type_hook_base.
+template <typename itype, typename SFINAE = void>
+struct polymorphic_type_hook_base {
+    static const void *get(const itype *src, const std::type_info *&) { return src; }
+};
+template <typename itype>
+struct polymorphic_type_hook_base<itype, detail::enable_if_t<std::is_polymorphic<itype>::value>> {
+    static const void *get(const itype *src, const std::type_info *&type) {
+        type = src ? &typeid(*src) : nullptr;
+        return dynamic_cast<const void *>(src);
+    }
+};
+template <typename itype, typename SFINAE = void>
+struct polymorphic_type_hook : public polymorphic_type_hook_base<itype> {};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Generic type caster for objects stored on the heap
+template <typename type>
+class type_caster_base : public type_caster_generic {
+    using itype = intrinsic_t<type>;
+
+public:
+    static constexpr auto name = const_name<type>();
+
+    type_caster_base() : type_caster_base(typeid(type)) {}
+    explicit type_caster_base(const std::type_info &info) : type_caster_generic(info) {}
+
+    static handle cast(const itype &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(std::addressof(src), policy, parent);
+    }
+
+    static handle cast(itype &&src, return_value_policy, handle parent) {
+        return cast(std::addressof(src), return_value_policy::move, parent);
+    }
+
+    // Returns a (pointer, type_info) pair taking care of necessary type lookup for a
+    // polymorphic type (using RTTI by default, but can be overridden by specializing
+    // polymorphic_type_hook). If the instance isn't derived, returns the base version.
+    static std::pair<const void *, const type_info *> src_and_type(const itype *src) {
+        const auto &cast_type = typeid(itype);
+        const std::type_info *instance_type = nullptr;
+        const void *vsrc = polymorphic_type_hook<itype>::get(src, instance_type);
+        if (instance_type && !same_type(cast_type, *instance_type)) {
+            // This is a base pointer to a derived type. If the derived type is registered
+            // with pybind11, we want to make the full derived object available.
+            // In the typical case where itype is polymorphic, we get the correct
+            // derived pointer (which may be != base pointer) by a dynamic_cast to
+            // most derived type. If itype is not polymorphic, we won't get here
+            // except via a user-provided specialization of polymorphic_type_hook,
+            // and the user has promised that no this-pointer adjustment is
+            // required in that case, so it's OK to use static_cast.
+            if (const auto *tpi = get_type_info(*instance_type)) {
+                return {vsrc, tpi};
+            }
+        }
+        // Otherwise we have either a nullptr, an `itype` pointer, or an unknown derived pointer,
+        // so don't do a cast
+        return type_caster_generic::src_and_type(src, cast_type, instance_type);
+    }
+
+    static handle cast(const itype *src, return_value_policy policy, handle parent) {
+        auto st = src_and_type(src);
+        return type_caster_generic::cast(st.first,
+                                         policy,
+                                         parent,
+                                         st.second,
+                                         make_copy_constructor(src),
+                                         make_move_constructor(src));
+    }
+
+    static handle cast_holder(const itype *src, const void *holder) {
+        auto st = src_and_type(src);
+        return type_caster_generic::cast(st.first,
+                                         return_value_policy::take_ownership,
+                                         {},
+                                         st.second,
+                                         nullptr,
+                                         nullptr,
+                                         holder);
+    }
+
+    template <typename T>
+    using cast_op_type = detail::cast_op_type<T>;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator itype *() { return (type *) value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator itype &() {
+        if (!value) {
+            throw reference_cast_error();
+        }
+        return *((itype *) value);
+    }
+
+protected:
+    using Constructor = void *(*) (const void *);
+
+    /* Only enabled when the types are {copy,move}-constructible *and* when the type
+       does not have a private operator new implementation. A comma operator is used in the
+       decltype argument to apply SFINAE to the public copy/move constructors.*/
+    template <typename T, typename = enable_if_t<is_copy_constructible<T>::value>>
+    static auto make_copy_constructor(const T *) -> decltype(new T(std::declval<const T>()),
+                                                             Constructor{}) {
+        return [](const void *arg) -> void * { return new T(*reinterpret_cast<const T *>(arg)); };
+    }
+
+    template <typename T, typename = enable_if_t<is_move_constructible<T>::value>>
+    static auto make_move_constructor(const T *) -> decltype(new T(std::declval<T &&>()),
+                                                             Constructor{}) {
+        return [](const void *arg) -> void * {
+            return new T(std::move(*const_cast<T *>(reinterpret_cast<const T *>(arg))));
+        };
+    }
+
+    static Constructor make_copy_constructor(...) { return nullptr; }
+    static Constructor make_move_constructor(...) { return nullptr; }
+};
+
+inline std::string quote_cpp_type_name(const std::string &cpp_type_name) {
+    return cpp_type_name; // No-op for now. See PR #4888
+}
+
+PYBIND11_NOINLINE std::string type_info_description(const std::type_info &ti) {
+    if (auto *type_data = get_type_info(ti)) {
+        handle th((PyObject *) type_data->type);
+        return th.attr("__module__").cast<std::string>() + '.'
+               + th.attr("__qualname__").cast<std::string>();
+    }
+    return quote_cpp_type_name(clean_type_id(ti.name()));
+}
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/typeid.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/typeid.h
new file mode 100644
index 0000000000000000000000000000000000000000..a67b52135bd15020426dfefccf4a211b141f7482
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/typeid.h
@@ -0,0 +1,65 @@
+/*
+    pybind11/detail/typeid.h: Compiler-independent access to type identifiers
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <cstdio>
+#include <cstdlib>
+
+#if defined(__GNUG__)
+#    include <cxxabi.h>
+#endif
+
+#include "common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Erase all occurrences of a substring
+inline void erase_all(std::string &string, const std::string &search) {
+    for (size_t pos = 0;;) {
+        pos = string.find(search, pos);
+        if (pos == std::string::npos) {
+            break;
+        }
+        string.erase(pos, search.length());
+    }
+}
+
+PYBIND11_NOINLINE void clean_type_id(std::string &name) {
+#if defined(__GNUG__)
+    int status = 0;
+    std::unique_ptr<char, void (*)(void *)> res{
+        abi::__cxa_demangle(name.c_str(), nullptr, nullptr, &status), std::free};
+    if (status == 0) {
+        name = res.get();
+    }
+#else
+    detail::erase_all(name, "class ");
+    detail::erase_all(name, "struct ");
+    detail::erase_all(name, "enum ");
+#endif
+    detail::erase_all(name, "pybind11::");
+}
+
+inline std::string clean_type_id(const char *typeid_name) {
+    std::string name(typeid_name);
+    detail::clean_type_id(name);
+    return name;
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Return a string representation of a C++ type
+template <typename T>
+static std::string type_id() {
+    return detail::clean_type_id(typeid(T).name());
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/detail/value_and_holder.h b/lib/python3.11/site-packages/torch/include/pybind11/detail/value_and_holder.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca37d70ad2e79d050bf8734bc97a825ec4d9c024
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/detail/value_and_holder.h
@@ -0,0 +1,77 @@
+// Copyright (c) 2016-2024 The Pybind Development Team.
+// All rights reserved. Use of this source code is governed by a
+// BSD-style license that can be found in the LICENSE file.
+
+#pragma once
+
+#include "common.h"
+
+#include <cstddef>
+#include <typeinfo>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+struct value_and_holder {
+    instance *inst = nullptr;
+    size_t index = 0u;
+    const detail::type_info *type = nullptr;
+    void **vh = nullptr;
+
+    // Main constructor for a found value/holder:
+    value_and_holder(instance *i, const detail::type_info *type, size_t vpos, size_t index)
+        : inst{i}, index{index}, type{type},
+          vh{inst->simple_layout ? inst->simple_value_holder
+                                 : &inst->nonsimple.values_and_holders[vpos]} {}
+
+    // Default constructor (used to signal a value-and-holder not found by get_value_and_holder())
+    value_and_holder() = default;
+
+    // Used for past-the-end iterator
+    explicit value_and_holder(size_t index) : index{index} {}
+
+    template <typename V = void>
+    V *&value_ptr() const {
+        return reinterpret_cast<V *&>(vh[0]);
+    }
+    // True if this `value_and_holder` has a non-null value pointer
+    explicit operator bool() const { return value_ptr() != nullptr; }
+
+    template <typename H>
+    H &holder() const {
+        return reinterpret_cast<H &>(vh[1]);
+    }
+    bool holder_constructed() const {
+        return inst->simple_layout
+                   ? inst->simple_holder_constructed
+                   : (inst->nonsimple.status[index] & instance::status_holder_constructed) != 0u;
+    }
+    // NOLINTNEXTLINE(readability-make-member-function-const)
+    void set_holder_constructed(bool v = true) {
+        if (inst->simple_layout) {
+            inst->simple_holder_constructed = v;
+        } else if (v) {
+            inst->nonsimple.status[index] |= instance::status_holder_constructed;
+        } else {
+            inst->nonsimple.status[index] &= (std::uint8_t) ~instance::status_holder_constructed;
+        }
+    }
+    bool instance_registered() const {
+        return inst->simple_layout
+                   ? inst->simple_instance_registered
+                   : ((inst->nonsimple.status[index] & instance::status_instance_registered) != 0);
+    }
+    // NOLINTNEXTLINE(readability-make-member-function-const)
+    void set_instance_registered(bool v = true) {
+        if (inst->simple_layout) {
+            inst->simple_instance_registered = v;
+        } else if (v) {
+            inst->nonsimple.status[index] |= instance::status_instance_registered;
+        } else {
+            inst->nonsimple.status[index] &= (std::uint8_t) ~instance::status_instance_registered;
+        }
+    }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/eigen.h b/lib/python3.11/site-packages/torch/include/pybind11/eigen.h
new file mode 100644
index 0000000000000000000000000000000000000000..273b9c9308d9de40d13abfe16256ace273c71761
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/eigen.h
@@ -0,0 +1,12 @@
+/*
+    pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "eigen/matrix.h"
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/eigen/common.h b/lib/python3.11/site-packages/torch/include/pybind11/eigen/common.h
new file mode 100644
index 0000000000000000000000000000000000000000..24f56d158442c2e2f4667806e91fd94b476cc96e
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/eigen/common.h
@@ -0,0 +1,9 @@
+// Copyright (c) 2023 The pybind Community.
+
+#pragma once
+
+// Common message for `static_assert()`s, which are useful to easily
+// preempt much less obvious errors.
+#define PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED                                    \
+    "Pointer types (in particular `PyObject *`) are not supported as scalar types for Eigen "     \
+    "types."
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/eigen/matrix.h b/lib/python3.11/site-packages/torch/include/pybind11/eigen/matrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..5cf1f0a2a0566a8155ef7bcc10806883286615ff
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/eigen/matrix.h
@@ -0,0 +1,715 @@
+/*
+    pybind11/eigen/matrix.h: Transparent conversion for dense and sparse Eigen matrices
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <pybind11/numpy.h>
+
+#include "common.h"
+
+/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
+   See also:
+       https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
+       https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
+*/
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(5054) // https://github.com/pybind/pybind11/pull/3741
+//       C5054: operator '&': deprecated between enumerations of different types
+#if defined(__MINGW32__)
+PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
+#endif
+
+#include <Eigen/Core>
+#include <Eigen/SparseCore>
+
+PYBIND11_WARNING_POP
+
+// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
+// move constructors that break things.  We could detect this an explicitly copy, but an extra copy
+// of matrices seems highly undesirable.
+static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
+              "Eigen matrix support in pybind11 requires Eigen >= 3.2.7");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
+using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
+template <typename MatrixType>
+using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
+template <typename MatrixType>
+using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
+using EigenIndex = Eigen::Index;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
+#else
+using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
+template <typename Scalar, int Flags, typename StorageIndex>
+using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
+#endif
+
+// Matches Eigen::Map, Eigen::Ref, blocks, etc:
+template <typename T>
+using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>,
+                                  std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
+template <typename T>
+using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
+template <typename T>
+using is_eigen_dense_plain
+    = all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
+template <typename T>
+using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
+// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above.  This
+// basically covers anything that can be assigned to a dense matrix but that don't have a typical
+// matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
+// SelfAdjointView fall into this category.
+template <typename T>
+using is_eigen_other
+    = all_of<is_template_base_of<Eigen::EigenBase, T>,
+             negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>>;
+
+// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
+template <bool EigenRowMajor>
+struct EigenConformable {
+    bool conformable = false;
+    EigenIndex rows = 0, cols = 0;
+    EigenDStride stride{0, 0};    // Only valid if negativestrides is false!
+    bool negativestrides = false; // If true, do not use stride!
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    EigenConformable(bool fits = false) : conformable{fits} {}
+    // Matrix type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
+        : conformable{true}, rows{r}, cols{c},
+          // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
+          // http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
+          stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
+                               : (cstride > 0 ? cstride : 0) /* outer stride */,
+                 EigenRowMajor ? (cstride > 0 ? cstride : 0)
+                               : (rstride > 0 ? rstride : 0) /* inner stride */},
+          negativestrides{rstride < 0 || cstride < 0} {}
+    // Vector type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
+        : EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
+
+    template <typename props>
+    bool stride_compatible() const {
+        // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
+        // matching strides, or a dimension size of 1 (in which case the stride value is
+        // irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
+        // (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
+        if (negativestrides) {
+            return false;
+        }
+        if (rows == 0 || cols == 0) {
+            return true;
+        }
+        return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
+                || (EigenRowMajor ? cols : rows) == 1)
+               && (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
+                   || (EigenRowMajor ? rows : cols) == 1);
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return conformable; }
+};
+
+template <typename Type>
+struct eigen_extract_stride {
+    using type = Type;
+};
+template <typename PlainObjectType, int MapOptions, typename StrideType>
+struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
+    using type = StrideType;
+};
+template <typename PlainObjectType, int Options, typename StrideType>
+struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> {
+    using type = StrideType;
+};
+
+// Helper struct for extracting information from an Eigen type
+template <typename Type_>
+struct EigenProps {
+    using Type = Type_;
+    using Scalar = typename Type::Scalar;
+    using StrideType = typename eigen_extract_stride<Type>::type;
+    static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
+                                size = Type::SizeAtCompileTime;
+    static constexpr bool row_major = Type::IsRowMajor,
+                          vector
+                          = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
+        fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
+                          fixed = size != Eigen::Dynamic, // Fully-fixed size
+        dynamic = !fixed_rows && !fixed_cols;             // Fully-dynamic size
+
+    template <EigenIndex i, EigenIndex ifzero>
+    using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
+    static constexpr EigenIndex inner_stride
+        = if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
+        outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
+        vector      ? size
+        : row_major ? cols
+                    : rows > ::value;
+    static constexpr bool dynamic_stride
+        = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
+    static constexpr bool requires_row_major
+        = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
+    static constexpr bool requires_col_major
+        = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
+
+    // Takes an input array and determines whether we can make it fit into the Eigen type.  If
+    // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
+    // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
+    static EigenConformable<row_major> conformable(const array &a) {
+        const auto dims = a.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        if (dims == 2) { // Matrix type: require exact match (or dynamic)
+
+            EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
+                       np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
+                       np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
+            if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols)) {
+                return false;
+            }
+
+            return {np_rows, np_cols, np_rstride, np_cstride};
+        }
+
+        // Otherwise we're storing an n-vector.  Only one of the strides will be used, but
+        // whichever is used, we want the (single) numpy stride value.
+        const EigenIndex n = a.shape(0),
+                         stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
+
+        if (vector) { // Eigen type is a compile-time vector
+            if (fixed && size != n) {
+                return false; // Vector size mismatch
+            }
+            return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
+        }
+        if (fixed) {
+            // The type has a fixed size, but is not a vector: abort
+            return false;
+        }
+        if (fixed_cols) {
+            // Since this isn't a vector, cols must be != 1.  We allow this only if it exactly
+            // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
+            if (cols != n) {
+                return false;
+            }
+            return {1, n, stride};
+        } // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
+        if (fixed_rows && rows != n) {
+            return false;
+        }
+        return {n, 1, stride};
+    }
+
+    static constexpr bool show_writeable
+        = is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
+    static constexpr bool show_order = is_eigen_dense_map<Type>::value;
+    static constexpr bool show_c_contiguous = show_order && requires_row_major;
+    static constexpr bool show_f_contiguous
+        = !show_c_contiguous && show_order && requires_col_major;
+
+    static constexpr auto descriptor
+        = const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
+          + const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
+          + const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
+          +
+          // For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to
+          // be satisfied: writeable=True (for a mutable reference), and, depending on the map's
+          // stride options, possibly f_contiguous or c_contiguous.  We include them in the
+          // descriptor output to provide some hint as to why a TypeError is occurring (otherwise
+          // it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
+          // an error message that you *gave* a numpy.ndarray of the right type and dimensions.
+          const_name<show_writeable>(", flags.writeable", "")
+          + const_name<show_c_contiguous>(", flags.c_contiguous", "")
+          + const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
+};
+
+// Casts an Eigen type to numpy array.  If given a base, the numpy array references the src data,
+// otherwise it'll make a copy.  writeable lets you turn off the writeable flag for the array.
+template <typename props>
+handle
+eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
+    constexpr ssize_t elem_size = sizeof(typename props::Scalar);
+    array a;
+    if (props::vector) {
+        a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
+    } else {
+        a = array({src.rows(), src.cols()},
+                  {elem_size * src.rowStride(), elem_size * src.colStride()},
+                  src.data(),
+                  base);
+    }
+
+    if (!writeable) {
+        array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+    }
+
+    return a.release();
+}
+
+// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
+// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
+// the base will be set to None, and lifetime management is up to the caller).  The numpy array is
+// non-writeable if the given type is const.
+template <typename props, typename Type>
+handle eigen_ref_array(Type &src, handle parent = none()) {
+    // none here is to get past array's should-we-copy detection, which currently always
+    // copies when there is no base.  Setting the base to None should be harmless.
+    return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
+}
+
+// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
+// numpy array that references the encapsulated data with a python-side reference to the capsule to
+// tie its destruction to that of any dependent python objects.  Const-ness is determined by
+// whether or not the Type of the pointer given is const.
+template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
+handle eigen_encapsulate(Type *src) {
+    capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
+    return eigen_ref_array<props>(*src, base);
+}
+
+// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
+// types.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using props = EigenProps<Type>;
+
+    bool load(handle src, bool convert) {
+        // If we're in no-convert mode, only load if given an array of the correct type
+        if (!convert && !isinstance<array_t<Scalar>>(src)) {
+            return false;
+        }
+
+        // Coerce into an array, but don't do type conversion yet; the copy below handles it.
+        auto buf = array::ensure(src);
+
+        if (!buf) {
+            return false;
+        }
+
+        auto dims = buf.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        auto fits = props::conformable(buf);
+        if (!fits) {
+            return false;
+        }
+
+        // Allocate the new type, then build a numpy reference into it
+        value = Type(fits.rows, fits.cols);
+        auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
+        if (dims == 1) {
+            ref = ref.squeeze();
+        } else if (ref.ndim() == 1) {
+            buf = buf.squeeze();
+        }
+
+        int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
+
+        if (result < 0) { // Copy failed!
+            PyErr_Clear();
+            return false;
+        }
+
+        return true;
+    }
+
+private:
+    // Cast implementation
+    template <typename CType>
+    static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::take_ownership:
+            case return_value_policy::automatic:
+                return eigen_encapsulate<props>(src);
+            case return_value_policy::move:
+                return eigen_encapsulate<props>(new CType(std::move(*src)));
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(*src);
+            case return_value_policy::reference:
+            case return_value_policy::automatic_reference:
+                return eigen_ref_array<props>(*src);
+            case return_value_policy::reference_internal:
+                return eigen_ref_array<props>(*src, parent);
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        };
+    }
+
+public:
+    // Normal returned non-reference, non-const value:
+    static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // If you return a non-reference const, we mark the numpy array readonly:
+    static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // lvalue reference return; default (automatic) becomes copy
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+    // const lvalue reference return; default (automatic) becomes copy
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+    // non-const pointer return
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+    // const pointer return
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return &value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &&() && { return std::move(value); }
+    template <typename T>
+    using cast_op_type = movable_cast_op_type<T>;
+
+private:
+    Type value;
+};
+
+// Base class for casting reference/map/block/etc. objects back to python.
+template <typename MapType>
+struct eigen_map_caster {
+    static_assert(!std::is_pointer<typename MapType::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+private:
+    using props = EigenProps<MapType>;
+
+public:
+    // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
+    // to stay around), but we'll allow it under the assumption that you know what you're doing
+    // (and have an appropriate keep_alive in place).  We return a numpy array pointing directly at
+    // the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
+    // Note that this means you need to ensure you don't destroy the object in some other way (e.g.
+    // with an appropriate keep_alive, or with a reference to a statically allocated matrix).
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::copy:
+                return eigen_array_cast<props>(src);
+            case return_value_policy::reference_internal:
+                return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
+            case return_value_policy::reference:
+            case return_value_policy::automatic:
+            case return_value_policy::automatic_reference:
+                return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
+        }
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator MapType() = delete;
+    template <typename>
+    using cast_op_type = MapType;
+};
+
+// We can return any map-like object (but can only load Refs, specialized next):
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> : eigen_map_caster<Type> {};
+
+// Loader for Ref<...> arguments.  See the documentation for info on how to make this work without
+// copying (it requires some extra effort in many cases).
+template <typename PlainObjectType, typename StrideType>
+struct type_caster<
+    Eigen::Ref<PlainObjectType, 0, StrideType>,
+    enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>>
+    : public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
+private:
+    using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
+    using props = EigenProps<Type>;
+    using Scalar = typename props::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
+    using Array
+        = array_t<Scalar,
+                  array::forcecast
+                      | ((props::row_major ? props::inner_stride : props::outer_stride) == 1
+                             ? array::c_style
+                         : (props::row_major ? props::outer_stride : props::inner_stride) == 1
+                             ? array::f_style
+                             : 0)>;
+    static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
+    // Delay construction (these have no default constructor)
+    std::unique_ptr<MapType> map;
+    std::unique_ptr<Type> ref;
+    // Our array.  When possible, this is just a numpy array pointing to the source data, but
+    // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
+    // incompatible layout, or is an array of a type that needs to be converted).  Using a numpy
+    // temporary (rather than an Eigen temporary) saves an extra copy when we need both type
+    // conversion and storage order conversion.  (Note that we refuse to use this temporary copy
+    // when loading an argument for a Ref<M> with M non-const, i.e. a read-write reference).
+    Array copy_or_ref;
+
+public:
+    bool load(handle src, bool convert) {
+        // First check whether what we have is already an array of the right type.  If not, we
+        // can't avoid a copy (because the copy is also going to do type conversion).
+        bool need_copy = !isinstance<Array>(src);
+
+        EigenConformable<props::row_major> fits;
+        if (!need_copy) {
+            // We don't need a converting copy, but we also need to check whether the strides are
+            // compatible with the Ref's stride requirements
+            auto aref = reinterpret_borrow<Array>(src);
+
+            if (aref && (!need_writeable || aref.writeable())) {
+                fits = props::conformable(aref);
+                if (!fits) {
+                    return false; // Incompatible dimensions
+                }
+                if (!fits.template stride_compatible<props>()) {
+                    need_copy = true;
+                } else {
+                    copy_or_ref = std::move(aref);
+                }
+            } else {
+                need_copy = true;
+            }
+        }
+
+        if (need_copy) {
+            // We need to copy: If we need a mutable reference, or we're not supposed to convert
+            // (either because we're in the no-convert overload pass, or because we're explicitly
+            // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
+            if (!convert || need_writeable) {
+                return false;
+            }
+
+            Array copy = Array::ensure(src);
+            if (!copy) {
+                return false;
+            }
+            fits = props::conformable(copy);
+            if (!fits || !fits.template stride_compatible<props>()) {
+                return false;
+            }
+            copy_or_ref = std::move(copy);
+            loader_life_support::add_patient(copy_or_ref);
+        }
+
+        ref.reset();
+        map.reset(new MapType(data(copy_or_ref),
+                              fits.rows,
+                              fits.cols,
+                              make_stride(fits.stride.outer(), fits.stride.inner())));
+        ref.reset(new Type(*map));
+
+        return true;
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return ref.get(); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return *ref; }
+    template <typename _T>
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+
+private:
+    template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
+    Scalar *data(Array &a) {
+        return a.mutable_data();
+    }
+
+    template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
+    const Scalar *data(Array &a) {
+        return a.data();
+    }
+
+    // Attempt to figure out a constructor of `Stride` that will work.
+    // If both strides are fixed, use a default constructor:
+    template <typename S>
+    using stride_ctor_default = bool_constant<S::InnerStrideAtCompileTime != Eigen::Dynamic
+                                              && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                                              && std::is_default_constructible<S>::value>;
+    // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
+    // Eigen::Stride, and use it:
+    template <typename S>
+    using stride_ctor_dual
+        = bool_constant<!stride_ctor_default<S>::value
+                        && std::is_constructible<S, EigenIndex, EigenIndex>::value>;
+    // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
+    // it (passing whichever stride is dynamic).
+    template <typename S>
+    using stride_ctor_outer
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::OuterStrideAtCompileTime == Eigen::Dynamic
+                        && S::InnerStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+    template <typename S>
+    using stride_ctor_inner
+        = bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
+                        && S::InnerStrideAtCompileTime == Eigen::Dynamic
+                        && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible<S, EigenIndex>::value>;
+
+    template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex) {
+        return S();
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex inner) {
+        return S(outer, inner);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex) {
+        return S(outer);
+    }
+    template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex inner) {
+        return S(inner);
+    }
+};
+
+// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
+// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
+// load() is not supported, but we can cast them into the python domain by first copying to a
+// regular Eigen::Matrix, then casting that.
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+protected:
+    using Matrix
+        = Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
+    using props = EigenProps<Matrix>;
+
+public:
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        handle h = eigen_encapsulate<props>(new Matrix(src));
+        return h;
+    }
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator Type() = delete;
+    template <typename>
+    using cast_op_type = Type;
+};
+
+template <typename Type>
+struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer<Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>;
+    using Index = typename Type::Index;
+    static constexpr bool rowMajor = Type::IsRowMajor;
+
+    bool load(handle src, bool) {
+        if (!src) {
+            return false;
+        }
+
+        auto obj = reinterpret_borrow<object>(src);
+        object sparse_module = module_::import("scipy.sparse");
+        object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        if (!type::handle_of(obj).is(matrix_type)) {
+            try {
+                obj = matrix_type(obj);
+            } catch (const error_already_set &) {
+                return false;
+            }
+        }
+
+        auto values = array_t<Scalar>((object) obj.attr("data"));
+        auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
+        auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
+        auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
+        auto nnz = obj.attr("nnz").cast<Index>();
+
+        if (!values || !innerIndices || !outerIndices) {
+            return false;
+        }
+
+        value = EigenMapSparseMatrix<Scalar,
+                                     Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
+                                     StorageIndex>(shape[0].cast<Index>(),
+                                                   shape[1].cast<Index>(),
+                                                   std::move(nnz),
+                                                   outerIndices.mutable_data(),
+                                                   innerIndices.mutable_data(),
+                                                   values.mutable_data());
+
+        return true;
+    }
+
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        const_cast<Type &>(src).makeCompressed();
+
+        object matrix_type
+            = module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        array data(src.nonZeros(), src.valuePtr());
+        array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
+        array innerIndices(src.nonZeros(), src.innerIndexPtr());
+
+        return matrix_type(pybind11::make_tuple(
+                               std::move(data), std::move(innerIndices), std::move(outerIndices)),
+                           pybind11::make_tuple(src.rows(), src.cols()))
+            .release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
+                                                             "scipy.sparse.csc_matrix[")
+                             + npy_format_descriptor<Scalar>::name + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/eigen/tensor.h b/lib/python3.11/site-packages/torch/include/pybind11/eigen/tensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a9d7c2522a2cf90c8f9bfdcf9b993e08ee20cf0
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/eigen/tensor.h
@@ -0,0 +1,515 @@
+/*
+    pybind11/eigen/tensor.h: Transparent conversion for Eigen tensors
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include <pybind11/numpy.h>
+
+#include "common.h"
+
+#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+static_assert(__GNUC__ > 5, "Eigen Tensor support in pybind11 requires GCC > 5.0");
+#endif
+
+// Disable warnings for Eigen
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(4554)
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+#if defined(__MINGW32__)
+PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
+#endif
+
+#include <unsupported/Eigen/CXX11/Tensor>
+
+PYBIND11_WARNING_POP
+
+static_assert(EIGEN_VERSION_AT_LEAST(3, 3, 0),
+              "Eigen Tensor support in pybind11 requires Eigen >= 3.3.0");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline bool is_tensor_aligned(const void *data) {
+    return (reinterpret_cast<std::size_t>(data) % EIGEN_DEFAULT_ALIGN_BYTES) == 0;
+}
+
+template <typename T>
+constexpr int compute_array_flag_from_tensor() {
+    static_assert((static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor))
+                      || (static_cast<int>(T::Layout) == static_cast<int>(Eigen::ColMajor)),
+                  "Layout must be row or column major");
+    return (static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor)) ? array::c_style
+                                                                              : array::f_style;
+}
+
+template <typename T>
+struct eigen_tensor_helper {};
+
+template <typename Scalar_, int NumIndices_, int Options_, typename IndexType>
+struct eigen_tensor_helper<Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>> {
+    using Type = Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>;
+    using ValidType = void;
+
+    static Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape(const Type &f) {
+        return f.dimensions();
+    }
+
+    static constexpr bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> & /*shape*/) {
+        return true;
+    }
+
+    template <typename T>
+    struct helper {};
+
+    template <size_t... Is>
+    struct helper<index_sequence<Is...>> {
+        static constexpr auto value = ::pybind11::detail::concat(const_name(((void) Is, "?"))...);
+    };
+
+    static constexpr auto dimensions_descriptor
+        = helper<decltype(make_index_sequence<Type::NumIndices>())>::value;
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        return new Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) { delete tensor; }
+};
+
+template <typename Scalar_, typename std::ptrdiff_t... Indices, int Options_, typename IndexType>
+struct eigen_tensor_helper<
+    Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>> {
+    using Type = Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>;
+    using ValidType = void;
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices>
+    get_shape(const Type & /*f*/) {
+        return get_shape();
+    }
+
+    static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape() {
+        return Eigen::DSizes<typename Type::Index, Type::NumIndices>(Indices...);
+    }
+
+    static bool
+    is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> &shape) {
+        return get_shape() == shape;
+    }
+
+    static constexpr auto dimensions_descriptor
+        = ::pybind11::detail::concat(const_name<Indices>()...);
+
+    template <typename... Args>
+    static Type *alloc(Args &&...args) {
+        Eigen::aligned_allocator<Type> allocator;
+        return ::new (allocator.allocate(1)) Type(std::forward<Args>(args)...);
+    }
+
+    static void free(Type *tensor) {
+        Eigen::aligned_allocator<Type> allocator;
+        tensor->~Type();
+        allocator.deallocate(tensor, 1);
+    }
+};
+
+template <typename Type, bool ShowDetails, bool NeedsWriteable = false>
+struct get_tensor_descriptor {
+    static constexpr auto details
+        = const_name<NeedsWriteable>(", flags.writeable", "")
+          + const_name<static_cast<int>(Type::Layout) == static_cast<int>(Eigen::RowMajor)>(
+              ", flags.c_contiguous", ", flags.f_contiguous");
+    static constexpr auto value
+        = const_name("numpy.ndarray[") + npy_format_descriptor<typename Type::Scalar>::name
+          + const_name("[") + eigen_tensor_helper<remove_cv_t<Type>>::dimensions_descriptor
+          + const_name("]") + const_name<ShowDetails>(details, const_name("")) + const_name("]");
+};
+
+// When EIGEN_AVOID_STL_ARRAY is defined, Eigen::DSizes<T, 0> does not have the begin() member
+// function. Falling back to a simple loop works around this issue.
+//
+// We need to disable the type-limits warning for the inner loop when size = 0.
+
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_GCC("-Wtype-limits")
+
+template <typename T, int size>
+std::vector<T> convert_dsizes_to_vector(const Eigen::DSizes<T, size> &arr) {
+    std::vector<T> result(size);
+
+    for (size_t i = 0; i < size; i++) {
+        result[i] = arr[i];
+    }
+
+    return result;
+}
+
+template <typename T, int size>
+Eigen::DSizes<T, size> get_shape_for_array(const array &arr) {
+    Eigen::DSizes<T, size> result;
+    const T *shape = arr.shape();
+    for (size_t i = 0; i < size; i++) {
+        result[i] = shape[i];
+    }
+
+    return result;
+}
+
+PYBIND11_WARNING_POP
+
+template <typename Type>
+struct type_caster<Type, typename eigen_tensor_helper<Type>::ValidType> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using Helper = eigen_tensor_helper<Type>;
+    static constexpr auto temp_name = get_tensor_descriptor<Type, false>::value;
+    PYBIND11_TYPE_CASTER(Type, temp_name);
+
+    bool load(handle src, bool convert) {
+        if (!convert) {
+            if (!isinstance<array>(src)) {
+                return false;
+            }
+            array temp = array::ensure(src);
+            if (!temp) {
+                return false;
+            }
+
+            if (!temp.dtype().is(dtype::of<typename Type::Scalar>())) {
+                return false;
+            }
+        }
+
+        array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()> arr(
+            reinterpret_borrow<object>(src));
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+        auto data_pointer = arr.data();
+#else
+        // Handle Eigen bug
+        auto data_pointer = const_cast<typename Type::Scalar *>(arr.data());
+#endif
+
+        if (is_tensor_aligned(arr.data())) {
+            value = Eigen::TensorMap<const Type, Eigen::Aligned>(data_pointer, shape);
+        } else {
+            value = Eigen::TensorMap<const Type>(data_pointer, shape);
+        }
+
+        return true;
+    }
+
+    static handle cast(Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(const Type &&src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::reference_internal) {
+            pybind11_fail("Cannot use a reference return value policy for an rvalue");
+        }
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        bool writeable = false;
+        switch (policy) {
+            case return_value_policy::move:
+                if (std::is_const<C>::value) {
+                    pybind11_fail("Cannot move from a constant reference");
+                }
+
+                src = Helper::alloc(std::move(*src));
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::take_ownership:
+                if (std::is_const<C>::value) {
+                    // This cast is ugly, and might be UB in some cases, but we don't have an
+                    // alternative here as we must free that memory
+                    Helper::free(const_cast<Type *>(src));
+                    pybind11_fail("Cannot take ownership of a const reference");
+                }
+
+                parent_object
+                    = capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
+                writeable = true;
+                break;
+
+            case return_value_policy::copy:
+                writeable = true;
+                break;
+
+            case return_value_policy::reference:
+                parent_object = none();
+                writeable = !std::is_const<C>::value;
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                writeable = !std::is_const<C>::value;
+                break;
+
+            default:
+                pybind11_fail("pybind11 bug in eigen.h, please file a bug report");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)), src->data(), parent_object);
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+};
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<!needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+    return reinterpret_cast<StoragePointerType>(arr.data());
+#else
+    // Handle Eigen bug
+    return reinterpret_cast<StoragePointerType>(const_cast<void *>(arr.data()));
+#endif
+}
+
+template <typename StoragePointerType,
+          bool needs_writeable,
+          enable_if_t<needs_writeable, bool> = true>
+StoragePointerType get_array_data_for_type(array &arr) {
+    return reinterpret_cast<StoragePointerType>(arr.mutable_data());
+}
+
+template <typename T, typename = void>
+struct get_storage_pointer_type;
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::StoragePointerType>> {
+    using SPT = typename MapType::StoragePointerType;
+};
+
+template <typename MapType>
+struct get_storage_pointer_type<MapType, void_t<typename MapType::PointerArgType>> {
+    using SPT = typename MapType::PointerArgType;
+};
+
+template <typename Type, int Options>
+struct type_caster<Eigen::TensorMap<Type, Options>,
+                   typename eigen_tensor_helper<remove_cv_t<Type>>::ValidType> {
+    static_assert(!std::is_pointer<typename Type::Scalar>::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using MapType = Eigen::TensorMap<Type, Options>;
+    using Helper = eigen_tensor_helper<remove_cv_t<Type>>;
+
+    bool load(handle src, bool /*convert*/) {
+        // Note that we have a lot more checks here as we want to make sure to avoid copies
+        if (!isinstance<array>(src)) {
+            return false;
+        }
+        auto arr = reinterpret_borrow<array>(src);
+        if ((arr.flags() & compute_array_flag_from_tensor<Type>()) == 0) {
+            return false;
+        }
+
+        if (!arr.dtype().is(dtype::of<typename Type::Scalar>())) {
+            return false;
+        }
+
+        if (arr.ndim() != Type::NumIndices) {
+            return false;
+        }
+
+        constexpr bool is_aligned = (Options & Eigen::Aligned) != 0;
+
+        if (is_aligned && !is_tensor_aligned(arr.data())) {
+            return false;
+        }
+
+        auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
+
+        if (!Helper::is_correct_shape(shape)) {
+            return false;
+        }
+
+        if (needs_writeable && !arr.writeable()) {
+            return false;
+        }
+
+        auto result = get_array_data_for_type<typename get_storage_pointer_type<MapType>::SPT,
+                                              needs_writeable>(arr);
+
+        value.reset(new MapType(std::move(result), std::move(shape)));
+
+        return true;
+    }
+
+    static handle cast(MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &&src, return_value_policy policy, handle parent) {
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+
+    static handle cast(MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    static handle cast(const MapType *src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic) {
+            policy = return_value_policy::take_ownership;
+        } else if (policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::reference;
+        }
+        return cast_impl(src, policy, parent);
+    }
+
+    template <typename C>
+    static handle cast_impl(C *src, return_value_policy policy, handle parent) {
+        object parent_object;
+        constexpr bool writeable = !std::is_const<C>::value;
+        switch (policy) {
+            case return_value_policy::reference:
+                parent_object = none();
+                break;
+
+            case return_value_policy::reference_internal:
+                // Default should do the right thing
+                if (!parent) {
+                    pybind11_fail("Cannot use reference internal when there is no parent");
+                }
+                parent_object = reinterpret_borrow<object>(parent);
+                break;
+
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map type, must be either "
+                              "reference or reference_internal");
+        }
+
+        auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
+            convert_dsizes_to_vector(Helper::get_shape(*src)),
+            src->data(),
+            std::move(parent_object));
+
+        if (!writeable) {
+            array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+        }
+
+        return result.release();
+    }
+
+#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
+
+    static constexpr bool needs_writeable = !std::is_const<typename std::remove_pointer<
+        typename get_storage_pointer_type<MapType>::SPT>::type>::value;
+#else
+    // Handle Eigen bug
+    static constexpr bool needs_writeable = !std::is_const<Type>::value;
+#endif
+
+protected:
+    // TODO: Move to std::optional once std::optional has more support
+    std::unique_ptr<MapType> value;
+
+public:
+    static constexpr auto name = get_tensor_descriptor<Type, true, needs_writeable>::value;
+    explicit operator MapType *() { return value.get(); }
+    explicit operator MapType &() { return *value; }
+    explicit operator MapType &&() && { return std::move(*value); }
+
+    template <typename T_>
+    using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/embed.h b/lib/python3.11/site-packages/torch/include/pybind11/embed.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d29eb824954629bbfcf7fbf8c298b4288141b0f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/embed.h
@@ -0,0 +1,313 @@
+/*
+    pybind11/embed.h: Support for embedding the interpreter
+
+    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "eval.h"
+
+#include <memory>
+#include <vector>
+
+#if defined(PYPY_VERSION)
+#    error Embedding the interpreter is not supported with PyPy
+#endif
+
+#define PYBIND11_EMBEDDED_MODULE_IMPL(name)                                                       \
+    extern "C" PyObject *pybind11_init_impl_##name();                                             \
+    extern "C" PyObject *pybind11_init_impl_##name() { return pybind11_init_wrapper_##name(); }
+
+/** \rst
+    Add a new module to the table of builtins for the interpreter. Must be
+    defined in global scope. The first macro parameter is the name of the
+    module (without quotes). The second parameter is the variable which will
+    be used as the interface to add functions and classes to the module.
+
+    .. code-block:: cpp
+
+        PYBIND11_EMBEDDED_MODULE(example, m) {
+            // ... initialize functions and classes here
+            m.def("foo", []() {
+                return "Hello, World!";
+            });
+        }
+ \endrst */
+#define PYBIND11_EMBEDDED_MODULE(name, variable)                                                  \
+    static ::pybind11::module_::module_def PYBIND11_CONCAT(pybind11_module_def_, name);           \
+    static void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_ &);                     \
+    static PyObject PYBIND11_CONCAT(*pybind11_init_wrapper_, name)() {                            \
+        auto m = ::pybind11::module_::create_extension_module(                                    \
+            PYBIND11_TOSTRING(name), nullptr, &PYBIND11_CONCAT(pybind11_module_def_, name));      \
+        try {                                                                                     \
+            PYBIND11_CONCAT(pybind11_init_, name)(m);                                             \
+            return m.ptr();                                                                       \
+        }                                                                                         \
+        PYBIND11_CATCH_INIT_EXCEPTIONS                                                            \
+    }                                                                                             \
+    PYBIND11_EMBEDDED_MODULE_IMPL(name)                                                           \
+    ::pybind11::detail::embedded_module PYBIND11_CONCAT(pybind11_module_, name)(                  \
+        PYBIND11_TOSTRING(name), PYBIND11_CONCAT(pybind11_init_impl_, name));                     \
+    void PYBIND11_CONCAT(pybind11_init_, name)(::pybind11::module_                                \
+                                               & variable) // NOLINT(bugprone-macro-parentheses)
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Python 2.7/3.x compatible version of `PyImport_AppendInittab` and error checks.
+struct embedded_module {
+    using init_t = PyObject *(*) ();
+    embedded_module(const char *name, init_t init) {
+        if (Py_IsInitialized() != 0) {
+            pybind11_fail("Can't add new modules after the interpreter has been initialized");
+        }
+
+        auto result = PyImport_AppendInittab(name, init);
+        if (result == -1) {
+            pybind11_fail("Insufficient memory to add a new module");
+        }
+    }
+};
+
+struct wide_char_arg_deleter {
+    void operator()(wchar_t *ptr) const {
+        // API docs: https://docs.python.org/3/c-api/sys.html#c.Py_DecodeLocale
+        PyMem_RawFree(ptr);
+    }
+};
+
+inline wchar_t *widen_chars(const char *safe_arg) {
+    wchar_t *widened_arg = Py_DecodeLocale(safe_arg, nullptr);
+    return widened_arg;
+}
+
+inline void precheck_interpreter() {
+    if (Py_IsInitialized() != 0) {
+        pybind11_fail("The interpreter is already running");
+    }
+}
+
+#if !defined(PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX)
+#    define PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX (0x03080000)
+#endif
+
+#if PY_VERSION_HEX < PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX
+inline void initialize_interpreter_pre_pyconfig(bool init_signal_handlers,
+                                                int argc,
+                                                const char *const *argv,
+                                                bool add_program_dir_to_path) {
+    detail::precheck_interpreter();
+    Py_InitializeEx(init_signal_handlers ? 1 : 0);
+
+    // Before it was special-cased in python 3.8, passing an empty or null argv
+    // caused a segfault, so we have to reimplement the special case ourselves.
+    bool special_case = (argv == nullptr || argc <= 0);
+
+    const char *const empty_argv[]{"\0"};
+    const char *const *safe_argv = special_case ? empty_argv : argv;
+    if (special_case) {
+        argc = 1;
+    }
+
+    auto argv_size = static_cast<size_t>(argc);
+    // SetArgv* on python 3 takes wchar_t, so we have to convert.
+    std::unique_ptr<wchar_t *[]> widened_argv(new wchar_t *[argv_size]);
+    std::vector<std::unique_ptr<wchar_t[], detail::wide_char_arg_deleter>> widened_argv_entries;
+    widened_argv_entries.reserve(argv_size);
+    for (size_t ii = 0; ii < argv_size; ++ii) {
+        widened_argv_entries.emplace_back(detail::widen_chars(safe_argv[ii]));
+        if (!widened_argv_entries.back()) {
+            // A null here indicates a character-encoding failure or the python
+            // interpreter out of memory. Give up.
+            return;
+        }
+        widened_argv[ii] = widened_argv_entries.back().get();
+    }
+
+    auto *pysys_argv = widened_argv.get();
+
+    PySys_SetArgvEx(argc, pysys_argv, static_cast<int>(add_program_dir_to_path));
+}
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if PY_VERSION_HEX >= PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX
+inline void initialize_interpreter(PyConfig *config,
+                                   int argc = 0,
+                                   const char *const *argv = nullptr,
+                                   bool add_program_dir_to_path = true) {
+    detail::precheck_interpreter();
+    PyStatus status = PyConfig_SetBytesArgv(config, argc, const_cast<char *const *>(argv));
+    if (PyStatus_Exception(status) != 0) {
+        // A failure here indicates a character-encoding failure or the python
+        // interpreter out of memory. Give up.
+        PyConfig_Clear(config);
+        throw std::runtime_error(PyStatus_IsError(status) != 0 ? status.err_msg
+                                                               : "Failed to prepare CPython");
+    }
+    status = Py_InitializeFromConfig(config);
+    if (PyStatus_Exception(status) != 0) {
+        PyConfig_Clear(config);
+        throw std::runtime_error(PyStatus_IsError(status) != 0 ? status.err_msg
+                                                               : "Failed to init CPython");
+    }
+    if (add_program_dir_to_path) {
+        PyRun_SimpleString("import sys, os.path; "
+                           "sys.path.insert(0, "
+                           "os.path.abspath(os.path.dirname(sys.argv[0])) "
+                           "if sys.argv and os.path.exists(sys.argv[0]) else '')");
+    }
+    PyConfig_Clear(config);
+}
+#endif
+
+/** \rst
+    Initialize the Python interpreter. No other pybind11 or CPython API functions can be
+    called before this is done; with the exception of `PYBIND11_EMBEDDED_MODULE`. The
+    optional `init_signal_handlers` parameter can be used to skip the registration of
+    signal handlers (see the `Python documentation`_ for details). Calling this function
+    again after the interpreter has already been initialized is a fatal error.
+
+    If initializing the Python interpreter fails, then the program is terminated.  (This
+    is controlled by the CPython runtime and is an exception to pybind11's normal behavior
+    of throwing exceptions on errors.)
+
+    The remaining optional parameters, `argc`, `argv`, and `add_program_dir_to_path` are
+    used to populate ``sys.argv`` and ``sys.path``.
+    See the |PySys_SetArgvEx documentation|_ for details.
+
+    .. _Python documentation: https://docs.python.org/3/c-api/init.html#c.Py_InitializeEx
+    .. |PySys_SetArgvEx documentation| replace:: ``PySys_SetArgvEx`` documentation
+    .. _PySys_SetArgvEx documentation: https://docs.python.org/3/c-api/init.html#c.PySys_SetArgvEx
+ \endrst */
+inline void initialize_interpreter(bool init_signal_handlers = true,
+                                   int argc = 0,
+                                   const char *const *argv = nullptr,
+                                   bool add_program_dir_to_path = true) {
+#if PY_VERSION_HEX < PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX
+    detail::initialize_interpreter_pre_pyconfig(
+        init_signal_handlers, argc, argv, add_program_dir_to_path);
+#else
+    PyConfig config;
+    PyConfig_InitPythonConfig(&config);
+    // See PR #4473 for background
+    config.parse_argv = 0;
+
+    config.install_signal_handlers = init_signal_handlers ? 1 : 0;
+    initialize_interpreter(&config, argc, argv, add_program_dir_to_path);
+#endif
+}
+
+/** \rst
+    Shut down the Python interpreter. No pybind11 or CPython API functions can be called
+    after this. In addition, pybind11 objects must not outlive the interpreter:
+
+    .. code-block:: cpp
+
+        { // BAD
+            py::initialize_interpreter();
+            auto hello = py::str("Hello, World!");
+            py::finalize_interpreter();
+        } // <-- BOOM, hello's destructor is called after interpreter shutdown
+
+        { // GOOD
+            py::initialize_interpreter();
+            { // scoped
+                auto hello = py::str("Hello, World!");
+            } // <-- OK, hello is cleaned up properly
+            py::finalize_interpreter();
+        }
+
+        { // BETTER
+            py::scoped_interpreter guard{};
+            auto hello = py::str("Hello, World!");
+        }
+
+    .. warning::
+
+        The interpreter can be restarted by calling `initialize_interpreter` again.
+        Modules created using pybind11 can be safely re-initialized. However, Python
+        itself cannot completely unload binary extension modules and there are several
+        caveats with regard to interpreter restarting. All the details can be found
+        in the CPython documentation. In short, not all interpreter memory may be
+        freed, either due to reference cycles or user-created global data.
+
+ \endrst */
+inline void finalize_interpreter() {
+    // Get the internals pointer (without creating it if it doesn't exist).  It's possible for the
+    // internals to be created during Py_Finalize() (e.g. if a py::capsule calls `get_internals()`
+    // during destruction), so we get the pointer-pointer here and check it after Py_Finalize().
+    detail::internals **internals_ptr_ptr = detail::get_internals_pp();
+    // It could also be stashed in state_dict, so look there too:
+    if (object internals_obj
+        = get_internals_obj_from_state_dict(detail::get_python_state_dict())) {
+        internals_ptr_ptr = detail::get_internals_pp_from_capsule(internals_obj);
+    }
+    // Local internals contains data managed by the current interpreter, so we must clear them to
+    // avoid undefined behaviors when initializing another interpreter
+    detail::get_local_internals().registered_types_cpp.clear();
+    detail::get_local_internals().registered_exception_translators.clear();
+
+    Py_Finalize();
+
+    if (internals_ptr_ptr) {
+        delete *internals_ptr_ptr;
+        *internals_ptr_ptr = nullptr;
+    }
+}
+
+/** \rst
+    Scope guard version of `initialize_interpreter` and `finalize_interpreter`.
+    This a move-only guard and only a single instance can exist.
+
+    See `initialize_interpreter` for a discussion of its constructor arguments.
+
+    .. code-block:: cpp
+
+        #include <pybind11/embed.h>
+
+        int main() {
+            py::scoped_interpreter guard{};
+            py::print(Hello, World!);
+        } // <-- interpreter shutdown
+ \endrst */
+class scoped_interpreter {
+public:
+    explicit scoped_interpreter(bool init_signal_handlers = true,
+                                int argc = 0,
+                                const char *const *argv = nullptr,
+                                bool add_program_dir_to_path = true) {
+        initialize_interpreter(init_signal_handlers, argc, argv, add_program_dir_to_path);
+    }
+
+#if PY_VERSION_HEX >= PYBIND11_PYCONFIG_SUPPORT_PY_VERSION_HEX
+    explicit scoped_interpreter(PyConfig *config,
+                                int argc = 0,
+                                const char *const *argv = nullptr,
+                                bool add_program_dir_to_path = true) {
+        initialize_interpreter(config, argc, argv, add_program_dir_to_path);
+    }
+#endif
+
+    scoped_interpreter(const scoped_interpreter &) = delete;
+    scoped_interpreter(scoped_interpreter &&other) noexcept { other.is_valid = false; }
+    scoped_interpreter &operator=(const scoped_interpreter &) = delete;
+    scoped_interpreter &operator=(scoped_interpreter &&) = delete;
+
+    ~scoped_interpreter() {
+        if (is_valid) {
+            finalize_interpreter();
+        }
+    }
+
+private:
+    bool is_valid = true;
+};
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/eval.h b/lib/python3.11/site-packages/torch/include/pybind11/eval.h
new file mode 100644
index 0000000000000000000000000000000000000000..bd5f981f53b87dd8c378a20dcd8109a6b3172cfe
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/eval.h
@@ -0,0 +1,156 @@
+/*
+    pybind11/eval.h: Support for evaluating Python expressions and statements
+    from strings and files
+
+    Copyright (c) 2016 Klemens Morgenstern <klemens.morgenstern@ed-chemnitz.de> and
+                       Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <utility>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline void ensure_builtins_in_globals(object &global) {
+#if defined(PYPY_VERSION) || PY_VERSION_HEX < 0x03080000
+    // Running exec and eval adds `builtins` module under `__builtins__` key to
+    // globals if not yet present.  Python 3.8 made PyRun_String behave
+    // similarly. Let's also do that for older versions, for consistency. This
+    // was missing from PyPy3.8 7.3.7.
+    if (!global.contains("__builtins__"))
+        global["__builtins__"] = module_::import(PYBIND11_BUILTINS_MODULE);
+#else
+    (void) global;
+#endif
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+enum eval_mode {
+    /// Evaluate a string containing an isolated expression
+    eval_expr,
+
+    /// Evaluate a string containing a single statement. Returns \c none
+    eval_single_statement,
+
+    /// Evaluate a string containing a sequence of statement. Returns \c none
+    eval_statements
+};
+
+template <eval_mode mode = eval_expr>
+object eval(const str &expr, object global = globals(), object local = object()) {
+    if (!local) {
+        local = global;
+    }
+
+    detail::ensure_builtins_in_globals(global);
+
+    /* PyRun_String does not accept a PyObject / encoding specifier,
+       this seems to be the only alternative */
+    std::string buffer = "# -*- coding: utf-8 -*-\n" + (std::string) expr;
+
+    int start = 0;
+    switch (mode) {
+        case eval_expr:
+            start = Py_eval_input;
+            break;
+        case eval_single_statement:
+            start = Py_single_input;
+            break;
+        case eval_statements:
+            start = Py_file_input;
+            break;
+        default:
+            pybind11_fail("invalid evaluation mode");
+    }
+
+    PyObject *result = PyRun_String(buffer.c_str(), start, global.ptr(), local.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+template <eval_mode mode = eval_expr, size_t N>
+object eval(const char (&s)[N], object global = globals(), object local = object()) {
+    /* Support raw string literals by removing common leading whitespace */
+    auto expr = (s[0] == '\n') ? str(module_::import("textwrap").attr("dedent")(s)) : str(s);
+    return eval<mode>(expr, std::move(global), std::move(local));
+}
+
+inline void exec(const str &expr, object global = globals(), object local = object()) {
+    eval<eval_statements>(expr, std::move(global), std::move(local));
+}
+
+template <size_t N>
+void exec(const char (&s)[N], object global = globals(), object local = object()) {
+    eval<eval_statements>(s, std::move(global), std::move(local));
+}
+
+#if defined(PYPY_VERSION)
+template <eval_mode mode = eval_statements>
+object eval_file(str, object, object) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+template <eval_mode mode = eval_statements>
+object eval_file(str, object) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+template <eval_mode mode = eval_statements>
+object eval_file(str) {
+    pybind11_fail("eval_file not supported in PyPy3. Use eval");
+}
+#else
+template <eval_mode mode = eval_statements>
+object eval_file(str fname, object global = globals(), object local = object()) {
+    if (!local) {
+        local = global;
+    }
+
+    detail::ensure_builtins_in_globals(global);
+
+    int start = 0;
+    switch (mode) {
+        case eval_expr:
+            start = Py_eval_input;
+            break;
+        case eval_single_statement:
+            start = Py_single_input;
+            break;
+        case eval_statements:
+            start = Py_file_input;
+            break;
+        default:
+            pybind11_fail("invalid evaluation mode");
+    }
+
+    int closeFile = 1;
+    std::string fname_str = (std::string) fname;
+    FILE *f = _Py_fopen_obj(fname.ptr(), "r");
+    if (!f) {
+        PyErr_Clear();
+        pybind11_fail("File \"" + fname_str + "\" could not be opened!");
+    }
+
+    if (!global.contains("__file__")) {
+        global["__file__"] = std::move(fname);
+    }
+
+    PyObject *result
+        = PyRun_FileEx(f, fname_str.c_str(), start, global.ptr(), local.ptr(), closeFile);
+
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+#endif
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/functional.h b/lib/python3.11/site-packages/torch/include/pybind11/functional.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b3610117c75dd569928c8c78aff275582e3b342
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/functional.h
@@ -0,0 +1,149 @@
+/*
+    pybind11/functional.h: std::function<> support
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#define PYBIND11_HAS_TYPE_CASTER_STD_FUNCTION_SPECIALIZATIONS
+
+#include "pybind11.h"
+
+#include <functional>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+PYBIND11_NAMESPACE_BEGIN(type_caster_std_function_specializations)
+
+// ensure GIL is held during functor destruction
+struct func_handle {
+    function f;
+#if !(defined(_MSC_VER) && _MSC_VER == 1916 && defined(PYBIND11_CPP17))
+    // This triggers a syntax error under very special conditions (very weird indeed).
+    explicit
+#endif
+        func_handle(function &&f_) noexcept
+        : f(std::move(f_)) {
+    }
+    func_handle(const func_handle &f_) { operator=(f_); }
+    func_handle &operator=(const func_handle &f_) {
+        gil_scoped_acquire acq;
+        f = f_.f;
+        return *this;
+    }
+    ~func_handle() {
+        gil_scoped_acquire acq;
+        function kill_f(std::move(f));
+    }
+};
+
+// to emulate 'move initialization capture' in C++11
+struct func_wrapper_base {
+    func_handle hfunc;
+    explicit func_wrapper_base(func_handle &&hf) noexcept : hfunc(hf) {}
+};
+
+template <typename Return, typename... Args>
+struct func_wrapper : func_wrapper_base {
+    using func_wrapper_base::func_wrapper_base;
+    Return operator()(Args... args) const {
+        gil_scoped_acquire acq;
+        // casts the returned object as a rvalue to the return type
+        return hfunc.f(std::forward<Args>(args)...).template cast<Return>();
+    }
+};
+
+PYBIND11_NAMESPACE_END(type_caster_std_function_specializations)
+
+template <typename Return, typename... Args>
+struct type_caster<std::function<Return(Args...)>> {
+    using type = std::function<Return(Args...)>;
+    using retval_type = conditional_t<std::is_same<Return, void>::value, void_type, Return>;
+    using function_type = Return (*)(Args...);
+
+public:
+    bool load(handle src, bool convert) {
+        if (src.is_none()) {
+            // Defer accepting None to other overloads (if we aren't in convert mode):
+            if (!convert) {
+                return false;
+            }
+            return true;
+        }
+
+        if (!isinstance<function>(src)) {
+            return false;
+        }
+
+        auto func = reinterpret_borrow<function>(src);
+
+        /*
+           When passing a C++ function as an argument to another C++
+           function via Python, every function call would normally involve
+           a full C++ -> Python -> C++ roundtrip, which can be prohibitive.
+           Here, we try to at least detect the case where the function is
+           stateless (i.e. function pointer or lambda function without
+           captured variables), in which case the roundtrip can be avoided.
+         */
+        if (auto cfunc = func.cpp_function()) {
+            auto *cfunc_self = PyCFunction_GET_SELF(cfunc.ptr());
+            if (cfunc_self == nullptr) {
+                PyErr_Clear();
+            } else if (isinstance<capsule>(cfunc_self)) {
+                auto c = reinterpret_borrow<capsule>(cfunc_self);
+
+                function_record *rec = nullptr;
+                // Check that we can safely reinterpret the capsule into a function_record
+                if (detail::is_function_record_capsule(c)) {
+                    rec = c.get_pointer<function_record>();
+                }
+
+                while (rec != nullptr) {
+                    if (rec->is_stateless
+                        && same_type(typeid(function_type),
+                                     *reinterpret_cast<const std::type_info *>(rec->data[1]))) {
+                        struct capture {
+                            function_type f;
+                        };
+                        value = ((capture *) &rec->data)->f;
+                        return true;
+                    }
+                    rec = rec->next;
+                }
+            }
+            // PYPY segfaults here when passing builtin function like sum.
+            // Raising an fail exception here works to prevent the segfault, but only on gcc.
+            // See PR #1413 for full details
+        }
+
+        value = type_caster_std_function_specializations::func_wrapper<Return, Args...>(
+            type_caster_std_function_specializations::func_handle(std::move(func)));
+        return true;
+    }
+
+    template <typename Func>
+    static handle cast(Func &&f_, return_value_policy policy, handle /* parent */) {
+        if (!f_) {
+            return none().release();
+        }
+
+        auto result = f_.template target<function_type>();
+        if (result) {
+            return cpp_function(*result, policy).release();
+        }
+        return cpp_function(std::forward<Func>(f_), policy).release();
+    }
+
+    PYBIND11_TYPE_CASTER(type,
+                         const_name("Callable[[")
+                             + ::pybind11::detail::concat(make_caster<Args>::name...)
+                             + const_name("], ") + make_caster<retval_type>::name
+                             + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/gil.h b/lib/python3.11/site-packages/torch/include/pybind11/gil.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b0edaee4e5521569fd985f273e3892fdcefe17f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/gil.h
@@ -0,0 +1,219 @@
+/*
+    pybind11/gil.h: RAII helpers for managing the GIL
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+
+#include <cassert>
+
+#if !defined(PYBIND11_SIMPLE_GIL_MANAGEMENT)
+#    include "detail/internals.h"
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// forward declarations
+PyThreadState *get_thread_state_unchecked();
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if !defined(PYBIND11_SIMPLE_GIL_MANAGEMENT)
+
+/* The functions below essentially reproduce the PyGILState_* API using a RAII
+ * pattern, but there are a few important differences:
+ *
+ * 1. When acquiring the GIL from an non-main thread during the finalization
+ *    phase, the GILState API blindly terminates the calling thread, which
+ *    is often not what is wanted. This API does not do this.
+ *
+ * 2. The gil_scoped_release function can optionally cut the relationship
+ *    of a PyThreadState and its associated thread, which allows moving it to
+ *    another thread (this is a fairly rare/advanced use case).
+ *
+ * 3. The reference count of an acquired thread state can be controlled. This
+ *    can be handy to prevent cases where callbacks issued from an external
+ *    thread would otherwise constantly construct and destroy thread state data
+ *    structures.
+ *
+ * See the Python bindings of NanoGUI (http://github.com/wjakob/nanogui) for an
+ * example which uses features 2 and 3 to migrate the Python thread of
+ * execution to another thread (to run the event loop on the original thread,
+ * in this case).
+ */
+
+class gil_scoped_acquire {
+public:
+    PYBIND11_NOINLINE gil_scoped_acquire() {
+        auto &internals = detail::get_internals();
+        tstate = (PyThreadState *) PYBIND11_TLS_GET_VALUE(internals.tstate);
+
+        if (!tstate) {
+            /* Check if the GIL was acquired using the PyGILState_* API instead (e.g. if
+               calling from a Python thread). Since we use a different key, this ensures
+               we don't create a new thread state and deadlock in PyEval_AcquireThread
+               below. Note we don't save this state with internals.tstate, since we don't
+               create it we would fail to clear it (its reference count should be > 0). */
+            tstate = PyGILState_GetThisThreadState();
+        }
+
+        if (!tstate) {
+            tstate = PyThreadState_New(internals.istate);
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            if (!tstate) {
+                pybind11_fail("scoped_acquire: could not create thread state!");
+            }
+#    endif
+            tstate->gilstate_counter = 0;
+            PYBIND11_TLS_REPLACE_VALUE(internals.tstate, tstate);
+        } else {
+            release = detail::get_thread_state_unchecked() != tstate;
+        }
+
+        if (release) {
+            PyEval_AcquireThread(tstate);
+        }
+
+        inc_ref();
+    }
+
+    gil_scoped_acquire(const gil_scoped_acquire &) = delete;
+    gil_scoped_acquire &operator=(const gil_scoped_acquire &) = delete;
+
+    void inc_ref() { ++tstate->gilstate_counter; }
+
+    PYBIND11_NOINLINE void dec_ref() {
+        --tstate->gilstate_counter;
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+        if (detail::get_thread_state_unchecked() != tstate) {
+            pybind11_fail("scoped_acquire::dec_ref(): thread state must be current!");
+        }
+        if (tstate->gilstate_counter < 0) {
+            pybind11_fail("scoped_acquire::dec_ref(): reference count underflow!");
+        }
+#    endif
+        if (tstate->gilstate_counter == 0) {
+#    if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+            if (!release) {
+                pybind11_fail("scoped_acquire::dec_ref(): internal error!");
+            }
+#    endif
+            PyThreadState_Clear(tstate);
+            if (active) {
+                PyThreadState_DeleteCurrent();
+            }
+            PYBIND11_TLS_DELETE_VALUE(detail::get_internals().tstate);
+            release = false;
+        }
+    }
+
+    /// This method will disable the PyThreadState_DeleteCurrent call and the
+    /// GIL won't be acquired. This method should be used if the interpreter
+    /// could be shutting down when this is called, as thread deletion is not
+    /// allowed during shutdown. Check _Py_IsFinalizing() on Python 3.7+, and
+    /// protect subsequent code.
+    PYBIND11_NOINLINE void disarm() { active = false; }
+
+    PYBIND11_NOINLINE ~gil_scoped_acquire() {
+        dec_ref();
+        if (release) {
+            PyEval_SaveThread();
+        }
+    }
+
+private:
+    PyThreadState *tstate = nullptr;
+    bool release = true;
+    bool active = true;
+};
+
+class gil_scoped_release {
+public:
+    // PRECONDITION: The GIL must be held when this constructor is called.
+    explicit gil_scoped_release(bool disassoc = false) : disassoc(disassoc) {
+        assert(PyGILState_Check());
+        // `get_internals()` must be called here unconditionally in order to initialize
+        // `internals.tstate` for subsequent `gil_scoped_acquire` calls. Otherwise, an
+        // initialization race could occur as multiple threads try `gil_scoped_acquire`.
+        auto &internals = detail::get_internals();
+        // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer)
+        tstate = PyEval_SaveThread();
+        if (disassoc) {
+            // Python >= 3.7 can remove this, it's an int before 3.7
+            // NOLINTNEXTLINE(readability-qualified-auto)
+            auto key = internals.tstate;
+            PYBIND11_TLS_DELETE_VALUE(key);
+        }
+    }
+
+    gil_scoped_release(const gil_scoped_release &) = delete;
+    gil_scoped_release &operator=(const gil_scoped_release &) = delete;
+
+    /// This method will disable the PyThreadState_DeleteCurrent call and the
+    /// GIL won't be acquired. This method should be used if the interpreter
+    /// could be shutting down when this is called, as thread deletion is not
+    /// allowed during shutdown. Check _Py_IsFinalizing() on Python 3.7+, and
+    /// protect subsequent code.
+    PYBIND11_NOINLINE void disarm() { active = false; }
+
+    ~gil_scoped_release() {
+        if (!tstate) {
+            return;
+        }
+        // `PyEval_RestoreThread()` should not be called if runtime is finalizing
+        if (active) {
+            PyEval_RestoreThread(tstate);
+        }
+        if (disassoc) {
+            // Python >= 3.7 can remove this, it's an int before 3.7
+            // NOLINTNEXTLINE(readability-qualified-auto)
+            auto key = detail::get_internals().tstate;
+            PYBIND11_TLS_REPLACE_VALUE(key, tstate);
+        }
+    }
+
+private:
+    PyThreadState *tstate;
+    bool disassoc;
+    bool active = true;
+};
+
+#else // PYBIND11_SIMPLE_GIL_MANAGEMENT
+
+class gil_scoped_acquire {
+    PyGILState_STATE state;
+
+public:
+    gil_scoped_acquire() : state{PyGILState_Ensure()} {}
+    gil_scoped_acquire(const gil_scoped_acquire &) = delete;
+    gil_scoped_acquire &operator=(const gil_scoped_acquire &) = delete;
+    ~gil_scoped_acquire() { PyGILState_Release(state); }
+    void disarm() {}
+};
+
+class gil_scoped_release {
+    PyThreadState *state;
+
+public:
+    // PRECONDITION: The GIL must be held when this constructor is called.
+    gil_scoped_release() {
+        assert(PyGILState_Check());
+        state = PyEval_SaveThread();
+    }
+    gil_scoped_release(const gil_scoped_release &) = delete;
+    gil_scoped_release &operator=(const gil_scoped_release &) = delete;
+    ~gil_scoped_release() { PyEval_RestoreThread(state); }
+    void disarm() {}
+};
+
+#endif // PYBIND11_SIMPLE_GIL_MANAGEMENT
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/gil_safe_call_once.h b/lib/python3.11/site-packages/torch/include/pybind11/gil_safe_call_once.h
new file mode 100644
index 0000000000000000000000000000000000000000..5f9e1b03c64bc18dd573708cd670df3a7b7bde66
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/gil_safe_call_once.h
@@ -0,0 +1,100 @@
+// Copyright (c) 2023 The pybind Community.
+
+#pragma once
+
+#include "detail/common.h"
+#include "gil.h"
+
+#include <cassert>
+#include <mutex>
+
+#ifdef Py_GIL_DISABLED
+#    include <atomic>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+// Use the `gil_safe_call_once_and_store` class below instead of the naive
+//
+//   static auto imported_obj = py::module_::import("module_name"); // BAD, DO NOT USE!
+//
+// which has two serious issues:
+//
+//     1. Py_DECREF() calls potentially after the Python interpreter was finalized already, and
+//     2. deadlocks in multi-threaded processes (because of missing lock ordering).
+//
+// The following alternative avoids both problems:
+//
+//   PYBIND11_CONSTINIT static py::gil_safe_call_once_and_store<py::object> storage;
+//   auto &imported_obj = storage // Do NOT make this `static`!
+//       .call_once_and_store_result([]() {
+//           return py::module_::import("module_name");
+//       })
+//       .get_stored();
+//
+// The parameter of `call_once_and_store_result()` must be callable. It can make
+// CPython API calls, and in particular, it can temporarily release the GIL.
+//
+// `T` can be any C++ type, it does not have to involve CPython API types.
+//
+// The behavior with regard to signals, e.g. `SIGINT` (`KeyboardInterrupt`),
+// is not ideal. If the main thread is the one to actually run the `Callable`,
+// then a `KeyboardInterrupt` will interrupt it if it is running normal Python
+// code. The situation is different if a non-main thread runs the
+// `Callable`, and then the main thread starts waiting for it to complete:
+// a `KeyboardInterrupt` will not interrupt the non-main thread, but it will
+// get processed only when it is the main thread's turn again and it is running
+// normal Python code. However, this will be unnoticeable for quick call-once
+// functions, which is usually the case.
+template <typename T>
+class gil_safe_call_once_and_store {
+public:
+    // PRECONDITION: The GIL must be held when `call_once_and_store_result()` is called.
+    template <typename Callable>
+    gil_safe_call_once_and_store &call_once_and_store_result(Callable &&fn) {
+        if (!is_initialized_) { // This read is guarded by the GIL.
+            // Multiple threads may enter here, because the GIL is released in the next line and
+            // CPython API calls in the `fn()` call below may release and reacquire the GIL.
+            gil_scoped_release gil_rel; // Needed to establish lock ordering.
+            std::call_once(once_flag_, [&] {
+                // Only one thread will ever enter here.
+                gil_scoped_acquire gil_acq;
+                ::new (storage_) T(fn()); // fn may release, but will reacquire, the GIL.
+                is_initialized_ = true;   // This write is guarded by the GIL.
+            });
+            // All threads will observe `is_initialized_` as true here.
+        }
+        // Intentionally not returning `T &` to ensure the calling code is self-documenting.
+        return *this;
+    }
+
+    // This must only be called after `call_once_and_store_result()` was called.
+    T &get_stored() {
+        assert(is_initialized_);
+        PYBIND11_WARNING_PUSH
+#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ < 5
+        // Needed for gcc 4.8.5
+        PYBIND11_WARNING_DISABLE_GCC("-Wstrict-aliasing")
+#endif
+        return *reinterpret_cast<T *>(storage_);
+        PYBIND11_WARNING_POP
+    }
+
+    constexpr gil_safe_call_once_and_store() = default;
+    PYBIND11_DTOR_CONSTEXPR ~gil_safe_call_once_and_store() = default;
+
+private:
+    alignas(T) char storage_[sizeof(T)] = {};
+    std::once_flag once_flag_ = {};
+#ifdef Py_GIL_DISABLED
+    std::atomic_bool
+#else
+    bool
+#endif
+        is_initialized_{false};
+    // The `is_initialized_`-`storage_` pair is very similar to `std::optional`,
+    // but the latter does not have the triviality properties of former,
+    // therefore `std::optional` is not a viable alternative here.
+};
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/iostream.h b/lib/python3.11/site-packages/torch/include/pybind11/iostream.h
new file mode 100644
index 0000000000000000000000000000000000000000..1878089e3171bcfbd575ff4d7f925f981489316c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/iostream.h
@@ -0,0 +1,265 @@
+/*
+    pybind11/iostream.h -- Tools to assist with redirecting cout and cerr to Python
+
+    Copyright (c) 2017 Henry F. Schreiner
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+
+    WARNING: The implementation in this file is NOT thread safe. Multiple
+    threads writing to a redirected ostream concurrently cause data races
+    and potentially buffer overflows. Therefore it is currently a requirement
+    that all (possibly) concurrent redirected ostream writes are protected by
+    a mutex.
+    #HelpAppreciated: Work on iostream.h thread safety.
+    For more background see the discussions under
+    https://github.com/pybind/pybind11/pull/2982 and
+    https://github.com/pybind/pybind11/pull/2995.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+#include <algorithm>
+#include <cstring>
+#include <iostream>
+#include <iterator>
+#include <memory>
+#include <ostream>
+#include <streambuf>
+#include <string>
+#include <utility>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Buffer that writes to Python instead of C++
+class pythonbuf : public std::streambuf {
+private:
+    using traits_type = std::streambuf::traits_type;
+
+    const size_t buf_size;
+    std::unique_ptr<char[]> d_buffer;
+    object pywrite;
+    object pyflush;
+
+    int overflow(int c) override {
+        if (!traits_type::eq_int_type(c, traits_type::eof())) {
+            *pptr() = traits_type::to_char_type(c);
+            pbump(1);
+        }
+        return sync() == 0 ? traits_type::not_eof(c) : traits_type::eof();
+    }
+
+    // Computes how many bytes at the end of the buffer are part of an
+    // incomplete sequence of UTF-8 bytes.
+    // Precondition: pbase() < pptr()
+    size_t utf8_remainder() const {
+        const auto rbase = std::reverse_iterator<char *>(pbase());
+        const auto rpptr = std::reverse_iterator<char *>(pptr());
+        auto is_ascii = [](char c) { return (static_cast<unsigned char>(c) & 0x80) == 0x00; };
+        auto is_leading = [](char c) { return (static_cast<unsigned char>(c) & 0xC0) == 0xC0; };
+        auto is_leading_2b = [](char c) { return static_cast<unsigned char>(c) <= 0xDF; };
+        auto is_leading_3b = [](char c) { return static_cast<unsigned char>(c) <= 0xEF; };
+        // If the last character is ASCII, there are no incomplete code points
+        if (is_ascii(*rpptr)) {
+            return 0;
+        }
+        // Otherwise, work back from the end of the buffer and find the first
+        // UTF-8 leading byte
+        const auto rpend = rbase - rpptr >= 3 ? rpptr + 3 : rbase;
+        const auto leading = std::find_if(rpptr, rpend, is_leading);
+        if (leading == rbase) {
+            return 0;
+        }
+        const auto dist = static_cast<size_t>(leading - rpptr);
+        size_t remainder = 0;
+
+        if (dist == 0) {
+            remainder = 1; // 1-byte code point is impossible
+        } else if (dist == 1) {
+            remainder = is_leading_2b(*leading) ? 0 : dist + 1;
+        } else if (dist == 2) {
+            remainder = is_leading_3b(*leading) ? 0 : dist + 1;
+        }
+        // else if (dist >= 3), at least 4 bytes before encountering an UTF-8
+        // leading byte, either no remainder or invalid UTF-8.
+        // Invalid UTF-8 will cause an exception later when converting
+        // to a Python string, so that's not handled here.
+        return remainder;
+    }
+
+    // This function must be non-virtual to be called in a destructor.
+    int _sync() {
+        if (pbase() != pptr()) { // If buffer is not empty
+            gil_scoped_acquire tmp;
+            // This subtraction cannot be negative, so dropping the sign.
+            auto size = static_cast<size_t>(pptr() - pbase());
+            size_t remainder = utf8_remainder();
+
+            if (size > remainder) {
+                str line(pbase(), size - remainder);
+                pywrite(std::move(line));
+                pyflush();
+            }
+
+            // Copy the remainder at the end of the buffer to the beginning:
+            if (remainder > 0) {
+                std::memmove(pbase(), pptr() - remainder, remainder);
+            }
+            setp(pbase(), epptr());
+            pbump(static_cast<int>(remainder));
+        }
+        return 0;
+    }
+
+    int sync() override { return _sync(); }
+
+public:
+    explicit pythonbuf(const object &pyostream, size_t buffer_size = 1024)
+        : buf_size(buffer_size), d_buffer(new char[buf_size]), pywrite(pyostream.attr("write")),
+          pyflush(pyostream.attr("flush")) {
+        setp(d_buffer.get(), d_buffer.get() + buf_size - 1);
+    }
+
+    pythonbuf(pythonbuf &&) = default;
+
+    /// Sync before destroy
+    ~pythonbuf() override { _sync(); }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+    This a move-only guard that redirects output.
+
+    .. code-block:: cpp
+
+        #include <pybind11/iostream.h>
+
+        ...
+
+        {
+            py::scoped_ostream_redirect output;
+            std::cout << "Hello, World!"; // Python stdout
+        } // <-- return std::cout to normal
+
+    You can explicitly pass the c++ stream and the python object,
+    for example to guard stderr instead.
+
+    .. code-block:: cpp
+
+        {
+            py::scoped_ostream_redirect output{
+                std::cerr, py::module::import("sys").attr("stderr")};
+            std::cout << "Hello, World!";
+        }
+ \endrst */
+class scoped_ostream_redirect {
+protected:
+    std::streambuf *old;
+    std::ostream &costream;
+    detail::pythonbuf buffer;
+
+public:
+    explicit scoped_ostream_redirect(std::ostream &costream = std::cout,
+                                     const object &pyostream
+                                     = module_::import("sys").attr("stdout"))
+        : costream(costream), buffer(pyostream) {
+        old = costream.rdbuf(&buffer);
+    }
+
+    ~scoped_ostream_redirect() { costream.rdbuf(old); }
+
+    scoped_ostream_redirect(const scoped_ostream_redirect &) = delete;
+    scoped_ostream_redirect(scoped_ostream_redirect &&other) = default;
+    scoped_ostream_redirect &operator=(const scoped_ostream_redirect &) = delete;
+    scoped_ostream_redirect &operator=(scoped_ostream_redirect &&) = delete;
+};
+
+/** \rst
+    Like `scoped_ostream_redirect`, but redirects cerr by default. This class
+    is provided primary to make ``py::call_guard`` easier to make.
+
+    .. code-block:: cpp
+
+     m.def("noisy_func", &noisy_func,
+           py::call_guard<scoped_ostream_redirect,
+                          scoped_estream_redirect>());
+
+\endrst */
+class scoped_estream_redirect : public scoped_ostream_redirect {
+public:
+    explicit scoped_estream_redirect(std::ostream &costream = std::cerr,
+                                     const object &pyostream
+                                     = module_::import("sys").attr("stderr"))
+        : scoped_ostream_redirect(costream, pyostream) {}
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Class to redirect output as a context manager. C++ backend.
+class OstreamRedirect {
+    bool do_stdout_;
+    bool do_stderr_;
+    std::unique_ptr<scoped_ostream_redirect> redirect_stdout;
+    std::unique_ptr<scoped_estream_redirect> redirect_stderr;
+
+public:
+    explicit OstreamRedirect(bool do_stdout = true, bool do_stderr = true)
+        : do_stdout_(do_stdout), do_stderr_(do_stderr) {}
+
+    void enter() {
+        if (do_stdout_) {
+            redirect_stdout.reset(new scoped_ostream_redirect());
+        }
+        if (do_stderr_) {
+            redirect_stderr.reset(new scoped_estream_redirect());
+        }
+    }
+
+    void exit() {
+        redirect_stdout.reset();
+        redirect_stderr.reset();
+    }
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+    This is a helper function to add a C++ redirect context manager to Python
+    instead of using a C++ guard. To use it, add the following to your binding code:
+
+    .. code-block:: cpp
+
+        #include <pybind11/iostream.h>
+
+        ...
+
+        py::add_ostream_redirect(m, "ostream_redirect");
+
+    You now have a Python context manager that redirects your output:
+
+    .. code-block:: python
+
+        with m.ostream_redirect():
+            m.print_to_cout_function()
+
+    This manager can optionally be told which streams to operate on:
+
+    .. code-block:: python
+
+        with m.ostream_redirect(stdout=true, stderr=true):
+            m.noisy_function_with_error_printing()
+
+ \endrst */
+inline class_<detail::OstreamRedirect>
+add_ostream_redirect(module_ m, const std::string &name = "ostream_redirect") {
+    return class_<detail::OstreamRedirect>(std::move(m), name.c_str(), module_local())
+        .def(init<bool, bool>(), arg("stdout") = true, arg("stderr") = true)
+        .def("__enter__", &detail::OstreamRedirect::enter)
+        .def("__exit__", [](detail::OstreamRedirect &self_, const args &) { self_.exit(); });
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/numpy.h b/lib/python3.11/site-packages/torch/include/pybind11/numpy.h
new file mode 100644
index 0000000000000000000000000000000000000000..09894cf74f4aef2b91f657f84ebfb6af3e7e8d0f
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/numpy.h
@@ -0,0 +1,2139 @@
+/*
+    pybind11/numpy.h: Basic NumPy support, vectorize() wrapper
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "detail/common.h"
+#include "complex.h"
+#include "gil_safe_call_once.h"
+#include "pytypes.h"
+
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <numeric>
+#include <sstream>
+#include <string>
+#include <type_traits>
+#include <typeindex>
+#include <utility>
+#include <vector>
+
+#if defined(PYBIND11_NUMPY_1_ONLY) && !defined(PYBIND11_INTERNAL_NUMPY_1_ONLY_DETECTED)
+#    error PYBIND11_NUMPY_1_ONLY must be defined before any pybind11 header is included.
+#endif
+
+/* This will be true on all flat address space platforms and allows us to reduce the
+   whole npy_intp / ssize_t / Py_intptr_t business down to just ssize_t for all size
+   and dimension types (e.g. shape, strides, indexing), instead of inflicting this
+   upon the library user.
+   Note that NumPy 2 now uses ssize_t for `npy_intp` to simplify this. */
+static_assert(sizeof(::pybind11::ssize_t) == sizeof(Py_intptr_t), "ssize_t != Py_intptr_t");
+static_assert(std::is_signed<Py_intptr_t>::value, "Py_intptr_t must be signed");
+// We now can reinterpret_cast between py::ssize_t and Py_intptr_t (MSVC + PyPy cares)
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+class dtype; // Forward declaration
+class array; // Forward declaration
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+struct handle_type_name<dtype> {
+    static constexpr auto name = const_name("numpy.dtype");
+};
+
+template <>
+struct handle_type_name<array> {
+    static constexpr auto name = const_name("numpy.ndarray");
+};
+
+template <typename type, typename SFINAE = void>
+struct npy_format_descriptor;
+
+/* NumPy 1 proxy (always includes legacy fields) */
+struct PyArrayDescr1_Proxy {
+    PyObject_HEAD
+    PyObject *typeobj;
+    char kind;
+    char type;
+    char byteorder;
+    char flags;
+    int type_num;
+    int elsize;
+    int alignment;
+    char *subarray;
+    PyObject *fields;
+    PyObject *names;
+};
+
+#ifndef PYBIND11_NUMPY_1_ONLY
+struct PyArrayDescr_Proxy {
+    PyObject_HEAD
+    PyObject *typeobj;
+    char kind;
+    char type;
+    char byteorder;
+    char _former_flags;
+    int type_num;
+    /* Additional fields are NumPy version specific. */
+};
+#else
+/* NumPy 1.x only, we can expose all fields */
+using PyArrayDescr_Proxy = PyArrayDescr1_Proxy;
+#endif
+
+/* NumPy 2 proxy, including legacy fields */
+struct PyArrayDescr2_Proxy {
+    PyObject_HEAD
+    PyObject *typeobj;
+    char kind;
+    char type;
+    char byteorder;
+    char _former_flags;
+    int type_num;
+    std::uint64_t flags;
+    ssize_t elsize;
+    ssize_t alignment;
+    PyObject *metadata;
+    Py_hash_t hash;
+    void *reserved_null[2];
+    /* The following fields only exist if 0 <= type_num < 2056 */
+    char *subarray;
+    PyObject *fields;
+    PyObject *names;
+};
+
+struct PyArray_Proxy {
+    PyObject_HEAD
+    char *data;
+    int nd;
+    ssize_t *dimensions;
+    ssize_t *strides;
+    PyObject *base;
+    PyObject *descr;
+    int flags;
+};
+
+struct PyVoidScalarObject_Proxy {
+    PyObject_VAR_HEAD char *obval;
+    PyArrayDescr_Proxy *descr;
+    int flags;
+    PyObject *base;
+};
+
+struct numpy_type_info {
+    PyObject *dtype_ptr;
+    std::string format_str;
+};
+
+struct numpy_internals {
+    std::unordered_map<std::type_index, numpy_type_info> registered_dtypes;
+
+    numpy_type_info *get_type_info(const std::type_info &tinfo, bool throw_if_missing = true) {
+        auto it = registered_dtypes.find(std::type_index(tinfo));
+        if (it != registered_dtypes.end()) {
+            return &(it->second);
+        }
+        if (throw_if_missing) {
+            pybind11_fail(std::string("NumPy type info missing for ") + tinfo.name());
+        }
+        return nullptr;
+    }
+
+    template <typename T>
+    numpy_type_info *get_type_info(bool throw_if_missing = true) {
+        return get_type_info(typeid(typename std::remove_cv<T>::type), throw_if_missing);
+    }
+};
+
+PYBIND11_NOINLINE void load_numpy_internals(numpy_internals *&ptr) {
+    ptr = &get_or_create_shared_data<numpy_internals>("_numpy_internals");
+}
+
+inline numpy_internals &get_numpy_internals() {
+    static numpy_internals *ptr = nullptr;
+    if (!ptr) {
+        load_numpy_internals(ptr);
+    }
+    return *ptr;
+}
+
+PYBIND11_NOINLINE module_ import_numpy_core_submodule(const char *submodule_name) {
+    module_ numpy = module_::import("numpy");
+    str version_string = numpy.attr("__version__");
+
+    module_ numpy_lib = module_::import("numpy.lib");
+    object numpy_version = numpy_lib.attr("NumpyVersion")(version_string);
+    int major_version = numpy_version.attr("major").cast<int>();
+
+#ifdef PYBIND11_NUMPY_1_ONLY
+    if (major_version >= 2) {
+        throw std::runtime_error(
+            "This extension was built with PYBIND11_NUMPY_1_ONLY defined, "
+            "but NumPy 2 is used in this process. For NumPy2 compatibility, "
+            "this extension needs to be rebuilt without the PYBIND11_NUMPY_1_ONLY define.");
+    }
+#endif
+    /* `numpy.core` was renamed to `numpy._core` in NumPy 2.0 as it officially
+        became a private module. */
+    std::string numpy_core_path = major_version >= 2 ? "numpy._core" : "numpy.core";
+    return module_::import((numpy_core_path + "." + submodule_name).c_str());
+}
+
+template <typename T>
+struct same_size {
+    template <typename U>
+    using as = bool_constant<sizeof(T) == sizeof(U)>;
+};
+
+template <typename Concrete>
+constexpr int platform_lookup() {
+    return -1;
+}
+
+// Lookup a type according to its size, and return a value corresponding to the NumPy typenum.
+template <typename Concrete, typename T, typename... Ts, typename... Ints>
+constexpr int platform_lookup(int I, Ints... Is) {
+    return sizeof(Concrete) == sizeof(T) ? I : platform_lookup<Concrete, Ts...>(Is...);
+}
+
+struct npy_api {
+    enum constants {
+        NPY_ARRAY_C_CONTIGUOUS_ = 0x0001,
+        NPY_ARRAY_F_CONTIGUOUS_ = 0x0002,
+        NPY_ARRAY_OWNDATA_ = 0x0004,
+        NPY_ARRAY_FORCECAST_ = 0x0010,
+        NPY_ARRAY_ENSUREARRAY_ = 0x0040,
+        NPY_ARRAY_ALIGNED_ = 0x0100,
+        NPY_ARRAY_WRITEABLE_ = 0x0400,
+        NPY_BOOL_ = 0,
+        NPY_BYTE_,
+        NPY_UBYTE_,
+        NPY_SHORT_,
+        NPY_USHORT_,
+        NPY_INT_,
+        NPY_UINT_,
+        NPY_LONG_,
+        NPY_ULONG_,
+        NPY_LONGLONG_,
+        NPY_ULONGLONG_,
+        NPY_FLOAT_,
+        NPY_DOUBLE_,
+        NPY_LONGDOUBLE_,
+        NPY_CFLOAT_,
+        NPY_CDOUBLE_,
+        NPY_CLONGDOUBLE_,
+        NPY_OBJECT_ = 17,
+        NPY_STRING_,
+        NPY_UNICODE_,
+        NPY_VOID_,
+        // Platform-dependent normalization
+        NPY_INT8_ = NPY_BYTE_,
+        NPY_UINT8_ = NPY_UBYTE_,
+        NPY_INT16_ = NPY_SHORT_,
+        NPY_UINT16_ = NPY_USHORT_,
+        // `npy_common.h` defines the integer aliases. In order, it checks:
+        // NPY_BITSOF_LONG, NPY_BITSOF_LONGLONG, NPY_BITSOF_INT, NPY_BITSOF_SHORT, NPY_BITSOF_CHAR
+        // and assigns the alias to the first matching size, so we should check in this order.
+        NPY_INT32_
+        = platform_lookup<std::int32_t, long, int, short>(NPY_LONG_, NPY_INT_, NPY_SHORT_),
+        NPY_UINT32_ = platform_lookup<std::uint32_t, unsigned long, unsigned int, unsigned short>(
+            NPY_ULONG_, NPY_UINT_, NPY_USHORT_),
+        NPY_INT64_
+        = platform_lookup<std::int64_t, long, long long, int>(NPY_LONG_, NPY_LONGLONG_, NPY_INT_),
+        NPY_UINT64_
+        = platform_lookup<std::uint64_t, unsigned long, unsigned long long, unsigned int>(
+            NPY_ULONG_, NPY_ULONGLONG_, NPY_UINT_),
+    };
+
+    unsigned int PyArray_RUNTIME_VERSION_;
+
+    struct PyArray_Dims {
+        Py_intptr_t *ptr;
+        int len;
+    };
+
+    static npy_api &get() {
+        PYBIND11_CONSTINIT static gil_safe_call_once_and_store<npy_api> storage;
+        return storage.call_once_and_store_result(lookup).get_stored();
+    }
+
+    bool PyArray_Check_(PyObject *obj) const {
+        return PyObject_TypeCheck(obj, PyArray_Type_) != 0;
+    }
+    bool PyArrayDescr_Check_(PyObject *obj) const {
+        return PyObject_TypeCheck(obj, PyArrayDescr_Type_) != 0;
+    }
+
+    unsigned int (*PyArray_GetNDArrayCFeatureVersion_)();
+    PyObject *(*PyArray_DescrFromType_)(int);
+    PyObject *(*PyArray_NewFromDescr_)(PyTypeObject *,
+                                       PyObject *,
+                                       int,
+                                       Py_intptr_t const *,
+                                       Py_intptr_t const *,
+                                       void *,
+                                       int,
+                                       PyObject *);
+    // Unused. Not removed because that affects ABI of the class.
+    PyObject *(*PyArray_DescrNewFromType_)(int);
+    int (*PyArray_CopyInto_)(PyObject *, PyObject *);
+    PyObject *(*PyArray_NewCopy_)(PyObject *, int);
+    PyTypeObject *PyArray_Type_;
+    PyTypeObject *PyVoidArrType_Type_;
+    PyTypeObject *PyArrayDescr_Type_;
+    PyObject *(*PyArray_DescrFromScalar_)(PyObject *);
+    PyObject *(*PyArray_FromAny_)(PyObject *, PyObject *, int, int, int, PyObject *);
+    int (*PyArray_DescrConverter_)(PyObject *, PyObject **);
+    bool (*PyArray_EquivTypes_)(PyObject *, PyObject *);
+#ifdef PYBIND11_NUMPY_1_ONLY
+    int (*PyArray_GetArrayParamsFromObject_)(PyObject *,
+                                             PyObject *,
+                                             unsigned char,
+                                             PyObject **,
+                                             int *,
+                                             Py_intptr_t *,
+                                             PyObject **,
+                                             PyObject *);
+#endif
+    PyObject *(*PyArray_Squeeze_)(PyObject *);
+    // Unused. Not removed because that affects ABI of the class.
+    int (*PyArray_SetBaseObject_)(PyObject *, PyObject *);
+    PyObject *(*PyArray_Resize_)(PyObject *, PyArray_Dims *, int, int);
+    PyObject *(*PyArray_Newshape_)(PyObject *, PyArray_Dims *, int);
+    PyObject *(*PyArray_View_)(PyObject *, PyObject *, PyObject *);
+
+private:
+    enum functions {
+        API_PyArray_GetNDArrayCFeatureVersion = 211,
+        API_PyArray_Type = 2,
+        API_PyArrayDescr_Type = 3,
+        API_PyVoidArrType_Type = 39,
+        API_PyArray_DescrFromType = 45,
+        API_PyArray_DescrFromScalar = 57,
+        API_PyArray_FromAny = 69,
+        API_PyArray_Resize = 80,
+        // CopyInto was slot 82 and 50 was effectively an alias. NumPy 2 removed 82.
+        API_PyArray_CopyInto = 50,
+        API_PyArray_NewCopy = 85,
+        API_PyArray_NewFromDescr = 94,
+        API_PyArray_DescrNewFromType = 96,
+        API_PyArray_Newshape = 135,
+        API_PyArray_Squeeze = 136,
+        API_PyArray_View = 137,
+        API_PyArray_DescrConverter = 174,
+        API_PyArray_EquivTypes = 182,
+#ifdef PYBIND11_NUMPY_1_ONLY
+        API_PyArray_GetArrayParamsFromObject = 278,
+#endif
+        API_PyArray_SetBaseObject = 282
+    };
+
+    static npy_api lookup() {
+        module_ m = detail::import_numpy_core_submodule("multiarray");
+        auto c = m.attr("_ARRAY_API");
+        void **api_ptr = (void **) PyCapsule_GetPointer(c.ptr(), nullptr);
+        if (api_ptr == nullptr) {
+            raise_from(PyExc_SystemError, "FAILURE obtaining numpy _ARRAY_API pointer.");
+            throw error_already_set();
+        }
+        npy_api api;
+#define DECL_NPY_API(Func) api.Func##_ = (decltype(api.Func##_)) api_ptr[API_##Func];
+        DECL_NPY_API(PyArray_GetNDArrayCFeatureVersion);
+        api.PyArray_RUNTIME_VERSION_ = api.PyArray_GetNDArrayCFeatureVersion_();
+        if (api.PyArray_RUNTIME_VERSION_ < 0x7) {
+            pybind11_fail("pybind11 numpy support requires numpy >= 1.7.0");
+        }
+        DECL_NPY_API(PyArray_Type);
+        DECL_NPY_API(PyVoidArrType_Type);
+        DECL_NPY_API(PyArrayDescr_Type);
+        DECL_NPY_API(PyArray_DescrFromType);
+        DECL_NPY_API(PyArray_DescrFromScalar);
+        DECL_NPY_API(PyArray_FromAny);
+        DECL_NPY_API(PyArray_Resize);
+        DECL_NPY_API(PyArray_CopyInto);
+        DECL_NPY_API(PyArray_NewCopy);
+        DECL_NPY_API(PyArray_NewFromDescr);
+        DECL_NPY_API(PyArray_DescrNewFromType);
+        DECL_NPY_API(PyArray_Newshape);
+        DECL_NPY_API(PyArray_Squeeze);
+        DECL_NPY_API(PyArray_View);
+        DECL_NPY_API(PyArray_DescrConverter);
+        DECL_NPY_API(PyArray_EquivTypes);
+#ifdef PYBIND11_NUMPY_1_ONLY
+        DECL_NPY_API(PyArray_GetArrayParamsFromObject);
+#endif
+        DECL_NPY_API(PyArray_SetBaseObject);
+
+#undef DECL_NPY_API
+        return api;
+    }
+};
+
+inline PyArray_Proxy *array_proxy(void *ptr) { return reinterpret_cast<PyArray_Proxy *>(ptr); }
+
+inline const PyArray_Proxy *array_proxy(const void *ptr) {
+    return reinterpret_cast<const PyArray_Proxy *>(ptr);
+}
+
+inline PyArrayDescr_Proxy *array_descriptor_proxy(PyObject *ptr) {
+    return reinterpret_cast<PyArrayDescr_Proxy *>(ptr);
+}
+
+inline const PyArrayDescr_Proxy *array_descriptor_proxy(const PyObject *ptr) {
+    return reinterpret_cast<const PyArrayDescr_Proxy *>(ptr);
+}
+
+inline const PyArrayDescr1_Proxy *array_descriptor1_proxy(const PyObject *ptr) {
+    return reinterpret_cast<const PyArrayDescr1_Proxy *>(ptr);
+}
+
+inline const PyArrayDescr2_Proxy *array_descriptor2_proxy(const PyObject *ptr) {
+    return reinterpret_cast<const PyArrayDescr2_Proxy *>(ptr);
+}
+
+inline bool check_flags(const void *ptr, int flag) {
+    return (flag == (array_proxy(ptr)->flags & flag));
+}
+
+template <typename T>
+struct is_std_array : std::false_type {};
+template <typename T, size_t N>
+struct is_std_array<std::array<T, N>> : std::true_type {};
+template <typename T>
+struct is_complex : std::false_type {};
+template <typename T>
+struct is_complex<std::complex<T>> : std::true_type {};
+
+template <typename T>
+struct array_info_scalar {
+    using type = T;
+    static constexpr bool is_array = false;
+    static constexpr bool is_empty = false;
+    static constexpr auto extents = const_name("");
+    static void append_extents(list & /* shape */) {}
+};
+// Computes underlying type and a comma-separated list of extents for array
+// types (any mix of std::array and built-in arrays). An array of char is
+// treated as scalar because it gets special handling.
+template <typename T>
+struct array_info : array_info_scalar<T> {};
+template <typename T, size_t N>
+struct array_info<std::array<T, N>> {
+    using type = typename array_info<T>::type;
+    static constexpr bool is_array = true;
+    static constexpr bool is_empty = (N == 0) || array_info<T>::is_empty;
+    static constexpr size_t extent = N;
+
+    // appends the extents to shape
+    static void append_extents(list &shape) {
+        shape.append(N);
+        array_info<T>::append_extents(shape);
+    }
+
+    static constexpr auto extents = const_name<array_info<T>::is_array>(
+        ::pybind11::detail::concat(const_name<N>(), array_info<T>::extents), const_name<N>());
+};
+// For numpy we have special handling for arrays of characters, so we don't include
+// the size in the array extents.
+template <size_t N>
+struct array_info<char[N]> : array_info_scalar<char[N]> {};
+template <size_t N>
+struct array_info<std::array<char, N>> : array_info_scalar<std::array<char, N>> {};
+template <typename T, size_t N>
+struct array_info<T[N]> : array_info<std::array<T, N>> {};
+template <typename T>
+using remove_all_extents_t = typename array_info<T>::type;
+
+template <typename T>
+using is_pod_struct
+    = all_of<std::is_standard_layout<T>, // since we're accessing directly in memory
+                                         // we need a standard layout type
+#if defined(__GLIBCXX__)                                                                          \
+    && (__GLIBCXX__ < 20150422 || __GLIBCXX__ == 20150426 || __GLIBCXX__ == 20150623              \
+        || __GLIBCXX__ == 20150626 || __GLIBCXX__ == 20160803)
+             // libstdc++ < 5 (including versions 4.8.5, 4.9.3 and 4.9.4 which were released after
+             // 5) don't implement is_trivially_copyable, so approximate it
+             std::is_trivially_destructible<T>,
+             satisfies_any_of<T, std::has_trivial_copy_constructor, std::has_trivial_copy_assign>,
+#else
+             std::is_trivially_copyable<T>,
+#endif
+             satisfies_none_of<T,
+                               std::is_reference,
+                               std::is_array,
+                               is_std_array,
+                               std::is_arithmetic,
+                               is_complex,
+                               std::is_enum>>;
+
+// Replacement for std::is_pod (deprecated in C++20)
+template <typename T>
+using is_pod = all_of<std::is_standard_layout<T>, std::is_trivial<T>>;
+
+template <ssize_t Dim = 0, typename Strides>
+ssize_t byte_offset_unsafe(const Strides &) {
+    return 0;
+}
+template <ssize_t Dim = 0, typename Strides, typename... Ix>
+ssize_t byte_offset_unsafe(const Strides &strides, ssize_t i, Ix... index) {
+    return i * strides[Dim] + byte_offset_unsafe<Dim + 1>(strides, index...);
+}
+
+/**
+ * Proxy class providing unsafe, unchecked const access to array data.  This is constructed through
+ * the `unchecked<T, N>()` method of `array` or the `unchecked<N>()` method of `array_t<T>`. `Dims`
+ * will be -1 for dimensions determined at runtime.
+ */
+template <typename T, ssize_t Dims>
+class unchecked_reference {
+protected:
+    static constexpr bool Dynamic = Dims < 0;
+    const unsigned char *data_;
+    // Storing the shape & strides in local variables (i.e. these arrays) allows the compiler to
+    // make large performance gains on big, nested loops, but requires compile-time dimensions
+    conditional_t<Dynamic, const ssize_t *, std::array<ssize_t, (size_t) Dims>> shape_, strides_;
+    const ssize_t dims_;
+
+    friend class pybind11::array;
+    // Constructor for compile-time dimensions:
+    template <bool Dyn = Dynamic>
+    unchecked_reference(const void *data,
+                        const ssize_t *shape,
+                        const ssize_t *strides,
+                        enable_if_t<!Dyn, ssize_t>)
+        : data_{reinterpret_cast<const unsigned char *>(data)}, dims_{Dims} {
+        for (size_t i = 0; i < (size_t) dims_; i++) {
+            shape_[i] = shape[i];
+            strides_[i] = strides[i];
+        }
+    }
+    // Constructor for runtime dimensions:
+    template <bool Dyn = Dynamic>
+    unchecked_reference(const void *data,
+                        const ssize_t *shape,
+                        const ssize_t *strides,
+                        enable_if_t<Dyn, ssize_t> dims)
+        : data_{reinterpret_cast<const unsigned char *>(data)}, shape_{shape}, strides_{strides},
+          dims_{dims} {}
+
+public:
+    /**
+     * Unchecked const reference access to data at the given indices.  For a compile-time known
+     * number of dimensions, this requires the correct number of arguments; for run-time
+     * dimensionality, this is not checked (and so is up to the caller to use safely).
+     */
+    template <typename... Ix>
+    const T &operator()(Ix... index) const {
+        static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
+                      "Invalid number of indices for unchecked array reference");
+        return *reinterpret_cast<const T *>(data_
+                                            + byte_offset_unsafe(strides_, ssize_t(index)...));
+    }
+    /**
+     * Unchecked const reference access to data; this operator only participates if the reference
+     * is to a 1-dimensional array.  When present, this is exactly equivalent to `obj(index)`.
+     */
+    template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
+    const T &operator[](ssize_t index) const {
+        return operator()(index);
+    }
+
+    /// Pointer access to the data at the given indices.
+    template <typename... Ix>
+    const T *data(Ix... ix) const {
+        return &operator()(ssize_t(ix)...);
+    }
+
+    /// Returns the item size, i.e. sizeof(T)
+    constexpr static ssize_t itemsize() { return sizeof(T); }
+
+    /// Returns the shape (i.e. size) of dimension `dim`
+    ssize_t shape(ssize_t dim) const { return shape_[(size_t) dim]; }
+
+    /// Returns the number of dimensions of the array
+    ssize_t ndim() const { return dims_; }
+
+    /// Returns the total number of elements in the referenced array, i.e. the product of the
+    /// shapes
+    template <bool Dyn = Dynamic>
+    enable_if_t<!Dyn, ssize_t> size() const {
+        return std::accumulate(
+            shape_.begin(), shape_.end(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+    template <bool Dyn = Dynamic>
+    enable_if_t<Dyn, ssize_t> size() const {
+        return std::accumulate(shape_, shape_ + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+
+    /// Returns the total number of bytes used by the referenced data.  Note that the actual span
+    /// in memory may be larger if the referenced array has non-contiguous strides (e.g. for a
+    /// slice).
+    ssize_t nbytes() const { return size() * itemsize(); }
+};
+
+template <typename T, ssize_t Dims>
+class unchecked_mutable_reference : public unchecked_reference<T, Dims> {
+    friend class pybind11::array;
+    using ConstBase = unchecked_reference<T, Dims>;
+    using ConstBase::ConstBase;
+    using ConstBase::Dynamic;
+
+public:
+    // Bring in const-qualified versions from base class
+    using ConstBase::operator();
+    using ConstBase::operator[];
+
+    /// Mutable, unchecked access to data at the given indices.
+    template <typename... Ix>
+    T &operator()(Ix... index) {
+        static_assert(ssize_t{sizeof...(Ix)} == Dims || Dynamic,
+                      "Invalid number of indices for unchecked array reference");
+        return const_cast<T &>(ConstBase::operator()(index...));
+    }
+    /**
+     * Mutable, unchecked access data at the given index; this operator only participates if the
+     * reference is to a 1-dimensional array (or has runtime dimensions).  When present, this is
+     * exactly equivalent to `obj(index)`.
+     */
+    template <ssize_t D = Dims, typename = enable_if_t<D == 1 || Dynamic>>
+    T &operator[](ssize_t index) {
+        return operator()(index);
+    }
+
+    /// Mutable pointer access to the data at the given indices.
+    template <typename... Ix>
+    T *mutable_data(Ix... ix) {
+        return &operator()(ssize_t(ix)...);
+    }
+};
+
+template <typename T, ssize_t Dim>
+struct type_caster<unchecked_reference<T, Dim>> {
+    static_assert(Dim == 0 && Dim > 0 /* always fail */,
+                  "unchecked array proxy object is not castable");
+};
+template <typename T, ssize_t Dim>
+struct type_caster<unchecked_mutable_reference<T, Dim>>
+    : type_caster<unchecked_reference<T, Dim>> {};
+
+PYBIND11_NAMESPACE_END(detail)
+
+class dtype : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(dtype, object, detail::npy_api::get().PyArrayDescr_Check_)
+
+    explicit dtype(const buffer_info &info) {
+        dtype descr(_dtype_from_pep3118()(pybind11::str(info.format)));
+        // If info.itemsize == 0, use the value calculated from the format string
+        m_ptr = descr.strip_padding(info.itemsize != 0 ? info.itemsize : descr.itemsize())
+                    .release()
+                    .ptr();
+    }
+
+    explicit dtype(const pybind11::str &format) : dtype(from_args(format)) {}
+
+    explicit dtype(const std::string &format) : dtype(pybind11::str(format)) {}
+
+    explicit dtype(const char *format) : dtype(pybind11::str(format)) {}
+
+    dtype(list names, list formats, list offsets, ssize_t itemsize) {
+        dict args;
+        args["names"] = std::move(names);
+        args["formats"] = std::move(formats);
+        args["offsets"] = std::move(offsets);
+        args["itemsize"] = pybind11::int_(itemsize);
+        m_ptr = from_args(args).release().ptr();
+    }
+
+    /// Return dtype for the given typenum (one of the NPY_TYPES).
+    /// https://numpy.org/devdocs/reference/c-api/array.html#c.PyArray_DescrFromType
+    explicit dtype(int typenum)
+        : object(detail::npy_api::get().PyArray_DescrFromType_(typenum), stolen_t{}) {
+        if (m_ptr == nullptr) {
+            throw error_already_set();
+        }
+    }
+
+    /// This is essentially the same as calling numpy.dtype(args) in Python.
+    static dtype from_args(const object &args) {
+        PyObject *ptr = nullptr;
+        if ((detail::npy_api::get().PyArray_DescrConverter_(args.ptr(), &ptr) == 0) || !ptr) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<dtype>(ptr);
+    }
+
+    /// Return dtype associated with a C++ type.
+    template <typename T>
+    static dtype of() {
+        return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::dtype();
+    }
+
+    /// Size of the data type in bytes.
+#ifdef PYBIND11_NUMPY_1_ONLY
+    ssize_t itemsize() const { return detail::array_descriptor_proxy(m_ptr)->elsize; }
+#else
+    ssize_t itemsize() const {
+        if (detail::npy_api::get().PyArray_RUNTIME_VERSION_ < 0x12) {
+            return detail::array_descriptor1_proxy(m_ptr)->elsize;
+        }
+        return detail::array_descriptor2_proxy(m_ptr)->elsize;
+    }
+#endif
+
+    /// Returns true for structured data types.
+#ifdef PYBIND11_NUMPY_1_ONLY
+    bool has_fields() const { return detail::array_descriptor_proxy(m_ptr)->names != nullptr; }
+#else
+    bool has_fields() const {
+        if (detail::npy_api::get().PyArray_RUNTIME_VERSION_ < 0x12) {
+            return detail::array_descriptor1_proxy(m_ptr)->names != nullptr;
+        }
+        const auto *proxy = detail::array_descriptor2_proxy(m_ptr);
+        if (proxy->type_num < 0 || proxy->type_num >= 2056) {
+            return false;
+        }
+        return proxy->names != nullptr;
+    }
+#endif
+
+    /// Single-character code for dtype's kind.
+    /// For example, floating point types are 'f' and integral types are 'i'.
+    char kind() const { return detail::array_descriptor_proxy(m_ptr)->kind; }
+
+    /// Single-character for dtype's type.
+    /// For example, ``float`` is 'f', ``double`` 'd', ``int`` 'i', and ``long`` 'l'.
+    char char_() const {
+        // Note: The signature, `dtype::char_` follows the naming of NumPy's
+        // public Python API (i.e., ``dtype.char``), rather than its internal
+        // C API (``PyArray_Descr::type``).
+        return detail::array_descriptor_proxy(m_ptr)->type;
+    }
+
+    /// type number of dtype.
+    int num() const {
+        // Note: The signature, `dtype::num` follows the naming of NumPy's public
+        // Python API (i.e., ``dtype.num``), rather than its internal
+        // C API (``PyArray_Descr::type_num``).
+        return detail::array_descriptor_proxy(m_ptr)->type_num;
+    }
+
+    /// Single character for byteorder
+    char byteorder() const { return detail::array_descriptor_proxy(m_ptr)->byteorder; }
+
+/// Alignment of the data type
+#ifdef PYBIND11_NUMPY_1_ONLY
+    int alignment() const { return detail::array_descriptor_proxy(m_ptr)->alignment; }
+#else
+    ssize_t alignment() const {
+        if (detail::npy_api::get().PyArray_RUNTIME_VERSION_ < 0x12) {
+            return detail::array_descriptor1_proxy(m_ptr)->alignment;
+        }
+        return detail::array_descriptor2_proxy(m_ptr)->alignment;
+    }
+#endif
+
+/// Flags for the array descriptor
+#ifdef PYBIND11_NUMPY_1_ONLY
+    char flags() const { return detail::array_descriptor_proxy(m_ptr)->flags; }
+#else
+    std::uint64_t flags() const {
+        if (detail::npy_api::get().PyArray_RUNTIME_VERSION_ < 0x12) {
+            return (unsigned char) detail::array_descriptor1_proxy(m_ptr)->flags;
+        }
+        return detail::array_descriptor2_proxy(m_ptr)->flags;
+    }
+#endif
+
+private:
+    static object &_dtype_from_pep3118() {
+        PYBIND11_CONSTINIT static gil_safe_call_once_and_store<object> storage;
+        return storage
+            .call_once_and_store_result([]() {
+                return detail::import_numpy_core_submodule("_internal")
+                    .attr("_dtype_from_pep3118");
+            })
+            .get_stored();
+    }
+
+    dtype strip_padding(ssize_t itemsize) {
+        // Recursively strip all void fields with empty names that are generated for
+        // padding fields (as of NumPy v1.11).
+        if (!has_fields()) {
+            return *this;
+        }
+
+        struct field_descr {
+            pybind11::str name;
+            object format;
+            pybind11::int_ offset;
+            field_descr(pybind11::str &&name, object &&format, pybind11::int_ &&offset)
+                : name{std::move(name)}, format{std::move(format)}, offset{std::move(offset)} {};
+        };
+        auto field_dict = attr("fields").cast<dict>();
+        std::vector<field_descr> field_descriptors;
+        field_descriptors.reserve(field_dict.size());
+
+        for (auto field : field_dict.attr("items")()) {
+            auto spec = field.cast<tuple>();
+            auto name = spec[0].cast<pybind11::str>();
+            auto spec_fo = spec[1].cast<tuple>();
+            auto format = spec_fo[0].cast<dtype>();
+            auto offset = spec_fo[1].cast<pybind11::int_>();
+            if ((len(name) == 0u) && format.kind() == 'V') {
+                continue;
+            }
+            field_descriptors.emplace_back(
+                std::move(name), format.strip_padding(format.itemsize()), std::move(offset));
+        }
+
+        std::sort(field_descriptors.begin(),
+                  field_descriptors.end(),
+                  [](const field_descr &a, const field_descr &b) {
+                      return a.offset.cast<int>() < b.offset.cast<int>();
+                  });
+
+        list names, formats, offsets;
+        for (auto &descr : field_descriptors) {
+            names.append(std::move(descr.name));
+            formats.append(std::move(descr.format));
+            offsets.append(std::move(descr.offset));
+        }
+        return dtype(std::move(names), std::move(formats), std::move(offsets), itemsize);
+    }
+};
+
+class array : public buffer {
+public:
+    PYBIND11_OBJECT_CVT(array, buffer, detail::npy_api::get().PyArray_Check_, raw_array)
+
+    enum {
+        c_style = detail::npy_api::NPY_ARRAY_C_CONTIGUOUS_,
+        f_style = detail::npy_api::NPY_ARRAY_F_CONTIGUOUS_,
+        forcecast = detail::npy_api::NPY_ARRAY_FORCECAST_
+    };
+
+    array() : array(0, static_cast<const double *>(nullptr)) {}
+
+    using ShapeContainer = detail::any_container<ssize_t>;
+    using StridesContainer = detail::any_container<ssize_t>;
+
+    // Constructs an array taking shape/strides from arbitrary container types
+    array(const pybind11::dtype &dt,
+          ShapeContainer shape,
+          StridesContainer strides,
+          const void *ptr = nullptr,
+          handle base = handle()) {
+
+        if (strides->empty()) {
+            *strides = detail::c_strides(*shape, dt.itemsize());
+        }
+
+        auto ndim = shape->size();
+        if (ndim != strides->size()) {
+            pybind11_fail("NumPy: shape ndim doesn't match strides ndim");
+        }
+        auto descr = dt;
+
+        int flags = 0;
+        if (base && ptr) {
+            if (isinstance<array>(base)) {
+                /* Copy flags from base (except ownership bit) */
+                flags = reinterpret_borrow<array>(base).flags()
+                        & ~detail::npy_api::NPY_ARRAY_OWNDATA_;
+            } else {
+                /* Writable by default, easy to downgrade later on if needed */
+                flags = detail::npy_api::NPY_ARRAY_WRITEABLE_;
+            }
+        }
+
+        auto &api = detail::npy_api::get();
+        auto tmp = reinterpret_steal<object>(api.PyArray_NewFromDescr_(
+            api.PyArray_Type_,
+            descr.release().ptr(),
+            (int) ndim,
+            // Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
+            reinterpret_cast<Py_intptr_t *>(shape->data()),
+            reinterpret_cast<Py_intptr_t *>(strides->data()),
+            const_cast<void *>(ptr),
+            flags,
+            nullptr));
+        if (!tmp) {
+            throw error_already_set();
+        }
+        if (ptr) {
+            if (base) {
+                api.PyArray_SetBaseObject_(tmp.ptr(), base.inc_ref().ptr());
+            } else {
+                tmp = reinterpret_steal<object>(
+                    api.PyArray_NewCopy_(tmp.ptr(), -1 /* any order */));
+            }
+        }
+        m_ptr = tmp.release().ptr();
+    }
+
+    array(const pybind11::dtype &dt,
+          ShapeContainer shape,
+          const void *ptr = nullptr,
+          handle base = handle())
+        : array(dt, std::move(shape), {}, ptr, base) {}
+
+    template <typename T,
+              typename
+              = detail::enable_if_t<std::is_integral<T>::value && !std::is_same<bool, T>::value>>
+    array(const pybind11::dtype &dt, T count, const void *ptr = nullptr, handle base = handle())
+        : array(dt, {{count}}, ptr, base) {}
+
+    template <typename T>
+    array(ShapeContainer shape, StridesContainer strides, const T *ptr, handle base = handle())
+        : array(pybind11::dtype::of<T>(),
+                std::move(shape),
+                std::move(strides),
+                reinterpret_cast<const void *>(ptr),
+                base) {}
+
+    template <typename T>
+    array(ShapeContainer shape, const T *ptr, handle base = handle())
+        : array(std::move(shape), {}, ptr, base) {}
+
+    template <typename T>
+    explicit array(ssize_t count, const T *ptr, handle base = handle())
+        : array({count}, {}, ptr, base) {}
+
+    explicit array(const buffer_info &info, handle base = handle())
+        : array(pybind11::dtype(info), info.shape, info.strides, info.ptr, base) {}
+
+    /// Array descriptor (dtype)
+    pybind11::dtype dtype() const {
+        return reinterpret_borrow<pybind11::dtype>(detail::array_proxy(m_ptr)->descr);
+    }
+
+    /// Total number of elements
+    ssize_t size() const {
+        return std::accumulate(shape(), shape() + ndim(), (ssize_t) 1, std::multiplies<ssize_t>());
+    }
+
+    /// Byte size of a single element
+    ssize_t itemsize() const { return dtype().itemsize(); }
+
+    /// Total number of bytes
+    ssize_t nbytes() const { return size() * itemsize(); }
+
+    /// Number of dimensions
+    ssize_t ndim() const { return detail::array_proxy(m_ptr)->nd; }
+
+    /// Base object
+    object base() const { return reinterpret_borrow<object>(detail::array_proxy(m_ptr)->base); }
+
+    /// Dimensions of the array
+    const ssize_t *shape() const { return detail::array_proxy(m_ptr)->dimensions; }
+
+    /// Dimension along a given axis
+    ssize_t shape(ssize_t dim) const {
+        if (dim >= ndim()) {
+            fail_dim_check(dim, "invalid axis");
+        }
+        return shape()[dim];
+    }
+
+    /// Strides of the array
+    const ssize_t *strides() const { return detail::array_proxy(m_ptr)->strides; }
+
+    /// Stride along a given axis
+    ssize_t strides(ssize_t dim) const {
+        if (dim >= ndim()) {
+            fail_dim_check(dim, "invalid axis");
+        }
+        return strides()[dim];
+    }
+
+    /// Return the NumPy array flags
+    int flags() const { return detail::array_proxy(m_ptr)->flags; }
+
+    /// If set, the array is writeable (otherwise the buffer is read-only)
+    bool writeable() const {
+        return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_WRITEABLE_);
+    }
+
+    /// If set, the array owns the data (will be freed when the array is deleted)
+    bool owndata() const {
+        return detail::check_flags(m_ptr, detail::npy_api::NPY_ARRAY_OWNDATA_);
+    }
+
+    /// Pointer to the contained data. If index is not provided, points to the
+    /// beginning of the buffer. May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    const void *data(Ix... index) const {
+        return static_cast<const void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
+    }
+
+    /// Mutable pointer to the contained data. If index is not provided, points to the
+    /// beginning of the buffer. May throw if the index would lead to out of bounds access.
+    /// May throw if the array is not writeable.
+    template <typename... Ix>
+    void *mutable_data(Ix... index) {
+        check_writeable();
+        return static_cast<void *>(detail::array_proxy(m_ptr)->data + offset_at(index...));
+    }
+
+    /// Byte offset from beginning of the array to a given index (full or partial).
+    /// May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    ssize_t offset_at(Ix... index) const {
+        if ((ssize_t) sizeof...(index) > ndim()) {
+            fail_dim_check(sizeof...(index), "too many indices for an array");
+        }
+        return byte_offset(ssize_t(index)...);
+    }
+
+    ssize_t offset_at() const { return 0; }
+
+    /// Item count from beginning of the array to a given index (full or partial).
+    /// May throw if the index would lead to out of bounds access.
+    template <typename... Ix>
+    ssize_t index_at(Ix... index) const {
+        return offset_at(index...) / itemsize();
+    }
+
+    /**
+     * Returns a proxy object that provides access to the array's data without bounds or
+     * dimensionality checking.  Will throw if the array is missing the `writeable` flag.  Use with
+     * care: the array must not be destroyed or reshaped for the duration of the returned object,
+     * and the caller must take care not to access invalid dimensions or dimension indices.
+     */
+    template <typename T, ssize_t Dims = -1>
+    detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
+        if (Dims >= 0 && ndim() != Dims) {
+            throw std::domain_error("array has incorrect number of dimensions: "
+                                    + std::to_string(ndim()) + "; expected "
+                                    + std::to_string(Dims));
+        }
+        return detail::unchecked_mutable_reference<T, Dims>(
+            mutable_data(), shape(), strides(), ndim());
+    }
+
+    /**
+     * Returns a proxy object that provides const access to the array's data without bounds or
+     * dimensionality checking.  Unlike `mutable_unchecked()`, this does not require that the
+     * underlying array have the `writable` flag.  Use with care: the array must not be destroyed
+     * or reshaped for the duration of the returned object, and the caller must take care not to
+     * access invalid dimensions or dimension indices.
+     */
+    template <typename T, ssize_t Dims = -1>
+    detail::unchecked_reference<T, Dims> unchecked() const & {
+        if (Dims >= 0 && ndim() != Dims) {
+            throw std::domain_error("array has incorrect number of dimensions: "
+                                    + std::to_string(ndim()) + "; expected "
+                                    + std::to_string(Dims));
+        }
+        return detail::unchecked_reference<T, Dims>(data(), shape(), strides(), ndim());
+    }
+
+    /// Return a new view with all of the dimensions of length 1 removed
+    array squeeze() {
+        auto &api = detail::npy_api::get();
+        return reinterpret_steal<array>(api.PyArray_Squeeze_(m_ptr));
+    }
+
+    /// Resize array to given shape
+    /// If refcheck is true and more that one reference exist to this array
+    /// then resize will succeed only if it makes a reshape, i.e. original size doesn't change
+    void resize(ShapeContainer new_shape, bool refcheck = true) {
+        detail::npy_api::PyArray_Dims d
+            = {// Use reinterpret_cast for PyPy on Windows (remove if fixed, checked on 7.3.1)
+               reinterpret_cast<Py_intptr_t *>(new_shape->data()),
+               int(new_shape->size())};
+        // try to resize, set ordering param to -1 cause it's not used anyway
+        auto new_array = reinterpret_steal<object>(
+            detail::npy_api::get().PyArray_Resize_(m_ptr, &d, int(refcheck), -1));
+        if (!new_array) {
+            throw error_already_set();
+        }
+        if (isinstance<array>(new_array)) {
+            *this = std::move(new_array);
+        }
+    }
+
+    /// Optional `order` parameter omitted, to be added as needed.
+    array reshape(ShapeContainer new_shape) {
+        detail::npy_api::PyArray_Dims d
+            = {reinterpret_cast<Py_intptr_t *>(new_shape->data()), int(new_shape->size())};
+        auto new_array
+            = reinterpret_steal<array>(detail::npy_api::get().PyArray_Newshape_(m_ptr, &d, 0));
+        if (!new_array) {
+            throw error_already_set();
+        }
+        return new_array;
+    }
+
+    /// Create a view of an array in a different data type.
+    /// This function may fundamentally reinterpret the data in the array.
+    /// It is the responsibility of the caller to ensure that this is safe.
+    /// Only supports the `dtype` argument, the `type` argument is omitted,
+    /// to be added as needed.
+    array view(const std::string &dtype) {
+        auto &api = detail::npy_api::get();
+        auto new_view = reinterpret_steal<array>(api.PyArray_View_(
+            m_ptr, dtype::from_args(pybind11::str(dtype)).release().ptr(), nullptr));
+        if (!new_view) {
+            throw error_already_set();
+        }
+        return new_view;
+    }
+
+    /// Ensure that the argument is a NumPy array
+    /// In case of an error, nullptr is returned and the Python error is cleared.
+    static array ensure(handle h, int ExtraFlags = 0) {
+        auto result = reinterpret_steal<array>(raw_array(h.ptr(), ExtraFlags));
+        if (!result) {
+            PyErr_Clear();
+        }
+        return result;
+    }
+
+protected:
+    template <typename, typename>
+    friend struct detail::npy_format_descriptor;
+
+    void fail_dim_check(ssize_t dim, const std::string &msg) const {
+        throw index_error(msg + ": " + std::to_string(dim) + " (ndim = " + std::to_string(ndim())
+                          + ')');
+    }
+
+    template <typename... Ix>
+    ssize_t byte_offset(Ix... index) const {
+        check_dimensions(index...);
+        return detail::byte_offset_unsafe(strides(), ssize_t(index)...);
+    }
+
+    void check_writeable() const {
+        if (!writeable()) {
+            throw std::domain_error("array is not writeable");
+        }
+    }
+
+    template <typename... Ix>
+    void check_dimensions(Ix... index) const {
+        check_dimensions_impl(ssize_t(0), shape(), ssize_t(index)...);
+    }
+
+    void check_dimensions_impl(ssize_t, const ssize_t *) const {}
+
+    template <typename... Ix>
+    void check_dimensions_impl(ssize_t axis, const ssize_t *shape, ssize_t i, Ix... index) const {
+        if (i >= *shape) {
+            throw index_error(std::string("index ") + std::to_string(i)
+                              + " is out of bounds for axis " + std::to_string(axis)
+                              + " with size " + std::to_string(*shape));
+        }
+        check_dimensions_impl(axis + 1, shape + 1, index...);
+    }
+
+    /// Create array from any object -- always returns a new reference
+    static PyObject *raw_array(PyObject *ptr, int ExtraFlags = 0) {
+        if (ptr == nullptr) {
+            set_error(PyExc_ValueError, "cannot create a pybind11::array from a nullptr");
+            return nullptr;
+        }
+        return detail::npy_api::get().PyArray_FromAny_(
+            ptr, nullptr, 0, 0, detail::npy_api::NPY_ARRAY_ENSUREARRAY_ | ExtraFlags, nullptr);
+    }
+};
+
+template <typename T, int ExtraFlags = array::forcecast>
+class array_t : public array {
+private:
+    struct private_ctor {};
+    // Delegating constructor needed when both moving and accessing in the same constructor
+    array_t(private_ctor,
+            ShapeContainer &&shape,
+            StridesContainer &&strides,
+            const T *ptr,
+            handle base)
+        : array(std::move(shape), std::move(strides), ptr, base) {}
+
+public:
+    static_assert(!detail::array_info<T>::is_array, "Array types cannot be used with array_t");
+
+    using value_type = T;
+
+    array_t() : array(0, static_cast<const T *>(nullptr)) {}
+    array_t(handle h, borrowed_t) : array(h, borrowed_t{}) {}
+    array_t(handle h, stolen_t) : array(h, stolen_t{}) {}
+
+    PYBIND11_DEPRECATED("Use array_t<T>::ensure() instead")
+    array_t(handle h, bool is_borrowed) : array(raw_array_t(h.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            PyErr_Clear();
+        }
+        if (!is_borrowed) {
+            Py_XDECREF(h.ptr());
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    array_t(const object &o) : array(raw_array_t(o.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    explicit array_t(const buffer_info &info, handle base = handle()) : array(info, base) {}
+
+    array_t(ShapeContainer shape,
+            StridesContainer strides,
+            const T *ptr = nullptr,
+            handle base = handle())
+        : array(std::move(shape), std::move(strides), ptr, base) {}
+
+    explicit array_t(ShapeContainer shape, const T *ptr = nullptr, handle base = handle())
+        : array_t(private_ctor{},
+                  std::move(shape),
+                  (ExtraFlags & f_style) != 0 ? detail::f_strides(*shape, itemsize())
+                                              : detail::c_strides(*shape, itemsize()),
+                  ptr,
+                  base) {}
+
+    explicit array_t(ssize_t count, const T *ptr = nullptr, handle base = handle())
+        : array({count}, {}, ptr, base) {}
+
+    constexpr ssize_t itemsize() const { return sizeof(T); }
+
+    template <typename... Ix>
+    ssize_t index_at(Ix... index) const {
+        return offset_at(index...) / itemsize();
+    }
+
+    template <typename... Ix>
+    const T *data(Ix... index) const {
+        return static_cast<const T *>(array::data(index...));
+    }
+
+    template <typename... Ix>
+    T *mutable_data(Ix... index) {
+        return static_cast<T *>(array::mutable_data(index...));
+    }
+
+    // Reference to element at a given index
+    template <typename... Ix>
+    const T &at(Ix... index) const {
+        if ((ssize_t) sizeof...(index) != ndim()) {
+            fail_dim_check(sizeof...(index), "index dimension mismatch");
+        }
+        return *(static_cast<const T *>(array::data())
+                 + byte_offset(ssize_t(index)...) / itemsize());
+    }
+
+    // Mutable reference to element at a given index
+    template <typename... Ix>
+    T &mutable_at(Ix... index) {
+        if ((ssize_t) sizeof...(index) != ndim()) {
+            fail_dim_check(sizeof...(index), "index dimension mismatch");
+        }
+        return *(static_cast<T *>(array::mutable_data())
+                 + byte_offset(ssize_t(index)...) / itemsize());
+    }
+
+    /**
+     * Returns a proxy object that provides access to the array's data without bounds or
+     * dimensionality checking.  Will throw if the array is missing the `writeable` flag.  Use with
+     * care: the array must not be destroyed or reshaped for the duration of the returned object,
+     * and the caller must take care not to access invalid dimensions or dimension indices.
+     */
+    template <ssize_t Dims = -1>
+    detail::unchecked_mutable_reference<T, Dims> mutable_unchecked() & {
+        return array::mutable_unchecked<T, Dims>();
+    }
+
+    /**
+     * Returns a proxy object that provides const access to the array's data without bounds or
+     * dimensionality checking.  Unlike `mutable_unchecked()`, this does not require that the
+     * underlying array have the `writable` flag.  Use with care: the array must not be destroyed
+     * or reshaped for the duration of the returned object, and the caller must take care not to
+     * access invalid dimensions or dimension indices.
+     */
+    template <ssize_t Dims = -1>
+    detail::unchecked_reference<T, Dims> unchecked() const & {
+        return array::unchecked<T, Dims>();
+    }
+
+    /// Ensure that the argument is a NumPy array of the correct dtype (and if not, try to convert
+    /// it).  In case of an error, nullptr is returned and the Python error is cleared.
+    static array_t ensure(handle h) {
+        auto result = reinterpret_steal<array_t>(raw_array_t(h.ptr()));
+        if (!result) {
+            PyErr_Clear();
+        }
+        return result;
+    }
+
+    static bool check_(handle h) {
+        const auto &api = detail::npy_api::get();
+        return api.PyArray_Check_(h.ptr())
+               && api.PyArray_EquivTypes_(detail::array_proxy(h.ptr())->descr,
+                                          dtype::of<T>().ptr())
+               && detail::check_flags(h.ptr(), ExtraFlags & (array::c_style | array::f_style));
+    }
+
+protected:
+    /// Create array from any object -- always returns a new reference
+    static PyObject *raw_array_t(PyObject *ptr) {
+        if (ptr == nullptr) {
+            set_error(PyExc_ValueError, "cannot create a pybind11::array_t from a nullptr");
+            return nullptr;
+        }
+        return detail::npy_api::get().PyArray_FromAny_(ptr,
+                                                       dtype::of<T>().release().ptr(),
+                                                       0,
+                                                       0,
+                                                       detail::npy_api::NPY_ARRAY_ENSUREARRAY_
+                                                           | ExtraFlags,
+                                                       nullptr);
+    }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
+    static std::string format() {
+        return detail::npy_format_descriptor<typename std::remove_cv<T>::type>::format();
+    }
+};
+
+template <size_t N>
+struct format_descriptor<char[N]> {
+    static std::string format() { return std::to_string(N) + 's'; }
+};
+template <size_t N>
+struct format_descriptor<std::array<char, N>> {
+    static std::string format() { return std::to_string(N) + 's'; }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<std::is_enum<T>::value>> {
+    static std::string format() {
+        return format_descriptor<
+            typename std::remove_cv<typename std::underlying_type<T>::type>::type>::format();
+    }
+};
+
+template <typename T>
+struct format_descriptor<T, detail::enable_if_t<detail::array_info<T>::is_array>> {
+    static std::string format() {
+        using namespace detail;
+        static constexpr auto extents = const_name("(") + array_info<T>::extents + const_name(")");
+        return extents.text + format_descriptor<remove_all_extents_t<T>>::format();
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename T, int ExtraFlags>
+struct pyobject_caster<array_t<T, ExtraFlags>> {
+    using type = array_t<T, ExtraFlags>;
+
+    bool load(handle src, bool convert) {
+        if (!convert && !type::check_(src)) {
+            return false;
+        }
+        value = type::ensure(src);
+        return static_cast<bool>(value);
+    }
+
+    static handle cast(const handle &src, return_value_policy /* policy */, handle /* parent */) {
+        return src.inc_ref();
+    }
+    PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name);
+};
+
+template <typename T>
+struct compare_buffer_info<T, detail::enable_if_t<detail::is_pod_struct<T>::value>> {
+    static bool compare(const buffer_info &b) {
+        return npy_api::get().PyArray_EquivTypes_(dtype::of<T>().ptr(), dtype(b).ptr());
+    }
+};
+
+template <typename T, typename = void>
+struct npy_format_descriptor_name;
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<std::is_integral<T>::value>> {
+    static constexpr auto name = const_name<std::is_same<T, bool>::value>(
+        const_name("bool"),
+        const_name<std::is_signed<T>::value>("numpy.int", "numpy.uint")
+            + const_name<sizeof(T) * 8>());
+};
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<std::is_floating_point<T>::value>> {
+    static constexpr auto name = const_name < std::is_same<T, float>::value
+                                 || std::is_same<T, const float>::value
+                                 || std::is_same<T, double>::value
+                                 || std::is_same<T, const double>::value
+                                        > (const_name("numpy.float") + const_name<sizeof(T) * 8>(),
+                                           const_name("numpy.longdouble"));
+};
+
+template <typename T>
+struct npy_format_descriptor_name<T, enable_if_t<is_complex<T>::value>> {
+    static constexpr auto name = const_name < std::is_same<typename T::value_type, float>::value
+                                 || std::is_same<typename T::value_type, const float>::value
+                                 || std::is_same<typename T::value_type, double>::value
+                                 || std::is_same<typename T::value_type, const double>::value
+                                        > (const_name("numpy.complex")
+                                               + const_name<sizeof(typename T::value_type) * 16>(),
+                                           const_name("numpy.longcomplex"));
+};
+
+template <typename T>
+struct npy_format_descriptor<
+    T,
+    enable_if_t<satisfies_any_of<T, std::is_arithmetic, is_complex>::value>>
+    : npy_format_descriptor_name<T> {
+private:
+    // NB: the order here must match the one in common.h
+    constexpr static const int values[15] = {npy_api::NPY_BOOL_,
+                                             npy_api::NPY_BYTE_,
+                                             npy_api::NPY_UBYTE_,
+                                             npy_api::NPY_INT16_,
+                                             npy_api::NPY_UINT16_,
+                                             npy_api::NPY_INT32_,
+                                             npy_api::NPY_UINT32_,
+                                             npy_api::NPY_INT64_,
+                                             npy_api::NPY_UINT64_,
+                                             npy_api::NPY_FLOAT_,
+                                             npy_api::NPY_DOUBLE_,
+                                             npy_api::NPY_LONGDOUBLE_,
+                                             npy_api::NPY_CFLOAT_,
+                                             npy_api::NPY_CDOUBLE_,
+                                             npy_api::NPY_CLONGDOUBLE_};
+
+public:
+    static constexpr int value = values[detail::is_fmt_numeric<T>::index];
+
+    static pybind11::dtype dtype() { return pybind11::dtype(/*typenum*/ value); }
+};
+
+template <typename T>
+struct npy_format_descriptor<T, enable_if_t<is_same_ignoring_cvref<T, PyObject *>::value>> {
+    static constexpr auto name = const_name("object");
+
+    static constexpr int value = npy_api::NPY_OBJECT_;
+
+    static pybind11::dtype dtype() { return pybind11::dtype(/*typenum*/ value); }
+};
+
+#define PYBIND11_DECL_CHAR_FMT                                                                    \
+    static constexpr auto name = const_name("S") + const_name<N>();                               \
+    static pybind11::dtype dtype() {                                                              \
+        return pybind11::dtype(std::string("S") + std::to_string(N));                             \
+    }
+template <size_t N>
+struct npy_format_descriptor<char[N]> {
+    PYBIND11_DECL_CHAR_FMT
+};
+template <size_t N>
+struct npy_format_descriptor<std::array<char, N>> {
+    PYBIND11_DECL_CHAR_FMT
+};
+#undef PYBIND11_DECL_CHAR_FMT
+
+template <typename T>
+struct npy_format_descriptor<T, enable_if_t<array_info<T>::is_array>> {
+private:
+    using base_descr = npy_format_descriptor<typename array_info<T>::type>;
+
+public:
+    static_assert(!array_info<T>::is_empty, "Zero-sized arrays are not supported");
+
+    static constexpr auto name
+        = const_name("(") + array_info<T>::extents + const_name(")") + base_descr::name;
+    static pybind11::dtype dtype() {
+        list shape;
+        array_info<T>::append_extents(shape);
+        return pybind11::dtype::from_args(
+            pybind11::make_tuple(base_descr::dtype(), std::move(shape)));
+    }
+};
+
+template <typename T>
+struct npy_format_descriptor<T, enable_if_t<std::is_enum<T>::value>> {
+private:
+    using base_descr = npy_format_descriptor<typename std::underlying_type<T>::type>;
+
+public:
+    static constexpr auto name = base_descr::name;
+    static pybind11::dtype dtype() { return base_descr::dtype(); }
+};
+
+struct field_descriptor {
+    const char *name;
+    ssize_t offset;
+    ssize_t size;
+    std::string format;
+    dtype descr;
+};
+
+PYBIND11_NOINLINE void register_structured_dtype(any_container<field_descriptor> fields,
+                                                 const std::type_info &tinfo,
+                                                 ssize_t itemsize,
+                                                 bool (*direct_converter)(PyObject *, void *&)) {
+
+    auto &numpy_internals = get_numpy_internals();
+    if (numpy_internals.get_type_info(tinfo, false)) {
+        pybind11_fail("NumPy: dtype is already registered");
+    }
+
+    // Use ordered fields because order matters as of NumPy 1.14:
+    // https://docs.scipy.org/doc/numpy/release.html#multiple-field-indexing-assignment-of-structured-arrays
+    std::vector<field_descriptor> ordered_fields(std::move(fields));
+    std::sort(
+        ordered_fields.begin(),
+        ordered_fields.end(),
+        [](const field_descriptor &a, const field_descriptor &b) { return a.offset < b.offset; });
+
+    list names, formats, offsets;
+    for (auto &field : ordered_fields) {
+        if (!field.descr) {
+            pybind11_fail(std::string("NumPy: unsupported field dtype: `") + field.name + "` @ "
+                          + tinfo.name());
+        }
+        names.append(pybind11::str(field.name));
+        formats.append(field.descr);
+        offsets.append(pybind11::int_(field.offset));
+    }
+    auto *dtype_ptr
+        = pybind11::dtype(std::move(names), std::move(formats), std::move(offsets), itemsize)
+              .release()
+              .ptr();
+
+    // There is an existing bug in NumPy (as of v1.11): trailing bytes are
+    // not encoded explicitly into the format string. This will supposedly
+    // get fixed in v1.12; for further details, see these:
+    // - https://github.com/numpy/numpy/issues/7797
+    // - https://github.com/numpy/numpy/pull/7798
+    // Because of this, we won't use numpy's logic to generate buffer format
+    // strings and will just do it ourselves.
+    ssize_t offset = 0;
+    std::ostringstream oss;
+    // mark the structure as unaligned with '^', because numpy and C++ don't
+    // always agree about alignment (particularly for complex), and we're
+    // explicitly listing all our padding. This depends on none of the fields
+    // overriding the endianness. Putting the ^ in front of individual fields
+    // isn't guaranteed to work due to https://github.com/numpy/numpy/issues/9049
+    oss << "^T{";
+    for (auto &field : ordered_fields) {
+        if (field.offset > offset) {
+            oss << (field.offset - offset) << 'x';
+        }
+        oss << field.format << ':' << field.name << ':';
+        offset = field.offset + field.size;
+    }
+    if (itemsize > offset) {
+        oss << (itemsize - offset) << 'x';
+    }
+    oss << '}';
+    auto format_str = oss.str();
+
+    // Smoke test: verify that NumPy properly parses our buffer format string
+    auto &api = npy_api::get();
+    auto arr = array(buffer_info(nullptr, itemsize, format_str, 1));
+    if (!api.PyArray_EquivTypes_(dtype_ptr, arr.dtype().ptr())) {
+        pybind11_fail("NumPy: invalid buffer descriptor!");
+    }
+
+    auto tindex = std::type_index(tinfo);
+    numpy_internals.registered_dtypes[tindex] = {dtype_ptr, std::move(format_str)};
+    with_internals([tindex, &direct_converter](internals &internals) {
+        internals.direct_conversions[tindex].push_back(direct_converter);
+    });
+}
+
+template <typename T, typename SFINAE>
+struct npy_format_descriptor {
+    static_assert(is_pod_struct<T>::value,
+                  "Attempt to use a non-POD or unimplemented POD type as a numpy dtype");
+
+    static constexpr auto name = make_caster<T>::name;
+
+    static pybind11::dtype dtype() { return reinterpret_borrow<pybind11::dtype>(dtype_ptr()); }
+
+    static std::string format() {
+        static auto format_str = get_numpy_internals().get_type_info<T>(true)->format_str;
+        return format_str;
+    }
+
+    static void register_dtype(any_container<field_descriptor> fields) {
+        register_structured_dtype(std::move(fields),
+                                  typeid(typename std::remove_cv<T>::type),
+                                  sizeof(T),
+                                  &direct_converter);
+    }
+
+private:
+    static PyObject *dtype_ptr() {
+        static PyObject *ptr = get_numpy_internals().get_type_info<T>(true)->dtype_ptr;
+        return ptr;
+    }
+
+    static bool direct_converter(PyObject *obj, void *&value) {
+        auto &api = npy_api::get();
+        if (!PyObject_TypeCheck(obj, api.PyVoidArrType_Type_)) {
+            return false;
+        }
+        if (auto descr = reinterpret_steal<object>(api.PyArray_DescrFromScalar_(obj))) {
+            if (api.PyArray_EquivTypes_(dtype_ptr(), descr.ptr())) {
+                value = ((PyVoidScalarObject_Proxy *) obj)->obval;
+                return true;
+            }
+        }
+        return false;
+    }
+};
+
+#ifdef __CLION_IDE__ // replace heavy macro with dummy code for the IDE (doesn't affect code)
+#    define PYBIND11_NUMPY_DTYPE(Type, ...) ((void) 0)
+#    define PYBIND11_NUMPY_DTYPE_EX(Type, ...) ((void) 0)
+#else
+
+#    define PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, Name)                                          \
+        ::pybind11::detail::field_descriptor {                                                    \
+            Name, offsetof(T, Field), sizeof(decltype(std::declval<T>().Field)),                  \
+                ::pybind11::format_descriptor<decltype(std::declval<T>().Field)>::format(),       \
+                ::pybind11::detail::npy_format_descriptor<                                        \
+                    decltype(std::declval<T>().Field)>::dtype()                                   \
+        }
+
+// Extract name, offset and format descriptor for a struct field
+#    define PYBIND11_FIELD_DESCRIPTOR(T, Field) PYBIND11_FIELD_DESCRIPTOR_EX(T, Field, #Field)
+
+// The main idea of this macro is borrowed from https://github.com/swansontec/map-macro
+// (C) William Swanson, Paul Fultz
+#    define PYBIND11_EVAL0(...) __VA_ARGS__
+#    define PYBIND11_EVAL1(...) PYBIND11_EVAL0(PYBIND11_EVAL0(PYBIND11_EVAL0(__VA_ARGS__)))
+#    define PYBIND11_EVAL2(...) PYBIND11_EVAL1(PYBIND11_EVAL1(PYBIND11_EVAL1(__VA_ARGS__)))
+#    define PYBIND11_EVAL3(...) PYBIND11_EVAL2(PYBIND11_EVAL2(PYBIND11_EVAL2(__VA_ARGS__)))
+#    define PYBIND11_EVAL4(...) PYBIND11_EVAL3(PYBIND11_EVAL3(PYBIND11_EVAL3(__VA_ARGS__)))
+#    define PYBIND11_EVAL(...) PYBIND11_EVAL4(PYBIND11_EVAL4(PYBIND11_EVAL4(__VA_ARGS__)))
+#    define PYBIND11_MAP_END(...)
+#    define PYBIND11_MAP_OUT
+#    define PYBIND11_MAP_COMMA ,
+#    define PYBIND11_MAP_GET_END() 0, PYBIND11_MAP_END
+#    define PYBIND11_MAP_NEXT0(test, next, ...) next PYBIND11_MAP_OUT
+#    define PYBIND11_MAP_NEXT1(test, next) PYBIND11_MAP_NEXT0(test, next, 0)
+#    define PYBIND11_MAP_NEXT(test, next) PYBIND11_MAP_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    if defined(_MSC_VER)                                                                         \
+        && !defined(__clang__) // MSVC is not as eager to expand macros, hence this workaround
+#        define PYBIND11_MAP_LIST_NEXT1(test, next)                                               \
+            PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
+#    else
+#        define PYBIND11_MAP_LIST_NEXT1(test, next)                                               \
+            PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
+#    endif
+#    define PYBIND11_MAP_LIST_NEXT(test, next)                                                    \
+        PYBIND11_MAP_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    define PYBIND11_MAP_LIST0(f, t, x, peek, ...)                                                \
+        f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST1)(f, t, peek, __VA_ARGS__)
+#    define PYBIND11_MAP_LIST1(f, t, x, peek, ...)                                                \
+        f(t, x) PYBIND11_MAP_LIST_NEXT(peek, PYBIND11_MAP_LIST0)(f, t, peek, __VA_ARGS__)
+// PYBIND11_MAP_LIST(f, t, a1, a2, ...) expands to f(t, a1), f(t, a2), ...
+#    define PYBIND11_MAP_LIST(f, t, ...)                                                          \
+        PYBIND11_EVAL(PYBIND11_MAP_LIST1(f, t, __VA_ARGS__, (), 0))
+
+#    define PYBIND11_NUMPY_DTYPE(Type, ...)                                                       \
+        ::pybind11::detail::npy_format_descriptor<Type>::register_dtype(                          \
+            ::std::vector<::pybind11::detail::field_descriptor>{                                  \
+                PYBIND11_MAP_LIST(PYBIND11_FIELD_DESCRIPTOR, Type, __VA_ARGS__)})
+
+#    if defined(_MSC_VER) && !defined(__clang__)
+#        define PYBIND11_MAP2_LIST_NEXT1(test, next)                                              \
+            PYBIND11_EVAL0(PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0))
+#    else
+#        define PYBIND11_MAP2_LIST_NEXT1(test, next)                                              \
+            PYBIND11_MAP_NEXT0(test, PYBIND11_MAP_COMMA next, 0)
+#    endif
+#    define PYBIND11_MAP2_LIST_NEXT(test, next)                                                   \
+        PYBIND11_MAP2_LIST_NEXT1(PYBIND11_MAP_GET_END test, next)
+#    define PYBIND11_MAP2_LIST0(f, t, x1, x2, peek, ...)                                          \
+        f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST1)(f, t, peek, __VA_ARGS__)
+#    define PYBIND11_MAP2_LIST1(f, t, x1, x2, peek, ...)                                          \
+        f(t, x1, x2) PYBIND11_MAP2_LIST_NEXT(peek, PYBIND11_MAP2_LIST0)(f, t, peek, __VA_ARGS__)
+// PYBIND11_MAP2_LIST(f, t, a1, a2, ...) expands to f(t, a1, a2), f(t, a3, a4), ...
+#    define PYBIND11_MAP2_LIST(f, t, ...)                                                         \
+        PYBIND11_EVAL(PYBIND11_MAP2_LIST1(f, t, __VA_ARGS__, (), 0))
+
+#    define PYBIND11_NUMPY_DTYPE_EX(Type, ...)                                                    \
+        ::pybind11::detail::npy_format_descriptor<Type>::register_dtype(                          \
+            ::std::vector<::pybind11::detail::field_descriptor>{                                  \
+                PYBIND11_MAP2_LIST(PYBIND11_FIELD_DESCRIPTOR_EX, Type, __VA_ARGS__)})
+
+#endif // __CLION_IDE__
+
+class common_iterator {
+public:
+    using container_type = std::vector<ssize_t>;
+    using value_type = container_type::value_type;
+    using size_type = container_type::size_type;
+
+    common_iterator() : m_strides() {}
+
+    common_iterator(void *ptr, const container_type &strides, const container_type &shape)
+        : p_ptr(reinterpret_cast<char *>(ptr)), m_strides(strides.size()) {
+        m_strides.back() = static_cast<value_type>(strides.back());
+        for (size_type i = m_strides.size() - 1; i != 0; --i) {
+            size_type j = i - 1;
+            auto s = static_cast<value_type>(shape[i]);
+            m_strides[j] = strides[j] + m_strides[i] - strides[i] * s;
+        }
+    }
+
+    void increment(size_type dim) { p_ptr += m_strides[dim]; }
+
+    void *data() const { return p_ptr; }
+
+private:
+    char *p_ptr{nullptr};
+    container_type m_strides;
+};
+
+template <size_t N>
+class multi_array_iterator {
+public:
+    using container_type = std::vector<ssize_t>;
+
+    multi_array_iterator(const std::array<buffer_info, N> &buffers, const container_type &shape)
+        : m_shape(shape.size()), m_index(shape.size(), 0), m_common_iterator() {
+
+        // Manual copy to avoid conversion warning if using std::copy
+        for (size_t i = 0; i < shape.size(); ++i) {
+            m_shape[i] = shape[i];
+        }
+
+        container_type strides(shape.size());
+        for (size_t i = 0; i < N; ++i) {
+            init_common_iterator(buffers[i], shape, m_common_iterator[i], strides);
+        }
+    }
+
+    multi_array_iterator &operator++() {
+        for (size_t j = m_index.size(); j != 0; --j) {
+            size_t i = j - 1;
+            if (++m_index[i] != m_shape[i]) {
+                increment_common_iterator(i);
+                break;
+            }
+            m_index[i] = 0;
+        }
+        return *this;
+    }
+
+    template <size_t K, class T = void>
+    T *data() const {
+        return reinterpret_cast<T *>(m_common_iterator[K].data());
+    }
+
+private:
+    using common_iter = common_iterator;
+
+    void init_common_iterator(const buffer_info &buffer,
+                              const container_type &shape,
+                              common_iter &iterator,
+                              container_type &strides) {
+        auto buffer_shape_iter = buffer.shape.rbegin();
+        auto buffer_strides_iter = buffer.strides.rbegin();
+        auto shape_iter = shape.rbegin();
+        auto strides_iter = strides.rbegin();
+
+        while (buffer_shape_iter != buffer.shape.rend()) {
+            if (*shape_iter == *buffer_shape_iter) {
+                *strides_iter = *buffer_strides_iter;
+            } else {
+                *strides_iter = 0;
+            }
+
+            ++buffer_shape_iter;
+            ++buffer_strides_iter;
+            ++shape_iter;
+            ++strides_iter;
+        }
+
+        std::fill(strides_iter, strides.rend(), 0);
+        iterator = common_iter(buffer.ptr, strides, shape);
+    }
+
+    void increment_common_iterator(size_t dim) {
+        for (auto &iter : m_common_iterator) {
+            iter.increment(dim);
+        }
+    }
+
+    container_type m_shape;
+    container_type m_index;
+    std::array<common_iter, N> m_common_iterator;
+};
+
+enum class broadcast_trivial { non_trivial, c_trivial, f_trivial };
+
+// Populates the shape and number of dimensions for the set of buffers.  Returns a
+// broadcast_trivial enum value indicating whether the broadcast is "trivial"--that is, has each
+// buffer being either a singleton or a full-size, C-contiguous (`c_trivial`) or Fortran-contiguous
+// (`f_trivial`) storage buffer; returns `non_trivial` otherwise.
+template <size_t N>
+broadcast_trivial
+broadcast(const std::array<buffer_info, N> &buffers, ssize_t &ndim, std::vector<ssize_t> &shape) {
+    ndim = std::accumulate(
+        buffers.begin(), buffers.end(), ssize_t(0), [](ssize_t res, const buffer_info &buf) {
+            return std::max(res, buf.ndim);
+        });
+
+    shape.clear();
+    shape.resize((size_t) ndim, 1);
+
+    // Figure out the output size, and make sure all input arrays conform (i.e. are either size 1
+    // or the full size).
+    for (size_t i = 0; i < N; ++i) {
+        auto res_iter = shape.rbegin();
+        auto end = buffers[i].shape.rend();
+        for (auto shape_iter = buffers[i].shape.rbegin(); shape_iter != end;
+             ++shape_iter, ++res_iter) {
+            const auto &dim_size_in = *shape_iter;
+            auto &dim_size_out = *res_iter;
+
+            // Each input dimension can either be 1 or `n`, but `n` values must match across
+            // buffers
+            if (dim_size_out == 1) {
+                dim_size_out = dim_size_in;
+            } else if (dim_size_in != 1 && dim_size_in != dim_size_out) {
+                pybind11_fail("pybind11::vectorize: incompatible size/dimension of inputs!");
+            }
+        }
+    }
+
+    bool trivial_broadcast_c = true;
+    bool trivial_broadcast_f = true;
+    for (size_t i = 0; i < N && (trivial_broadcast_c || trivial_broadcast_f); ++i) {
+        if (buffers[i].size == 1) {
+            continue;
+        }
+
+        // Require the same number of dimensions:
+        if (buffers[i].ndim != ndim) {
+            return broadcast_trivial::non_trivial;
+        }
+
+        // Require all dimensions be full-size:
+        if (!std::equal(buffers[i].shape.cbegin(), buffers[i].shape.cend(), shape.cbegin())) {
+            return broadcast_trivial::non_trivial;
+        }
+
+        // Check for C contiguity (but only if previous inputs were also C contiguous)
+        if (trivial_broadcast_c) {
+            ssize_t expect_stride = buffers[i].itemsize;
+            auto end = buffers[i].shape.crend();
+            for (auto shape_iter = buffers[i].shape.crbegin(),
+                      stride_iter = buffers[i].strides.crbegin();
+                 trivial_broadcast_c && shape_iter != end;
+                 ++shape_iter, ++stride_iter) {
+                if (expect_stride == *stride_iter) {
+                    expect_stride *= *shape_iter;
+                } else {
+                    trivial_broadcast_c = false;
+                }
+            }
+        }
+
+        // Check for Fortran contiguity (if previous inputs were also F contiguous)
+        if (trivial_broadcast_f) {
+            ssize_t expect_stride = buffers[i].itemsize;
+            auto end = buffers[i].shape.cend();
+            for (auto shape_iter = buffers[i].shape.cbegin(),
+                      stride_iter = buffers[i].strides.cbegin();
+                 trivial_broadcast_f && shape_iter != end;
+                 ++shape_iter, ++stride_iter) {
+                if (expect_stride == *stride_iter) {
+                    expect_stride *= *shape_iter;
+                } else {
+                    trivial_broadcast_f = false;
+                }
+            }
+        }
+    }
+
+    return trivial_broadcast_c   ? broadcast_trivial::c_trivial
+           : trivial_broadcast_f ? broadcast_trivial::f_trivial
+                                 : broadcast_trivial::non_trivial;
+}
+
+template <typename T>
+struct vectorize_arg {
+    static_assert(!std::is_rvalue_reference<T>::value,
+                  "Functions with rvalue reference arguments cannot be vectorized");
+    // The wrapped function gets called with this type:
+    using call_type = remove_reference_t<T>;
+    // Is this a vectorized argument?
+    static constexpr bool vectorize
+        = satisfies_any_of<call_type, std::is_arithmetic, is_complex, is_pod>::value
+          && satisfies_none_of<call_type,
+                               std::is_pointer,
+                               std::is_array,
+                               is_std_array,
+                               std::is_enum>::value
+          && (!std::is_reference<T>::value
+              || (std::is_lvalue_reference<T>::value && std::is_const<call_type>::value));
+    // Accept this type: an array for vectorized types, otherwise the type as-is:
+    using type = conditional_t<vectorize, array_t<remove_cv_t<call_type>, array::forcecast>, T>;
+};
+
+// py::vectorize when a return type is present
+template <typename Func, typename Return, typename... Args>
+struct vectorize_returned_array {
+    using Type = array_t<Return>;
+
+    static Type create(broadcast_trivial trivial, const std::vector<ssize_t> &shape) {
+        if (trivial == broadcast_trivial::f_trivial) {
+            return array_t<Return, array::f_style>(shape);
+        }
+        return array_t<Return>(shape);
+    }
+
+    static Return *mutable_data(Type &array) { return array.mutable_data(); }
+
+    static Return call(Func &f, Args &...args) { return f(args...); }
+
+    static void call(Return *out, size_t i, Func &f, Args &...args) { out[i] = f(args...); }
+};
+
+// py::vectorize when a return type is not present
+template <typename Func, typename... Args>
+struct vectorize_returned_array<Func, void, Args...> {
+    using Type = none;
+
+    static Type create(broadcast_trivial, const std::vector<ssize_t> &) { return none(); }
+
+    static void *mutable_data(Type &) { return nullptr; }
+
+    static detail::void_type call(Func &f, Args &...args) {
+        f(args...);
+        return {};
+    }
+
+    static void call(void *, size_t, Func &f, Args &...args) { f(args...); }
+};
+
+template <typename Func, typename Return, typename... Args>
+struct vectorize_helper {
+
+// NVCC for some reason breaks if NVectorized is private
+#ifdef __CUDACC__
+public:
+#else
+private:
+#endif
+
+    static constexpr size_t N = sizeof...(Args);
+    static constexpr size_t NVectorized = constexpr_sum(vectorize_arg<Args>::vectorize...);
+    static_assert(
+        NVectorized >= 1,
+        "pybind11::vectorize(...) requires a function with at least one vectorizable argument");
+
+public:
+    template <typename T,
+              // SFINAE to prevent shadowing the copy constructor.
+              typename = detail::enable_if_t<
+                  !std::is_same<vectorize_helper, typename std::decay<T>::type>::value>>
+    explicit vectorize_helper(T &&f) : f(std::forward<T>(f)) {}
+
+    object operator()(typename vectorize_arg<Args>::type... args) {
+        return run(args...,
+                   make_index_sequence<N>(),
+                   select_indices<vectorize_arg<Args>::vectorize...>(),
+                   make_index_sequence<NVectorized>());
+    }
+
+private:
+    remove_reference_t<Func> f;
+
+    // Internal compiler error in MSVC 19.16.27025.1 (Visual Studio 2017 15.9.4), when compiling
+    // with "/permissive-" flag when arg_call_types is manually inlined.
+    using arg_call_types = std::tuple<typename vectorize_arg<Args>::call_type...>;
+    template <size_t Index>
+    using param_n_t = typename std::tuple_element<Index, arg_call_types>::type;
+
+    using returned_array = vectorize_returned_array<Func, Return, Args...>;
+
+    // Runs a vectorized function given arguments tuple and three index sequences:
+    //     - Index is the full set of 0 ... (N-1) argument indices;
+    //     - VIndex is the subset of argument indices with vectorized parameters, letting us access
+    //       vectorized arguments (anything not in this sequence is passed through)
+    //     - BIndex is a incremental sequence (beginning at 0) of the same size as VIndex, so that
+    //       we can store vectorized buffer_infos in an array (argument VIndex has its buffer at
+    //       index BIndex in the array).
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    object run(typename vectorize_arg<Args>::type &...args,
+               index_sequence<Index...> i_seq,
+               index_sequence<VIndex...> vi_seq,
+               index_sequence<BIndex...> bi_seq) {
+
+        // Pointers to values the function was called with; the vectorized ones set here will start
+        // out as array_t<T> pointers, but they will be changed them to T pointers before we make
+        // call the wrapped function.  Non-vectorized pointers are left as-is.
+        std::array<void *, N> params{{reinterpret_cast<void *>(&args)...}};
+
+        // The array of `buffer_info`s of vectorized arguments:
+        std::array<buffer_info, NVectorized> buffers{
+            {reinterpret_cast<array *>(params[VIndex])->request()...}};
+
+        /* Determine dimensions parameters of output array */
+        ssize_t nd = 0;
+        std::vector<ssize_t> shape(0);
+        auto trivial = broadcast(buffers, nd, shape);
+        auto ndim = (size_t) nd;
+
+        size_t size
+            = std::accumulate(shape.begin(), shape.end(), (size_t) 1, std::multiplies<size_t>());
+
+        // If all arguments are 0-dimension arrays (i.e. single values) return a plain value (i.e.
+        // not wrapped in an array).
+        if (size == 1 && ndim == 0) {
+            PYBIND11_EXPAND_SIDE_EFFECTS(params[VIndex] = buffers[BIndex].ptr);
+            return cast(
+                returned_array::call(f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...));
+        }
+
+        auto result = returned_array::create(trivial, shape);
+
+        PYBIND11_WARNING_PUSH
+#ifdef PYBIND11_DETECTED_CLANG_WITH_MISLEADING_CALL_STD_MOVE_EXPLICITLY_WARNING
+        PYBIND11_WARNING_DISABLE_CLANG("-Wreturn-std-move")
+#endif
+
+        if (size == 0) {
+            return result;
+        }
+
+        /* Call the function */
+        auto *mutable_data = returned_array::mutable_data(result);
+        if (trivial == broadcast_trivial::non_trivial) {
+            apply_broadcast(buffers, params, mutable_data, size, shape, i_seq, vi_seq, bi_seq);
+        } else {
+            apply_trivial(buffers, params, mutable_data, size, i_seq, vi_seq, bi_seq);
+        }
+
+        return result;
+        PYBIND11_WARNING_POP
+    }
+
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    void apply_trivial(std::array<buffer_info, NVectorized> &buffers,
+                       std::array<void *, N> &params,
+                       Return *out,
+                       size_t size,
+                       index_sequence<Index...>,
+                       index_sequence<VIndex...>,
+                       index_sequence<BIndex...>) {
+
+        // Initialize an array of mutable byte references and sizes with references set to the
+        // appropriate pointer in `params`; as we iterate, we'll increment each pointer by its size
+        // (except for singletons, which get an increment of 0).
+        std::array<std::pair<unsigned char *&, const size_t>, NVectorized> vecparams{
+            {std::pair<unsigned char *&, const size_t>(
+                reinterpret_cast<unsigned char *&>(params[VIndex] = buffers[BIndex].ptr),
+                buffers[BIndex].size == 1 ? 0 : sizeof(param_n_t<VIndex>))...}};
+
+        for (size_t i = 0; i < size; ++i) {
+            returned_array::call(
+                out, i, f, *reinterpret_cast<param_n_t<Index> *>(params[Index])...);
+            for (auto &x : vecparams) {
+                x.first += x.second;
+            }
+        }
+    }
+
+    template <size_t... Index, size_t... VIndex, size_t... BIndex>
+    void apply_broadcast(std::array<buffer_info, NVectorized> &buffers,
+                         std::array<void *, N> &params,
+                         Return *out,
+                         size_t size,
+                         const std::vector<ssize_t> &output_shape,
+                         index_sequence<Index...>,
+                         index_sequence<VIndex...>,
+                         index_sequence<BIndex...>) {
+
+        multi_array_iterator<NVectorized> input_iter(buffers, output_shape);
+
+        for (size_t i = 0; i < size; ++i, ++input_iter) {
+            PYBIND11_EXPAND_SIDE_EFFECTS((params[VIndex] = input_iter.template data<BIndex>()));
+            returned_array::call(
+                out, i, f, *reinterpret_cast<param_n_t<Index> *>(std::get<Index>(params))...);
+        }
+    }
+};
+
+template <typename Func, typename Return, typename... Args>
+vectorize_helper<Func, Return, Args...> vectorize_extractor(const Func &f, Return (*)(Args...)) {
+    return detail::vectorize_helper<Func, Return, Args...>(f);
+}
+
+template <typename T, int Flags>
+struct handle_type_name<array_t<T, Flags>> {
+    static constexpr auto name
+        = const_name("numpy.ndarray[") + npy_format_descriptor<T>::name + const_name("]");
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// Vanilla pointer vectorizer:
+template <typename Return, typename... Args>
+detail::vectorize_helper<Return (*)(Args...), Return, Args...> vectorize(Return (*f)(Args...)) {
+    return detail::vectorize_helper<Return (*)(Args...), Return, Args...>(f);
+}
+
+// lambda vectorizer:
+template <typename Func, detail::enable_if_t<detail::is_lambda<Func>::value, int> = 0>
+auto vectorize(Func &&f)
+    -> decltype(detail::vectorize_extractor(std::forward<Func>(f),
+                                            (detail::function_signature_t<Func> *) nullptr)) {
+    return detail::vectorize_extractor(std::forward<Func>(f),
+                                       (detail::function_signature_t<Func> *) nullptr);
+}
+
+// Vectorize a class method (non-const):
+template <typename Return,
+          typename Class,
+          typename... Args,
+          typename Helper = detail::vectorize_helper<
+              decltype(std::mem_fn(std::declval<Return (Class::*)(Args...)>())),
+              Return,
+              Class *,
+              Args...>>
+Helper vectorize(Return (Class::*f)(Args...)) {
+    return Helper(std::mem_fn(f));
+}
+
+// Vectorize a class method (const):
+template <typename Return,
+          typename Class,
+          typename... Args,
+          typename Helper = detail::vectorize_helper<
+              decltype(std::mem_fn(std::declval<Return (Class::*)(Args...) const>())),
+              Return,
+              const Class *,
+              Args...>>
+Helper vectorize(Return (Class::*f)(Args...) const) {
+    return Helper(std::mem_fn(f));
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/operators.h b/lib/python3.11/site-packages/torch/include/pybind11/operators.h
new file mode 100644
index 0000000000000000000000000000000000000000..16a88ae171846844ddac28839e2150f11d287839
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/operators.h
@@ -0,0 +1,202 @@
+/*
+    pybind11/operator.h: Metatemplates for operator overloading
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Enumeration with all supported operator types
+enum op_id : int {
+    op_add,
+    op_sub,
+    op_mul,
+    op_div,
+    op_mod,
+    op_divmod,
+    op_pow,
+    op_lshift,
+    op_rshift,
+    op_and,
+    op_xor,
+    op_or,
+    op_neg,
+    op_pos,
+    op_abs,
+    op_invert,
+    op_int,
+    op_long,
+    op_float,
+    op_str,
+    op_cmp,
+    op_gt,
+    op_ge,
+    op_lt,
+    op_le,
+    op_eq,
+    op_ne,
+    op_iadd,
+    op_isub,
+    op_imul,
+    op_idiv,
+    op_imod,
+    op_ilshift,
+    op_irshift,
+    op_iand,
+    op_ixor,
+    op_ior,
+    op_complex,
+    op_bool,
+    op_nonzero,
+    op_repr,
+    op_truediv,
+    op_itruediv,
+    op_hash
+};
+
+enum op_type : int {
+    op_l, /* base type on left */
+    op_r, /* base type on right */
+    op_u  /* unary operator */
+};
+
+struct self_t {};
+static const self_t self = self_t();
+
+/// Type for an unused type slot
+struct undefined_t {};
+
+/// Don't warn about an unused variable
+inline self_t __self() { return self; }
+
+/// base template of operator implementations
+template <op_id, op_type, typename B, typename L, typename R>
+struct op_impl {};
+
+/// Operator implementation generator
+template <op_id id, op_type ot, typename L, typename R>
+struct op_ {
+    static constexpr bool op_enable_if_hook = true;
+    template <typename Class, typename... Extra>
+    void execute(Class &cl, const Extra &...extra) const {
+        using Base = typename Class::type;
+        using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
+        using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
+        using op = op_impl<id, ot, Base, L_type, R_type>;
+        cl.def(op::name(), &op::execute, is_operator(), extra...);
+    }
+    template <typename Class, typename... Extra>
+    void execute_cast(Class &cl, const Extra &...extra) const {
+        using Base = typename Class::type;
+        using L_type = conditional_t<std::is_same<L, self_t>::value, Base, L>;
+        using R_type = conditional_t<std::is_same<R, self_t>::value, Base, R>;
+        using op = op_impl<id, ot, Base, L_type, R_type>;
+        cl.def(op::name(), &op::execute_cast, is_operator(), extra...);
+    }
+};
+
+#define PYBIND11_BINARY_OPERATOR(id, rid, op, expr)                                               \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_l, B, L, R> {                                                      \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(const L &l, const R &r) -> decltype(expr) { return (expr); }          \
+        static B execute_cast(const L &l, const R &r) { return B(expr); }                         \
+    };                                                                                            \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_r, B, L, R> {                                                      \
+        static char const *name() { return "__" #rid "__"; }                                      \
+        static auto execute(const R &r, const L &l) -> decltype(expr) { return (expr); }          \
+        static B execute_cast(const R &r, const L &l) { return B(expr); }                         \
+    };                                                                                            \
+    inline op_<op_##id, op_l, self_t, self_t> op(const self_t &, const self_t &) {                \
+        return op_<op_##id, op_l, self_t, self_t>();                                              \
+    }                                                                                             \
+    template <typename T>                                                                         \
+    op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) {                                 \
+        return op_<op_##id, op_l, self_t, T>();                                                   \
+    }                                                                                             \
+    template <typename T>                                                                         \
+    op_<op_##id, op_r, T, self_t> op(const T &, const self_t &) {                                 \
+        return op_<op_##id, op_r, T, self_t>();                                                   \
+    }
+
+#define PYBIND11_INPLACE_OPERATOR(id, op, expr)                                                   \
+    template <typename B, typename L, typename R>                                                 \
+    struct op_impl<op_##id, op_l, B, L, R> {                                                      \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(L &l, const R &r) -> decltype(expr) { return expr; }                  \
+        static B execute_cast(L &l, const R &r) { return B(expr); }                               \
+    };                                                                                            \
+    template <typename T>                                                                         \
+    op_<op_##id, op_l, self_t, T> op(const self_t &, const T &) {                                 \
+        return op_<op_##id, op_l, self_t, T>();                                                   \
+    }
+
+#define PYBIND11_UNARY_OPERATOR(id, op, expr)                                                     \
+    template <typename B, typename L>                                                             \
+    struct op_impl<op_##id, op_u, B, L, undefined_t> {                                            \
+        static char const *name() { return "__" #id "__"; }                                       \
+        static auto execute(const L &l) -> decltype(expr) { return expr; }                        \
+        static B execute_cast(const L &l) { return B(expr); }                                     \
+    };                                                                                            \
+    inline op_<op_##id, op_u, self_t, undefined_t> op(const self_t &) {                           \
+        return op_<op_##id, op_u, self_t, undefined_t>();                                         \
+    }
+
+PYBIND11_BINARY_OPERATOR(sub, rsub, operator-, l - r)
+PYBIND11_BINARY_OPERATOR(add, radd, operator+, l + r)
+PYBIND11_BINARY_OPERATOR(mul, rmul, operator*, l *r)
+PYBIND11_BINARY_OPERATOR(truediv, rtruediv, operator/, l / r)
+PYBIND11_BINARY_OPERATOR(mod, rmod, operator%, l % r)
+PYBIND11_BINARY_OPERATOR(lshift, rlshift, operator<<, l << r)
+PYBIND11_BINARY_OPERATOR(rshift, rrshift, operator>>, l >> r)
+PYBIND11_BINARY_OPERATOR(and, rand, operator&, l &r)
+PYBIND11_BINARY_OPERATOR(xor, rxor, operator^, l ^ r)
+PYBIND11_BINARY_OPERATOR(eq, eq, operator==, l == r)
+PYBIND11_BINARY_OPERATOR(ne, ne, operator!=, l != r)
+PYBIND11_BINARY_OPERATOR(or, ror, operator|, l | r)
+PYBIND11_BINARY_OPERATOR(gt, lt, operator>, l > r)
+PYBIND11_BINARY_OPERATOR(ge, le, operator>=, l >= r)
+PYBIND11_BINARY_OPERATOR(lt, gt, operator<, l < r)
+PYBIND11_BINARY_OPERATOR(le, ge, operator<=, l <= r)
+// PYBIND11_BINARY_OPERATOR(pow,       rpow,         pow,          std::pow(l,  r))
+PYBIND11_INPLACE_OPERATOR(iadd, operator+=, l += r)
+PYBIND11_INPLACE_OPERATOR(isub, operator-=, l -= r)
+PYBIND11_INPLACE_OPERATOR(imul, operator*=, l *= r)
+PYBIND11_INPLACE_OPERATOR(itruediv, operator/=, l /= r)
+PYBIND11_INPLACE_OPERATOR(imod, operator%=, l %= r)
+PYBIND11_INPLACE_OPERATOR(ilshift, operator<<=, l <<= r)
+PYBIND11_INPLACE_OPERATOR(irshift, operator>>=, l >>= r)
+PYBIND11_INPLACE_OPERATOR(iand, operator&=, l &= r)
+PYBIND11_INPLACE_OPERATOR(ixor, operator^=, l ^= r)
+PYBIND11_INPLACE_OPERATOR(ior, operator|=, l |= r)
+PYBIND11_UNARY_OPERATOR(neg, operator-, -l)
+PYBIND11_UNARY_OPERATOR(pos, operator+, +l)
+// WARNING: This usage of `abs` should only be done for existing STL overloads.
+// Adding overloads directly in to the `std::` namespace is advised against:
+// https://en.cppreference.com/w/cpp/language/extending_std
+PYBIND11_UNARY_OPERATOR(abs, abs, std::abs(l))
+PYBIND11_UNARY_OPERATOR(hash, hash, std::hash<L>()(l))
+PYBIND11_UNARY_OPERATOR(invert, operator~, (~l))
+PYBIND11_UNARY_OPERATOR(bool, operator!, !!l)
+PYBIND11_UNARY_OPERATOR(int, int_, (int) l)
+PYBIND11_UNARY_OPERATOR(float, float_, (double) l)
+
+#undef PYBIND11_BINARY_OPERATOR
+#undef PYBIND11_INPLACE_OPERATOR
+#undef PYBIND11_UNARY_OPERATOR
+PYBIND11_NAMESPACE_END(detail)
+
+using detail::self;
+// Add named operators so that they are accessible via `py::`.
+using detail::hash;
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/options.h b/lib/python3.11/site-packages/torch/include/pybind11/options.h
new file mode 100644
index 0000000000000000000000000000000000000000..1b2122522d5b4043c9df57f2beb943682eb4b208
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/options.h
@@ -0,0 +1,92 @@
+/*
+    pybind11/options.h: global settings that are configurable at runtime.
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+class options {
+public:
+    // Default RAII constructor, which leaves settings as they currently are.
+    options() : previous_state(global_state()) {}
+
+    // Class is non-copyable.
+    options(const options &) = delete;
+    options &operator=(const options &) = delete;
+
+    // Destructor, which restores settings that were in effect before.
+    ~options() { global_state() = previous_state; }
+
+    // Setter methods (affect the global state):
+
+    options &disable_user_defined_docstrings() & {
+        global_state().show_user_defined_docstrings = false;
+        return *this;
+    }
+
+    options &enable_user_defined_docstrings() & {
+        global_state().show_user_defined_docstrings = true;
+        return *this;
+    }
+
+    options &disable_function_signatures() & {
+        global_state().show_function_signatures = false;
+        return *this;
+    }
+
+    options &enable_function_signatures() & {
+        global_state().show_function_signatures = true;
+        return *this;
+    }
+
+    options &disable_enum_members_docstring() & {
+        global_state().show_enum_members_docstring = false;
+        return *this;
+    }
+
+    options &enable_enum_members_docstring() & {
+        global_state().show_enum_members_docstring = true;
+        return *this;
+    }
+
+    // Getter methods (return the global state):
+
+    static bool show_user_defined_docstrings() {
+        return global_state().show_user_defined_docstrings;
+    }
+
+    static bool show_function_signatures() { return global_state().show_function_signatures; }
+
+    static bool show_enum_members_docstring() {
+        return global_state().show_enum_members_docstring;
+    }
+
+    // This type is not meant to be allocated on the heap.
+    void *operator new(size_t) = delete;
+
+private:
+    struct state {
+        bool show_user_defined_docstrings = true; //< Include user-supplied texts in docstrings.
+        bool show_function_signatures = true;     //< Include auto-generated function signatures
+                                                  //  in docstrings.
+        bool show_enum_members_docstring = true;  //< Include auto-generated member list in enum
+                                                  //  docstrings.
+    };
+
+    static state &global_state() {
+        static state instance;
+        return instance;
+    }
+
+    state previous_state;
+};
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/pybind11.h b/lib/python3.11/site-packages/torch/include/pybind11/pybind11.h
new file mode 100644
index 0000000000000000000000000000000000000000..5219c0ff856281ba6acecb0bb53cb432382ded54
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/pybind11.h
@@ -0,0 +1,2978 @@
+/*
+    pybind11/pybind11.h: Main header file of the C++11 python
+    binding generator library
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+#include "detail/class.h"
+#include "detail/exception_translation.h"
+#include "detail/init.h"
+#include "attr.h"
+#include "gil.h"
+#include "gil_safe_call_once.h"
+#include "options.h"
+#include "typing.h"
+
+#include <cstdlib>
+#include <cstring>
+#include <memory>
+#include <new>
+#include <string>
+#include <utility>
+#include <vector>
+
+#if defined(__cpp_lib_launder) && !(defined(_MSC_VER) && (_MSC_VER < 1914))
+#    define PYBIND11_STD_LAUNDER std::launder
+#    define PYBIND11_HAS_STD_LAUNDER 1
+#else
+#    define PYBIND11_STD_LAUNDER
+#    define PYBIND11_HAS_STD_LAUNDER 0
+#endif
+#if defined(__GNUG__) && !defined(__clang__)
+#    include <cxxabi.h>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+/* https://stackoverflow.com/questions/46798456/handling-gccs-noexcept-type-warning
+   This warning is about ABI compatibility, not code health.
+   It is only actually needed in a couple places, but apparently GCC 7 "generates this warning if
+   and only if the first template instantiation ... involves noexcept" [stackoverflow], therefore
+   it could get triggered from seemingly random places, depending on user code.
+   No other GCC version generates this warning.
+ */
+#if defined(__GNUC__) && __GNUC__ == 7
+PYBIND11_WARNING_DISABLE_GCC("-Wnoexcept-type")
+#endif
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline std::string replace_newlines_and_squash(const char *text) {
+    const char *whitespaces = " \t\n\r\f\v";
+    std::string result(text);
+    bool previous_is_whitespace = false;
+
+    if (result.size() >= 2) {
+        // Do not modify string representations
+        char first_char = result[0];
+        char last_char = result[result.size() - 1];
+        if (first_char == last_char && first_char == '\'') {
+            return result;
+        }
+    }
+    result.clear();
+
+    // Replace characters in whitespaces array with spaces and squash consecutive spaces
+    while (*text != '\0') {
+        if (std::strchr(whitespaces, *text)) {
+            if (!previous_is_whitespace) {
+                result += ' ';
+                previous_is_whitespace = true;
+            }
+        } else {
+            result += *text;
+            previous_is_whitespace = false;
+        }
+        ++text;
+    }
+
+    // Strip leading and trailing whitespaces
+    const size_t str_begin = result.find_first_not_of(whitespaces);
+    if (str_begin == std::string::npos) {
+        return "";
+    }
+
+    const size_t str_end = result.find_last_not_of(whitespaces);
+    const size_t str_range = str_end - str_begin + 1;
+
+    return result.substr(str_begin, str_range);
+}
+
+#if defined(_MSC_VER)
+#    define PYBIND11_COMPAT_STRDUP _strdup
+#else
+#    define PYBIND11_COMPAT_STRDUP strdup
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Wraps an arbitrary C++ function/method/lambda function/.. into a callable Python object
+class cpp_function : public function {
+public:
+    cpp_function() = default;
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(std::nullptr_t) {}
+    cpp_function(std::nullptr_t, const is_setter &) {}
+
+    /// Construct a cpp_function from a vanilla function pointer
+    template <typename Return, typename... Args, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (*f)(Args...), const Extra &...extra) {
+        initialize(f, f, extra...);
+    }
+
+    /// Construct a cpp_function from a lambda function (possibly with internal state)
+    template <typename Func,
+              typename... Extra,
+              typename = detail::enable_if_t<detail::is_lambda<Func>::value>>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Func &&f, const Extra &...extra) {
+        initialize(
+            std::forward<Func>(f), (detail::function_signature_t<Func> *) nullptr, extra...);
+    }
+
+    /// Construct a cpp_function from a class method (non-const, no ref-qualifier)
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...), const Extra &...extra) {
+        initialize(
+            [f](Class *c, Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+            (Return(*)(Class *, Arg...)) nullptr,
+            extra...);
+    }
+
+    /// Construct a cpp_function from a class method (non-const, lvalue ref-qualifier)
+    /// A copy of the overload for non-const functions without explicit ref-qualifier
+    /// but with an added `&`.
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) &, const Extra &...extra) {
+        initialize(
+            [f](Class *c, Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+            (Return(*)(Class *, Arg...)) nullptr,
+            extra...);
+    }
+
+    /// Construct a cpp_function from a class method (const, no ref-qualifier)
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) const, const Extra &...extra) {
+        initialize([f](const Class *c,
+                       Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+                   (Return(*)(const Class *, Arg...)) nullptr,
+                   extra...);
+    }
+
+    /// Construct a cpp_function from a class method (const, lvalue ref-qualifier)
+    /// A copy of the overload for const functions without explicit ref-qualifier
+    /// but with an added `&`.
+    template <typename Return, typename Class, typename... Arg, typename... Extra>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    cpp_function(Return (Class::*f)(Arg...) const &, const Extra &...extra) {
+        initialize([f](const Class *c,
+                       Arg... args) -> Return { return (c->*f)(std::forward<Arg>(args)...); },
+                   (Return(*)(const Class *, Arg...)) nullptr,
+                   extra...);
+    }
+
+    /// Return the function name
+    object name() const { return attr("__name__"); }
+
+protected:
+    struct InitializingFunctionRecordDeleter {
+        // `destruct(function_record, false)`: `initialize_generic` copies strings and
+        // takes care of cleaning up in case of exceptions. So pass `false` to `free_strings`.
+        void operator()(detail::function_record *rec) { destruct(rec, false); }
+    };
+    using unique_function_record
+        = std::unique_ptr<detail::function_record, InitializingFunctionRecordDeleter>;
+
+    /// Space optimization: don't inline this frequently instantiated fragment
+    PYBIND11_NOINLINE unique_function_record make_function_record() {
+        return unique_function_record(new detail::function_record());
+    }
+
+    /// Special internal constructor for functors, lambda functions, etc.
+    template <typename Func, typename Return, typename... Args, typename... Extra>
+    void initialize(Func &&f, Return (*)(Args...), const Extra &...extra) {
+        using namespace detail;
+        struct capture {
+            remove_reference_t<Func> f;
+        };
+
+        /* Store the function including any extra state it might have (e.g. a lambda capture
+         * object) */
+        // The unique_ptr makes sure nothing is leaked in case of an exception.
+        auto unique_rec = make_function_record();
+        auto *rec = unique_rec.get();
+
+        /* Store the capture object directly in the function record if there is enough space */
+        if (sizeof(capture) <= sizeof(rec->data)) {
+            /* Without these pragmas, GCC warns that there might not be
+               enough space to use the placement new operator. However, the
+               'if' statement above ensures that this is the case. */
+            PYBIND11_WARNING_PUSH
+
+#if defined(__GNUG__) && __GNUC__ >= 6
+            PYBIND11_WARNING_DISABLE_GCC("-Wplacement-new")
+#endif
+
+            new ((capture *) &rec->data) capture{std::forward<Func>(f)};
+
+#if !PYBIND11_HAS_STD_LAUNDER
+            PYBIND11_WARNING_DISABLE_GCC("-Wstrict-aliasing")
+#endif
+
+            // UB without std::launder, but without breaking ABI and/or
+            // a significant refactoring it's "impossible" to solve.
+            if (!std::is_trivially_destructible<capture>::value) {
+                rec->free_data = [](function_record *r) {
+                    auto data = PYBIND11_STD_LAUNDER((capture *) &r->data);
+                    (void) data;
+                    data->~capture();
+                };
+            }
+            PYBIND11_WARNING_POP
+        } else {
+            rec->data[0] = new capture{std::forward<Func>(f)};
+            rec->free_data = [](function_record *r) { delete ((capture *) r->data[0]); };
+        }
+
+        /* Type casters for the function arguments and return value */
+        using cast_in = argument_loader<Args...>;
+        using cast_out
+            = make_caster<conditional_t<std::is_void<Return>::value, void_type, Return>>;
+
+        static_assert(
+            expected_num_args<Extra...>(
+                sizeof...(Args), cast_in::args_pos >= 0, cast_in::has_kwargs),
+            "The number of argument annotations does not match the number of function arguments");
+
+        /* Dispatch code which converts function arguments and performs the actual function call */
+        rec->impl = [](function_call &call) -> handle {
+            cast_in args_converter;
+
+            /* Try to cast the function arguments into the C++ domain */
+            if (!args_converter.load_args(call)) {
+                return PYBIND11_TRY_NEXT_OVERLOAD;
+            }
+
+            /* Invoke call policy pre-call hook */
+            process_attributes<Extra...>::precall(call);
+
+            /* Get a pointer to the capture object */
+            const auto *data = (sizeof(capture) <= sizeof(call.func.data) ? &call.func.data
+                                                                          : call.func.data[0]);
+            auto *cap = const_cast<capture *>(reinterpret_cast<const capture *>(data));
+
+            /* Override policy for rvalues -- usually to enforce rvp::move on an rvalue */
+            return_value_policy policy
+                = return_value_policy_override<Return>::policy(call.func.policy);
+
+            /* Function scope guard -- defaults to the compile-to-nothing `void_type` */
+            using Guard = extract_guard_t<Extra...>;
+
+            /* Perform the function call */
+            handle result;
+            if (call.func.is_setter) {
+                (void) std::move(args_converter).template call<Return, Guard>(cap->f);
+                result = none().release();
+            } else {
+                result = cast_out::cast(
+                    std::move(args_converter).template call<Return, Guard>(cap->f),
+                    policy,
+                    call.parent);
+            }
+
+            /* Invoke call policy post-call hook */
+            process_attributes<Extra...>::postcall(call, result);
+
+            return result;
+        };
+
+        rec->nargs_pos = cast_in::args_pos >= 0
+                             ? static_cast<std::uint16_t>(cast_in::args_pos)
+                             : sizeof...(Args) - cast_in::has_kwargs; // Will get reduced more if
+                                                                      // we have a kw_only
+        rec->has_args = cast_in::args_pos >= 0;
+        rec->has_kwargs = cast_in::has_kwargs;
+
+        /* Process any user-provided function attributes */
+        process_attributes<Extra...>::init(extra..., rec);
+
+        {
+            constexpr bool has_kw_only_args = any_of<std::is_same<kw_only, Extra>...>::value,
+                           has_pos_only_args = any_of<std::is_same<pos_only, Extra>...>::value,
+                           has_arg_annotations = any_of<is_keyword<Extra>...>::value;
+            static_assert(has_arg_annotations || !has_kw_only_args,
+                          "py::kw_only requires the use of argument annotations");
+            static_assert(has_arg_annotations || !has_pos_only_args,
+                          "py::pos_only requires the use of argument annotations (for docstrings "
+                          "and aligning the annotations to the argument)");
+
+            static_assert(constexpr_sum(is_kw_only<Extra>::value...) <= 1,
+                          "py::kw_only may be specified only once");
+            static_assert(constexpr_sum(is_pos_only<Extra>::value...) <= 1,
+                          "py::pos_only may be specified only once");
+            constexpr auto kw_only_pos = constexpr_first<is_kw_only, Extra...>();
+            constexpr auto pos_only_pos = constexpr_first<is_pos_only, Extra...>();
+            static_assert(!(has_kw_only_args && has_pos_only_args) || pos_only_pos < kw_only_pos,
+                          "py::pos_only must come before py::kw_only");
+        }
+
+        /* Generate a readable signature describing the function's arguments and return
+           value types */
+        static constexpr auto signature
+            = const_name("(") + cast_in::arg_names + const_name(") -> ") + cast_out::name;
+        PYBIND11_DESCR_CONSTEXPR auto types = decltype(signature)::types();
+
+        /* Register the function with Python from generic (non-templated) code */
+        // Pass on the ownership over the `unique_rec` to `initialize_generic`. `rec` stays valid.
+        initialize_generic(std::move(unique_rec), signature.text, types.data(), sizeof...(Args));
+
+        /* Stash some additional information used by an important optimization in 'functional.h' */
+        using FunctionType = Return (*)(Args...);
+        constexpr bool is_function_ptr
+            = std::is_convertible<Func, FunctionType>::value && sizeof(capture) == sizeof(void *);
+        if (is_function_ptr) {
+            rec->is_stateless = true;
+            rec->data[1]
+                = const_cast<void *>(reinterpret_cast<const void *>(&typeid(FunctionType)));
+        }
+    }
+
+    // Utility class that keeps track of all duplicated strings, and cleans them up in its
+    // destructor, unless they are released. Basically a RAII-solution to deal with exceptions
+    // along the way.
+    class strdup_guard {
+    public:
+        strdup_guard() = default;
+        strdup_guard(const strdup_guard &) = delete;
+        strdup_guard &operator=(const strdup_guard &) = delete;
+
+        ~strdup_guard() {
+            for (auto *s : strings) {
+                std::free(s);
+            }
+        }
+        char *operator()(const char *s) {
+            auto *t = PYBIND11_COMPAT_STRDUP(s);
+            strings.push_back(t);
+            return t;
+        }
+        void release() { strings.clear(); }
+
+    private:
+        std::vector<char *> strings;
+    };
+
+    /// Register a function call with Python (generic non-templated code goes here)
+    void initialize_generic(unique_function_record &&unique_rec,
+                            const char *text,
+                            const std::type_info *const *types,
+                            size_t args) {
+        // Do NOT receive `unique_rec` by value. If this function fails to move out the unique_ptr,
+        // we do not want this to destruct the pointer. `initialize` (the caller) still relies on
+        // the pointee being alive after this call. Only move out if a `capsule` is going to keep
+        // it alive.
+        auto *rec = unique_rec.get();
+
+        // Keep track of strdup'ed strings, and clean them up as long as the function's capsule
+        // has not taken ownership yet (when `unique_rec.release()` is called).
+        // Note: This cannot easily be fixed by a `unique_ptr` with custom deleter, because the
+        // strings are only referenced before strdup'ing. So only *after* the following block could
+        // `destruct` safely be called, but even then, `repr` could still throw in the middle of
+        // copying all strings.
+        strdup_guard guarded_strdup;
+
+        /* Create copies of all referenced C-style strings */
+        rec->name = guarded_strdup(rec->name ? rec->name : "");
+        if (rec->doc) {
+            rec->doc = guarded_strdup(rec->doc);
+        }
+        for (auto &a : rec->args) {
+            if (a.name) {
+                a.name = guarded_strdup(a.name);
+            }
+            if (a.descr) {
+                a.descr = guarded_strdup(a.descr);
+            } else if (a.value) {
+                a.descr = guarded_strdup(repr(a.value).cast<std::string>().c_str());
+            }
+        }
+
+        rec->is_constructor = (std::strcmp(rec->name, "__init__") == 0)
+                              || (std::strcmp(rec->name, "__setstate__") == 0);
+
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES) && !defined(PYBIND11_DISABLE_NEW_STYLE_INIT_WARNING)
+        if (rec->is_constructor && !rec->is_new_style_constructor) {
+            const auto class_name
+                = detail::get_fully_qualified_tp_name((PyTypeObject *) rec->scope.ptr());
+            const auto func_name = std::string(rec->name);
+            PyErr_WarnEx(PyExc_FutureWarning,
+                         ("pybind11-bound class '" + class_name
+                          + "' is using an old-style "
+                            "placement-new '"
+                          + func_name
+                          + "' which has been deprecated. See "
+                            "the upgrade guide in pybind11's docs. This message is only visible "
+                            "when compiled in debug mode.")
+                             .c_str(),
+                         0);
+        }
+#endif
+
+        /* Generate a proper function signature */
+        std::string signature;
+        size_t type_index = 0, arg_index = 0;
+        bool is_starred = false;
+        for (const auto *pc = text; *pc != '\0'; ++pc) {
+            const auto c = *pc;
+
+            if (c == '{') {
+                // Write arg name for everything except *args and **kwargs.
+                is_starred = *(pc + 1) == '*';
+                if (is_starred) {
+                    continue;
+                }
+                // Separator for keyword-only arguments, placed before the kw
+                // arguments start (unless we are already putting an *args)
+                if (!rec->has_args && arg_index == rec->nargs_pos) {
+                    signature += "*, ";
+                }
+                if (arg_index < rec->args.size() && rec->args[arg_index].name) {
+                    signature += rec->args[arg_index].name;
+                } else if (arg_index == 0 && rec->is_method) {
+                    signature += "self";
+                } else {
+                    signature += "arg" + std::to_string(arg_index - (rec->is_method ? 1 : 0));
+                }
+                signature += ": ";
+            } else if (c == '}') {
+                // Write default value if available.
+                if (!is_starred && arg_index < rec->args.size() && rec->args[arg_index].descr) {
+                    signature += " = ";
+                    signature += detail::replace_newlines_and_squash(rec->args[arg_index].descr);
+                }
+                // Separator for positional-only arguments (placed after the
+                // argument, rather than before like *
+                if (rec->nargs_pos_only > 0 && (arg_index + 1) == rec->nargs_pos_only) {
+                    signature += ", /";
+                }
+                if (!is_starred) {
+                    arg_index++;
+                }
+            } else if (c == '%') {
+                const std::type_info *t = types[type_index++];
+                if (!t) {
+                    pybind11_fail("Internal error while parsing type signature (1)");
+                }
+                if (auto *tinfo = detail::get_type_info(*t)) {
+                    handle th((PyObject *) tinfo->type);
+                    signature += th.attr("__module__").cast<std::string>() + "."
+                                 + th.attr("__qualname__").cast<std::string>();
+                } else if (rec->is_new_style_constructor && arg_index == 0) {
+                    // A new-style `__init__` takes `self` as `value_and_holder`.
+                    // Rewrite it to the proper class type.
+                    signature += rec->scope.attr("__module__").cast<std::string>() + "."
+                                 + rec->scope.attr("__qualname__").cast<std::string>();
+                } else {
+                    signature += detail::quote_cpp_type_name(detail::clean_type_id(t->name()));
+                }
+            } else {
+                signature += c;
+            }
+        }
+
+        if (arg_index != args - rec->has_args - rec->has_kwargs || types[type_index] != nullptr) {
+            pybind11_fail("Internal error while parsing type signature (2)");
+        }
+
+        rec->signature = guarded_strdup(signature.c_str());
+        rec->args.shrink_to_fit();
+        rec->nargs = (std::uint16_t) args;
+
+        if (rec->sibling && PYBIND11_INSTANCE_METHOD_CHECK(rec->sibling.ptr())) {
+            rec->sibling = PYBIND11_INSTANCE_METHOD_GET_FUNCTION(rec->sibling.ptr());
+        }
+
+        detail::function_record *chain = nullptr, *chain_start = rec;
+        if (rec->sibling) {
+            if (PyCFunction_Check(rec->sibling.ptr())) {
+                auto *self = PyCFunction_GET_SELF(rec->sibling.ptr());
+                if (!isinstance<capsule>(self)) {
+                    chain = nullptr;
+                } else {
+                    auto rec_capsule = reinterpret_borrow<capsule>(self);
+                    if (detail::is_function_record_capsule(rec_capsule)) {
+                        chain = rec_capsule.get_pointer<detail::function_record>();
+                        /* Never append a method to an overload chain of a parent class;
+                           instead, hide the parent's overloads in this case */
+                        if (!chain->scope.is(rec->scope)) {
+                            chain = nullptr;
+                        }
+                    } else {
+                        chain = nullptr;
+                    }
+                }
+            }
+            // Don't trigger for things like the default __init__, which are wrapper_descriptors
+            // that we are intentionally replacing
+            else if (!rec->sibling.is_none() && rec->name[0] != '_') {
+                pybind11_fail("Cannot overload existing non-function object \""
+                              + std::string(rec->name) + "\" with a function of the same name");
+            }
+        }
+
+        if (!chain) {
+            /* No existing overload was found, create a new function object */
+            rec->def = new PyMethodDef();
+            std::memset(rec->def, 0, sizeof(PyMethodDef));
+            rec->def->ml_name = rec->name;
+            rec->def->ml_meth
+                = reinterpret_cast<PyCFunction>(reinterpret_cast<void (*)()>(dispatcher));
+            rec->def->ml_flags = METH_VARARGS | METH_KEYWORDS;
+
+            capsule rec_capsule(unique_rec.release(),
+                                detail::get_function_record_capsule_name(),
+                                [](void *ptr) { destruct((detail::function_record *) ptr); });
+            guarded_strdup.release();
+
+            object scope_module;
+            if (rec->scope) {
+                if (hasattr(rec->scope, "__module__")) {
+                    scope_module = rec->scope.attr("__module__");
+                } else if (hasattr(rec->scope, "__name__")) {
+                    scope_module = rec->scope.attr("__name__");
+                }
+            }
+
+            m_ptr = PyCFunction_NewEx(rec->def, rec_capsule.ptr(), scope_module.ptr());
+            if (!m_ptr) {
+                pybind11_fail("cpp_function::cpp_function(): Could not allocate function object");
+            }
+        } else {
+            /* Append at the beginning or end of the overload chain */
+            m_ptr = rec->sibling.ptr();
+            inc_ref();
+            if (chain->is_method != rec->is_method) {
+                pybind11_fail(
+                    "overloading a method with both static and instance methods is not supported; "
+#if !defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                    "#define PYBIND11_DETAILED_ERROR_MESSAGES or compile in debug mode for more "
+                    "details"
+#else
+                    "error while attempting to bind "
+                    + std::string(rec->is_method ? "instance" : "static") + " method "
+                    + std::string(pybind11::str(rec->scope.attr("__name__"))) + "."
+                    + std::string(rec->name) + signature
+#endif
+                );
+            }
+
+            if (rec->prepend) {
+                // Beginning of chain; we need to replace the capsule's current head-of-the-chain
+                // pointer with this one, then make this one point to the previous head of the
+                // chain.
+                chain_start = rec;
+                rec->next = chain;
+                auto rec_capsule
+                    = reinterpret_borrow<capsule>(((PyCFunctionObject *) m_ptr)->m_self);
+                rec_capsule.set_pointer(unique_rec.release());
+                guarded_strdup.release();
+            } else {
+                // Or end of chain (normal behavior)
+                chain_start = chain;
+                while (chain->next) {
+                    chain = chain->next;
+                }
+                chain->next = unique_rec.release();
+                guarded_strdup.release();
+            }
+        }
+
+        std::string signatures;
+        int index = 0;
+        /* Create a nice pydoc rec including all signatures and
+           docstrings of the functions in the overload chain */
+        if (chain && options::show_function_signatures()
+            && std::strcmp(rec->name, "_pybind11_conduit_v1_") != 0) {
+            // First a generic signature
+            signatures += rec->name;
+            signatures += "(*args, **kwargs)\n";
+            signatures += "Overloaded function.\n\n";
+        }
+        // Then specific overload signatures
+        bool first_user_def = true;
+        for (auto *it = chain_start; it != nullptr; it = it->next) {
+            if (options::show_function_signatures()
+                && std::strcmp(rec->name, "_pybind11_conduit_v1_") != 0) {
+                if (index > 0) {
+                    signatures += '\n';
+                }
+                if (chain) {
+                    signatures += std::to_string(++index) + ". ";
+                }
+                signatures += rec->name;
+                signatures += it->signature;
+                signatures += '\n';
+            }
+            if (it->doc && it->doc[0] != '\0' && options::show_user_defined_docstrings()) {
+                // If we're appending another docstring, and aren't printing function signatures,
+                // we need to append a newline first:
+                if (!options::show_function_signatures()) {
+                    if (first_user_def) {
+                        first_user_def = false;
+                    } else {
+                        signatures += '\n';
+                    }
+                }
+                if (options::show_function_signatures()) {
+                    signatures += '\n';
+                }
+                signatures += it->doc;
+                if (options::show_function_signatures()) {
+                    signatures += '\n';
+                }
+            }
+        }
+
+        /* Install docstring */
+        auto *func = (PyCFunctionObject *) m_ptr;
+        std::free(const_cast<char *>(func->m_ml->ml_doc));
+        // Install docstring if it's non-empty (when at least one option is enabled)
+        func->m_ml->ml_doc
+            = signatures.empty() ? nullptr : PYBIND11_COMPAT_STRDUP(signatures.c_str());
+
+        if (rec->is_method) {
+            m_ptr = PYBIND11_INSTANCE_METHOD_NEW(m_ptr, rec->scope.ptr());
+            if (!m_ptr) {
+                pybind11_fail(
+                    "cpp_function::cpp_function(): Could not allocate instance method object");
+            }
+            Py_DECREF(func);
+        }
+    }
+
+    /// When a cpp_function is GCed, release any memory allocated by pybind11
+    static void destruct(detail::function_record *rec, bool free_strings = true) {
+// If on Python 3.9, check the interpreter "MICRO" (patch) version.
+// If this is running on 3.9.0, we have to work around a bug.
+#if !defined(PYPY_VERSION) && PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 9
+        static bool is_zero = Py_GetVersion()[4] == '0';
+#endif
+
+        while (rec) {
+            detail::function_record *next = rec->next;
+            if (rec->free_data) {
+                rec->free_data(rec);
+            }
+            // During initialization, these strings might not have been copied yet,
+            // so they cannot be freed. Once the function has been created, they can.
+            // Check `make_function_record` for more details.
+            if (free_strings) {
+                std::free((char *) rec->name);
+                std::free((char *) rec->doc);
+                std::free((char *) rec->signature);
+                for (auto &arg : rec->args) {
+                    std::free(const_cast<char *>(arg.name));
+                    std::free(const_cast<char *>(arg.descr));
+                }
+            }
+            for (auto &arg : rec->args) {
+                arg.value.dec_ref();
+            }
+            if (rec->def) {
+                std::free(const_cast<char *>(rec->def->ml_doc));
+// Python 3.9.0 decref's these in the wrong order; rec->def
+// If loaded on 3.9.0, let these leak (use Python 3.9.1 at runtime to fix)
+// See https://github.com/python/cpython/pull/22670
+#if !defined(PYPY_VERSION) && PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 9
+                if (!is_zero) {
+                    delete rec->def;
+                }
+#else
+                delete rec->def;
+#endif
+            }
+            delete rec;
+            rec = next;
+        }
+    }
+
+    /// Main dispatch logic for calls to functions bound using pybind11
+    static PyObject *dispatcher(PyObject *self, PyObject *args_in, PyObject *kwargs_in) {
+        using namespace detail;
+        assert(isinstance<capsule>(self));
+
+        /* Iterator over the list of potentially admissible overloads */
+        const function_record *overloads = reinterpret_cast<function_record *>(
+                                  PyCapsule_GetPointer(self, get_function_record_capsule_name())),
+                              *current_overload = overloads;
+        assert(overloads != nullptr);
+
+        /* Need to know how many arguments + keyword arguments there are to pick the right
+           overload */
+        const auto n_args_in = (size_t) PyTuple_GET_SIZE(args_in);
+
+        handle parent = n_args_in > 0 ? PyTuple_GET_ITEM(args_in, 0) : nullptr,
+               result = PYBIND11_TRY_NEXT_OVERLOAD;
+
+        auto self_value_and_holder = value_and_holder();
+        if (overloads->is_constructor) {
+            if (!parent
+                || !PyObject_TypeCheck(parent.ptr(), (PyTypeObject *) overloads->scope.ptr())) {
+                set_error(PyExc_TypeError,
+                          "__init__(self, ...) called with invalid or missing `self` argument");
+                return nullptr;
+            }
+
+            auto *const tinfo = get_type_info((PyTypeObject *) overloads->scope.ptr());
+            auto *const pi = reinterpret_cast<instance *>(parent.ptr());
+            self_value_and_holder = pi->get_value_and_holder(tinfo, true);
+
+            // If this value is already registered it must mean __init__ is invoked multiple times;
+            // we really can't support that in C++, so just ignore the second __init__.
+            if (self_value_and_holder.instance_registered()) {
+                return none().release().ptr();
+            }
+        }
+
+        try {
+            // We do this in two passes: in the first pass, we load arguments with `convert=false`;
+            // in the second, we allow conversion (except for arguments with an explicit
+            // py::arg().noconvert()).  This lets us prefer calls without conversion, with
+            // conversion as a fallback.
+            std::vector<function_call> second_pass;
+
+            // However, if there are no overloads, we can just skip the no-convert pass entirely
+            const bool overloaded
+                = current_overload != nullptr && current_overload->next != nullptr;
+
+            for (; current_overload != nullptr; current_overload = current_overload->next) {
+
+                /* For each overload:
+                   1. Copy all positional arguments we were given, also checking to make sure that
+                      named positional arguments weren't *also* specified via kwarg.
+                   2. If we weren't given enough, try to make up the omitted ones by checking
+                      whether they were provided by a kwarg matching the `py::arg("name")` name. If
+                      so, use it (and remove it from kwargs); if not, see if the function binding
+                      provided a default that we can use.
+                   3. Ensure that either all keyword arguments were "consumed", or that the
+                   function takes a kwargs argument to accept unconsumed kwargs.
+                   4. Any positional arguments still left get put into a tuple (for args), and any
+                      leftover kwargs get put into a dict.
+                   5. Pack everything into a vector; if we have py::args or py::kwargs, they are an
+                      extra tuple or dict at the end of the positional arguments.
+                   6. Call the function call dispatcher (function_record::impl)
+
+                   If one of these fail, move on to the next overload and keep trying until we get
+                   a result other than PYBIND11_TRY_NEXT_OVERLOAD.
+                 */
+
+                const function_record &func = *current_overload;
+                size_t num_args = func.nargs; // Number of positional arguments that we need
+                if (func.has_args) {
+                    --num_args; // (but don't count py::args
+                }
+                if (func.has_kwargs) {
+                    --num_args; //  or py::kwargs)
+                }
+                size_t pos_args = func.nargs_pos;
+
+                if (!func.has_args && n_args_in > pos_args) {
+                    continue; // Too many positional arguments for this overload
+                }
+
+                if (n_args_in < pos_args && func.args.size() < pos_args) {
+                    continue; // Not enough positional arguments given, and not enough defaults to
+                              // fill in the blanks
+                }
+
+                function_call call(func, parent);
+
+                // Protect std::min with parentheses
+                size_t args_to_copy = (std::min)(pos_args, n_args_in);
+                size_t args_copied = 0;
+
+                // 0. Inject new-style `self` argument
+                if (func.is_new_style_constructor) {
+                    // The `value` may have been preallocated by an old-style `__init__`
+                    // if it was a preceding candidate for overload resolution.
+                    if (self_value_and_holder) {
+                        self_value_and_holder.type->dealloc(self_value_and_holder);
+                    }
+
+                    call.init_self = PyTuple_GET_ITEM(args_in, 0);
+                    call.args.emplace_back(reinterpret_cast<PyObject *>(&self_value_and_holder));
+                    call.args_convert.push_back(false);
+                    ++args_copied;
+                }
+
+                // 1. Copy any position arguments given.
+                bool bad_arg = false;
+                for (; args_copied < args_to_copy; ++args_copied) {
+                    const argument_record *arg_rec
+                        = args_copied < func.args.size() ? &func.args[args_copied] : nullptr;
+                    if (kwargs_in && arg_rec && arg_rec->name
+                        && dict_getitemstring(kwargs_in, arg_rec->name)) {
+                        bad_arg = true;
+                        break;
+                    }
+
+                    handle arg(PyTuple_GET_ITEM(args_in, args_copied));
+                    if (arg_rec && !arg_rec->none && arg.is_none()) {
+                        bad_arg = true;
+                        break;
+                    }
+                    call.args.push_back(arg);
+                    call.args_convert.push_back(arg_rec ? arg_rec->convert : true);
+                }
+                if (bad_arg) {
+                    continue; // Maybe it was meant for another overload (issue #688)
+                }
+
+                // Keep track of how many position args we copied out in case we need to come back
+                // to copy the rest into a py::args argument.
+                size_t positional_args_copied = args_copied;
+
+                // We'll need to copy this if we steal some kwargs for defaults
+                dict kwargs = reinterpret_borrow<dict>(kwargs_in);
+
+                // 1.5. Fill in any missing pos_only args from defaults if they exist
+                if (args_copied < func.nargs_pos_only) {
+                    for (; args_copied < func.nargs_pos_only; ++args_copied) {
+                        const auto &arg_rec = func.args[args_copied];
+                        handle value;
+
+                        if (arg_rec.value) {
+                            value = arg_rec.value;
+                        }
+                        if (value) {
+                            call.args.push_back(value);
+                            call.args_convert.push_back(arg_rec.convert);
+                        } else {
+                            break;
+                        }
+                    }
+
+                    if (args_copied < func.nargs_pos_only) {
+                        continue; // Not enough defaults to fill the positional arguments
+                    }
+                }
+
+                // 2. Check kwargs and, failing that, defaults that may help complete the list
+                if (args_copied < num_args) {
+                    bool copied_kwargs = false;
+
+                    for (; args_copied < num_args; ++args_copied) {
+                        const auto &arg_rec = func.args[args_copied];
+
+                        handle value;
+                        if (kwargs_in && arg_rec.name) {
+                            value = dict_getitemstring(kwargs.ptr(), arg_rec.name);
+                        }
+
+                        if (value) {
+                            // Consume a kwargs value
+                            if (!copied_kwargs) {
+                                kwargs = reinterpret_steal<dict>(PyDict_Copy(kwargs.ptr()));
+                                copied_kwargs = true;
+                            }
+                            if (PyDict_DelItemString(kwargs.ptr(), arg_rec.name) == -1) {
+                                throw error_already_set();
+                            }
+                        } else if (arg_rec.value) {
+                            value = arg_rec.value;
+                        }
+
+                        if (!arg_rec.none && value.is_none()) {
+                            break;
+                        }
+
+                        if (value) {
+                            // If we're at the py::args index then first insert a stub for it to be
+                            // replaced later
+                            if (func.has_args && call.args.size() == func.nargs_pos) {
+                                call.args.push_back(none());
+                            }
+
+                            call.args.push_back(value);
+                            call.args_convert.push_back(arg_rec.convert);
+                        } else {
+                            break;
+                        }
+                    }
+
+                    if (args_copied < num_args) {
+                        continue; // Not enough arguments, defaults, or kwargs to fill the
+                                  // positional arguments
+                    }
+                }
+
+                // 3. Check everything was consumed (unless we have a kwargs arg)
+                if (kwargs && !kwargs.empty() && !func.has_kwargs) {
+                    continue; // Unconsumed kwargs, but no py::kwargs argument to accept them
+                }
+
+                // 4a. If we have a py::args argument, create a new tuple with leftovers
+                if (func.has_args) {
+                    tuple extra_args;
+                    if (args_to_copy == 0) {
+                        // We didn't copy out any position arguments from the args_in tuple, so we
+                        // can reuse it directly without copying:
+                        extra_args = reinterpret_borrow<tuple>(args_in);
+                    } else if (positional_args_copied >= n_args_in) {
+                        extra_args = tuple(0);
+                    } else {
+                        size_t args_size = n_args_in - positional_args_copied;
+                        extra_args = tuple(args_size);
+                        for (size_t i = 0; i < args_size; ++i) {
+                            extra_args[i] = PyTuple_GET_ITEM(args_in, positional_args_copied + i);
+                        }
+                    }
+                    if (call.args.size() <= func.nargs_pos) {
+                        call.args.push_back(extra_args);
+                    } else {
+                        call.args[func.nargs_pos] = extra_args;
+                    }
+                    call.args_convert.push_back(false);
+                    call.args_ref = std::move(extra_args);
+                }
+
+                // 4b. If we have a py::kwargs, pass on any remaining kwargs
+                if (func.has_kwargs) {
+                    if (!kwargs.ptr()) {
+                        kwargs = dict(); // If we didn't get one, send an empty one
+                    }
+                    call.args.push_back(kwargs);
+                    call.args_convert.push_back(false);
+                    call.kwargs_ref = std::move(kwargs);
+                }
+
+// 5. Put everything in a vector.  Not technically step 5, we've been building it
+// in `call.args` all along.
+#if defined(PYBIND11_DETAILED_ERROR_MESSAGES)
+                if (call.args.size() != func.nargs || call.args_convert.size() != func.nargs) {
+                    pybind11_fail("Internal error: function call dispatcher inserted wrong number "
+                                  "of arguments!");
+                }
+#endif
+
+                std::vector<bool> second_pass_convert;
+                if (overloaded) {
+                    // We're in the first no-convert pass, so swap out the conversion flags for a
+                    // set of all-false flags.  If the call fails, we'll swap the flags back in for
+                    // the conversion-allowed call below.
+                    second_pass_convert.resize(func.nargs, false);
+                    call.args_convert.swap(second_pass_convert);
+                }
+
+                // 6. Call the function.
+                try {
+                    loader_life_support guard{};
+                    result = func.impl(call);
+                } catch (reference_cast_error &) {
+                    result = PYBIND11_TRY_NEXT_OVERLOAD;
+                }
+
+                if (result.ptr() != PYBIND11_TRY_NEXT_OVERLOAD) {
+                    break;
+                }
+
+                if (overloaded) {
+                    // The (overloaded) call failed; if the call has at least one argument that
+                    // permits conversion (i.e. it hasn't been explicitly specified `.noconvert()`)
+                    // then add this call to the list of second pass overloads to try.
+                    for (size_t i = func.is_method ? 1 : 0; i < pos_args; i++) {
+                        if (second_pass_convert[i]) {
+                            // Found one: swap the converting flags back in and store the call for
+                            // the second pass.
+                            call.args_convert.swap(second_pass_convert);
+                            second_pass.push_back(std::move(call));
+                            break;
+                        }
+                    }
+                }
+            }
+
+            if (overloaded && !second_pass.empty() && result.ptr() == PYBIND11_TRY_NEXT_OVERLOAD) {
+                // The no-conversion pass finished without success, try again with conversion
+                // allowed
+                for (auto &call : second_pass) {
+                    try {
+                        loader_life_support guard{};
+                        result = call.func.impl(call);
+                    } catch (reference_cast_error &) {
+                        result = PYBIND11_TRY_NEXT_OVERLOAD;
+                    }
+
+                    if (result.ptr() != PYBIND11_TRY_NEXT_OVERLOAD) {
+                        // The error reporting logic below expects 'current_overload' to be valid,
+                        // as it would be if we'd encountered this failure in the first-pass loop.
+                        if (!result) {
+                            current_overload = &call.func;
+                        }
+                        break;
+                    }
+                }
+            }
+        } catch (error_already_set &e) {
+            e.restore();
+            return nullptr;
+#ifdef __GLIBCXX__
+        } catch (abi::__forced_unwind &) {
+            throw;
+#endif
+        } catch (...) {
+            try_translate_exceptions();
+            return nullptr;
+        }
+
+        auto append_note_if_missing_header_is_suspected = [](std::string &msg) {
+            if (msg.find("std::") != std::string::npos) {
+                msg += "\n\n"
+                       "Did you forget to `#include <pybind11/stl.h>`? Or <pybind11/complex.h>,\n"
+                       "<pybind11/functional.h>, <pybind11/chrono.h>, etc. Some automatic\n"
+                       "conversions are optional and require extra headers to be included\n"
+                       "when compiling your pybind11 module.";
+            }
+        };
+
+        if (result.ptr() == PYBIND11_TRY_NEXT_OVERLOAD) {
+            if (overloads->is_operator) {
+                return handle(Py_NotImplemented).inc_ref().ptr();
+            }
+
+            std::string msg = std::string(overloads->name) + "(): incompatible "
+                              + std::string(overloads->is_constructor ? "constructor" : "function")
+                              + " arguments. The following argument types are supported:\n";
+
+            int ctr = 0;
+            for (const function_record *it2 = overloads; it2 != nullptr; it2 = it2->next) {
+                msg += "    " + std::to_string(++ctr) + ". ";
+
+                bool wrote_sig = false;
+                if (overloads->is_constructor) {
+                    // For a constructor, rewrite `(self: Object, arg0, ...) -> NoneType` as
+                    // `Object(arg0, ...)`
+                    std::string sig = it2->signature;
+                    size_t start = sig.find('(') + 7; // skip "(self: "
+                    if (start < sig.size()) {
+                        // End at the , for the next argument
+                        size_t end = sig.find(", "), next = end + 2;
+                        size_t ret = sig.rfind(" -> ");
+                        // Or the ), if there is no comma:
+                        if (end >= sig.size()) {
+                            next = end = sig.find(')');
+                        }
+                        if (start < end && next < sig.size()) {
+                            msg.append(sig, start, end - start);
+                            msg += '(';
+                            msg.append(sig, next, ret - next);
+                            wrote_sig = true;
+                        }
+                    }
+                }
+                if (!wrote_sig) {
+                    msg += it2->signature;
+                }
+
+                msg += '\n';
+            }
+            msg += "\nInvoked with: ";
+            auto args_ = reinterpret_borrow<tuple>(args_in);
+            bool some_args = false;
+            for (size_t ti = overloads->is_constructor ? 1 : 0; ti < args_.size(); ++ti) {
+                if (!some_args) {
+                    some_args = true;
+                } else {
+                    msg += ", ";
+                }
+                try {
+                    msg += pybind11::repr(args_[ti]);
+                } catch (const error_already_set &) {
+                    msg += "<repr raised Error>";
+                }
+            }
+            if (kwargs_in) {
+                auto kwargs = reinterpret_borrow<dict>(kwargs_in);
+                if (!kwargs.empty()) {
+                    if (some_args) {
+                        msg += "; ";
+                    }
+                    msg += "kwargs: ";
+                    bool first = true;
+                    for (const auto &kwarg : kwargs) {
+                        if (first) {
+                            first = false;
+                        } else {
+                            msg += ", ";
+                        }
+                        msg += pybind11::str("{}=").format(kwarg.first);
+                        try {
+                            msg += pybind11::repr(kwarg.second);
+                        } catch (const error_already_set &) {
+                            msg += "<repr raised Error>";
+                        }
+                    }
+                }
+            }
+
+            append_note_if_missing_header_is_suspected(msg);
+            // Attach additional error info to the exception if supported
+            if (PyErr_Occurred()) {
+                // #HelpAppreciated: unit test coverage for this branch.
+                raise_from(PyExc_TypeError, msg.c_str());
+                return nullptr;
+            }
+            set_error(PyExc_TypeError, msg.c_str());
+            return nullptr;
+        }
+        if (!result) {
+            std::string msg = "Unable to convert function return value to a "
+                              "Python type! The signature was\n\t";
+            assert(current_overload != nullptr);
+            msg += current_overload->signature;
+            append_note_if_missing_header_is_suspected(msg);
+            // Attach additional error info to the exception if supported
+            if (PyErr_Occurred()) {
+                raise_from(PyExc_TypeError, msg.c_str());
+                return nullptr;
+            }
+            set_error(PyExc_TypeError, msg.c_str());
+            return nullptr;
+        }
+        if (overloads->is_constructor && !self_value_and_holder.holder_constructed()) {
+            auto *pi = reinterpret_cast<instance *>(parent.ptr());
+            self_value_and_holder.type->init_instance(pi, nullptr);
+        }
+        return result.ptr();
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+struct handle_type_name<cpp_function> {
+    static constexpr auto name = const_name("Callable");
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// Use to activate Py_MOD_GIL_NOT_USED.
+class mod_gil_not_used {
+public:
+    explicit mod_gil_not_used(bool flag = true) : flag_(flag) {}
+    bool flag() const { return flag_; }
+
+private:
+    bool flag_;
+};
+
+/// Wrapper for Python extension modules
+class module_ : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(module_, object, PyModule_Check)
+
+    /// Create a new top-level Python module with the given name and docstring
+    PYBIND11_DEPRECATED("Use PYBIND11_MODULE or module_::create_extension_module instead")
+    explicit module_(const char *name, const char *doc = nullptr) {
+        *this = create_extension_module(name, doc, new PyModuleDef());
+    }
+
+    /** \rst
+        Create Python binding for a new function within the module scope. ``Func``
+        can be a plain C++ function, a function pointer, or a lambda function. For
+        details on the ``Extra&& ... extra`` argument, see section :ref:`extras`.
+    \endrst */
+    template <typename Func, typename... Extra>
+    module_ &def(const char *name_, Func &&f, const Extra &...extra) {
+        cpp_function func(std::forward<Func>(f),
+                          name(name_),
+                          scope(*this),
+                          sibling(getattr(*this, name_, none())),
+                          extra...);
+        // NB: allow overwriting here because cpp_function sets up a chain with the intention of
+        // overwriting (and has already checked internally that it isn't overwriting
+        // non-functions).
+        add_object(name_, func, true /* overwrite */);
+        return *this;
+    }
+
+    /** \rst
+        Create and return a new Python submodule with the given name and docstring.
+        This also works recursively, i.e.
+
+        .. code-block:: cpp
+
+            py::module_ m("example", "pybind11 example plugin");
+            py::module_ m2 = m.def_submodule("sub", "A submodule of 'example'");
+            py::module_ m3 = m2.def_submodule("subsub", "A submodule of 'example.sub'");
+    \endrst */
+    module_ def_submodule(const char *name, const char *doc = nullptr) {
+        const char *this_name = PyModule_GetName(m_ptr);
+        if (this_name == nullptr) {
+            throw error_already_set();
+        }
+        std::string full_name = std::string(this_name) + '.' + name;
+        handle submodule = PyImport_AddModule(full_name.c_str());
+        if (!submodule) {
+            throw error_already_set();
+        }
+        auto result = reinterpret_borrow<module_>(submodule);
+        if (doc && options::show_user_defined_docstrings()) {
+            result.attr("__doc__") = pybind11::str(doc);
+        }
+        attr(name) = result;
+        return result;
+    }
+
+    /// Import and return a module or throws `error_already_set`.
+    static module_ import(const char *name) {
+        PyObject *obj = PyImport_ImportModule(name);
+        if (!obj) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<module_>(obj);
+    }
+
+    /// Reload the module or throws `error_already_set`.
+    void reload() {
+        PyObject *obj = PyImport_ReloadModule(ptr());
+        if (!obj) {
+            throw error_already_set();
+        }
+        *this = reinterpret_steal<module_>(obj);
+    }
+
+    /** \rst
+        Adds an object to the module using the given name.  Throws if an object with the given name
+        already exists.
+
+        ``overwrite`` should almost always be false: attempting to overwrite objects that pybind11
+        has established will, in most cases, break things.
+    \endrst */
+    PYBIND11_NOINLINE void add_object(const char *name, handle obj, bool overwrite = false) {
+        if (!overwrite && hasattr(*this, name)) {
+            pybind11_fail(
+                "Error during initialization: multiple incompatible definitions with name \""
+                + std::string(name) + "\"");
+        }
+
+        PyModule_AddObject(ptr(), name, obj.inc_ref().ptr() /* steals a reference */);
+    }
+
+    using module_def = PyModuleDef; // TODO: Can this be removed (it was needed only for Python 2)?
+
+    /** \rst
+        Create a new top-level module that can be used as the main module of a C extension.
+
+        ``def`` should point to a statically allocated module_def.
+    \endrst */
+    static module_ create_extension_module(const char *name,
+                                           const char *doc,
+                                           module_def *def,
+                                           mod_gil_not_used gil_not_used
+                                           = mod_gil_not_used(false)) {
+        // module_def is PyModuleDef
+        // Placement new (not an allocation).
+        def = new (def)
+            PyModuleDef{/* m_base */ PyModuleDef_HEAD_INIT,
+                        /* m_name */ name,
+                        /* m_doc */ options::show_user_defined_docstrings() ? doc : nullptr,
+                        /* m_size */ -1,
+                        /* m_methods */ nullptr,
+                        /* m_slots */ nullptr,
+                        /* m_traverse */ nullptr,
+                        /* m_clear */ nullptr,
+                        /* m_free */ nullptr};
+        auto *m = PyModule_Create(def);
+        if (m == nullptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Internal error in module_::create_extension_module()");
+        }
+        if (gil_not_used.flag()) {
+#ifdef Py_GIL_DISABLED
+            PyUnstable_Module_SetGIL(m, Py_MOD_GIL_NOT_USED);
+#endif
+        }
+        // TODO: Should be reinterpret_steal for Python 3, but Python also steals it again when
+        //       returned from PyInit_...
+        //       For Python 2, reinterpret_borrow was correct.
+        return reinterpret_borrow<module_>(m);
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+struct handle_type_name<module_> {
+    static constexpr auto name = const_name("module");
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+// When inside a namespace (or anywhere as long as it's not the first item on a line),
+// C++20 allows "module" to be used. This is provided for backward compatibility, and for
+// simplicity, if someone wants to use py::module for example, that is perfectly safe.
+using module = module_;
+
+/// \ingroup python_builtins
+/// Return a dictionary representing the global variables in the current execution frame,
+/// or ``__main__.__dict__`` if there is no frame (usually when the interpreter is embedded).
+inline dict globals() {
+#if PY_VERSION_HEX >= 0x030d0000
+    PyObject *p = PyEval_GetFrameGlobals();
+    return p ? reinterpret_steal<dict>(p)
+             : reinterpret_borrow<dict>(module_::import("__main__").attr("__dict__").ptr());
+#else
+    PyObject *p = PyEval_GetGlobals();
+    return reinterpret_borrow<dict>(p ? p : module_::import("__main__").attr("__dict__").ptr());
+#endif
+}
+
+template <typename... Args, typename = detail::enable_if_t<args_are_all_keyword_or_ds<Args...>()>>
+PYBIND11_DEPRECATED("make_simple_namespace should be replaced with "
+                    "py::module_::import(\"types\").attr(\"SimpleNamespace\") ")
+object make_simple_namespace(Args &&...args_) {
+    return module_::import("types").attr("SimpleNamespace")(std::forward<Args>(args_)...);
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+/// Generic support for creating new Python heap types
+class generic_type : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(generic_type, object, PyType_Check)
+protected:
+    void initialize(const type_record &rec) {
+        if (rec.scope && hasattr(rec.scope, "__dict__")
+            && rec.scope.attr("__dict__").contains(rec.name)) {
+            pybind11_fail("generic_type: cannot initialize type \"" + std::string(rec.name)
+                          + "\": an object with that name is already defined");
+        }
+
+        if ((rec.module_local ? get_local_type_info(*rec.type) : get_global_type_info(*rec.type))
+            != nullptr) {
+            pybind11_fail("generic_type: type \"" + std::string(rec.name)
+                          + "\" is already registered!");
+        }
+
+        m_ptr = make_new_python_type(rec);
+
+        /* Register supplemental type information in C++ dict */
+        auto *tinfo = new detail::type_info();
+        tinfo->type = (PyTypeObject *) m_ptr;
+        tinfo->cpptype = rec.type;
+        tinfo->type_size = rec.type_size;
+        tinfo->type_align = rec.type_align;
+        tinfo->operator_new = rec.operator_new;
+        tinfo->holder_size_in_ptrs = size_in_ptrs(rec.holder_size);
+        tinfo->init_instance = rec.init_instance;
+        tinfo->dealloc = rec.dealloc;
+        tinfo->simple_type = true;
+        tinfo->simple_ancestors = true;
+        tinfo->default_holder = rec.default_holder;
+        tinfo->module_local = rec.module_local;
+
+        with_internals([&](internals &internals) {
+            auto tindex = std::type_index(*rec.type);
+            tinfo->direct_conversions = &internals.direct_conversions[tindex];
+            if (rec.module_local) {
+                get_local_internals().registered_types_cpp[tindex] = tinfo;
+            } else {
+                internals.registered_types_cpp[tindex] = tinfo;
+            }
+            internals.registered_types_py[(PyTypeObject *) m_ptr] = {tinfo};
+        });
+
+        if (rec.bases.size() > 1 || rec.multiple_inheritance) {
+            mark_parents_nonsimple(tinfo->type);
+            tinfo->simple_ancestors = false;
+        } else if (rec.bases.size() == 1) {
+            auto *parent_tinfo = get_type_info((PyTypeObject *) rec.bases[0].ptr());
+            assert(parent_tinfo != nullptr);
+            bool parent_simple_ancestors = parent_tinfo->simple_ancestors;
+            tinfo->simple_ancestors = parent_simple_ancestors;
+            // The parent can no longer be a simple type if it has MI and has a child
+            parent_tinfo->simple_type = parent_tinfo->simple_type && parent_simple_ancestors;
+        }
+
+        if (rec.module_local) {
+            // Stash the local typeinfo and loader so that external modules can access it.
+            tinfo->module_local_load = &type_caster_generic::local_load;
+            setattr(m_ptr, PYBIND11_MODULE_LOCAL_ID, capsule(tinfo));
+        }
+    }
+
+    /// Helper function which tags all parents of a type using mult. inheritance
+    void mark_parents_nonsimple(PyTypeObject *value) {
+        auto t = reinterpret_borrow<tuple>(value->tp_bases);
+        for (handle h : t) {
+            auto *tinfo2 = get_type_info((PyTypeObject *) h.ptr());
+            if (tinfo2) {
+                tinfo2->simple_type = false;
+            }
+            mark_parents_nonsimple((PyTypeObject *) h.ptr());
+        }
+    }
+
+    void install_buffer_funcs(buffer_info *(*get_buffer)(PyObject *, void *),
+                              void *get_buffer_data) {
+        auto *type = (PyHeapTypeObject *) m_ptr;
+        auto *tinfo = detail::get_type_info(&type->ht_type);
+
+        if (!type->ht_type.tp_as_buffer) {
+            pybind11_fail("To be able to register buffer protocol support for the type '"
+                          + get_fully_qualified_tp_name(tinfo->type)
+                          + "' the associated class<>(..) invocation must "
+                            "include the pybind11::buffer_protocol() annotation!");
+        }
+
+        tinfo->get_buffer = get_buffer;
+        tinfo->get_buffer_data = get_buffer_data;
+    }
+
+    // rec_func must be set for either fget or fset.
+    void def_property_static_impl(const char *name,
+                                  handle fget,
+                                  handle fset,
+                                  detail::function_record *rec_func) {
+        const auto is_static = (rec_func != nullptr) && !(rec_func->is_method && rec_func->scope);
+        const auto has_doc = (rec_func != nullptr) && (rec_func->doc != nullptr)
+                             && pybind11::options::show_user_defined_docstrings();
+        auto property = handle(
+            (PyObject *) (is_static ? get_internals().static_property_type : &PyProperty_Type));
+        attr(name) = property(fget.ptr() ? fget : none(),
+                              fset.ptr() ? fset : none(),
+                              /*deleter*/ none(),
+                              pybind11::str(has_doc ? rec_func->doc : ""));
+    }
+};
+
+/// Set the pointer to operator new if it exists. The cast is needed because it can be overloaded.
+template <typename T,
+          typename = void_t<decltype(static_cast<void *(*) (size_t)>(T::operator new))>>
+void set_operator_new(type_record *r) {
+    r->operator_new = &T::operator new;
+}
+
+template <typename>
+void set_operator_new(...) {}
+
+template <typename T, typename SFINAE = void>
+struct has_operator_delete : std::false_type {};
+template <typename T>
+struct has_operator_delete<T, void_t<decltype(static_cast<void (*)(void *)>(T::operator delete))>>
+    : std::true_type {};
+template <typename T, typename SFINAE = void>
+struct has_operator_delete_size : std::false_type {};
+template <typename T>
+struct has_operator_delete_size<
+    T,
+    void_t<decltype(static_cast<void (*)(void *, size_t)>(T::operator delete))>> : std::true_type {
+};
+/// Call class-specific delete if it exists or global otherwise. Can also be an overload set.
+template <typename T, enable_if_t<has_operator_delete<T>::value, int> = 0>
+void call_operator_delete(T *p, size_t, size_t) {
+    T::operator delete(p);
+}
+template <typename T,
+          enable_if_t<!has_operator_delete<T>::value && has_operator_delete_size<T>::value, int>
+          = 0>
+void call_operator_delete(T *p, size_t s, size_t) {
+    T::operator delete(p, s);
+}
+
+inline void call_operator_delete(void *p, size_t s, size_t a) {
+    (void) s;
+    (void) a;
+#if defined(__cpp_aligned_new) && (!defined(_MSC_VER) || _MSC_VER >= 1912)
+    if (a > __STDCPP_DEFAULT_NEW_ALIGNMENT__) {
+#    ifdef __cpp_sized_deallocation
+        ::operator delete(p, s, std::align_val_t(a));
+#    else
+        ::operator delete(p, std::align_val_t(a));
+#    endif
+        return;
+    }
+#endif
+#ifdef __cpp_sized_deallocation
+    ::operator delete(p, s);
+#else
+    ::operator delete(p);
+#endif
+}
+
+inline void add_class_method(object &cls, const char *name_, const cpp_function &cf) {
+    cls.attr(cf.name()) = cf;
+    if (std::strcmp(name_, "__eq__") == 0 && !cls.attr("__dict__").contains("__hash__")) {
+        cls.attr("__hash__") = none();
+    }
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Given a pointer to a member function, cast it to its `Derived` version.
+/// Forward everything else unchanged.
+template <typename /*Derived*/, typename F>
+auto method_adaptor(F &&f) -> decltype(std::forward<F>(f)) {
+    return std::forward<F>(f);
+}
+
+template <typename Derived, typename Return, typename Class, typename... Args>
+auto method_adaptor(Return (Class::*pmf)(Args...)) -> Return (Derived::*)(Args...) {
+    static_assert(
+        detail::is_accessible_base_of<Class, Derived>::value,
+        "Cannot bind an inaccessible base class method; use a lambda definition instead");
+    return pmf;
+}
+
+template <typename Derived, typename Return, typename Class, typename... Args>
+auto method_adaptor(Return (Class::*pmf)(Args...) const) -> Return (Derived::*)(Args...) const {
+    static_assert(
+        detail::is_accessible_base_of<Class, Derived>::value,
+        "Cannot bind an inaccessible base class method; use a lambda definition instead");
+    return pmf;
+}
+
+template <typename type_, typename... options>
+class class_ : public detail::generic_type {
+    template <typename T>
+    using is_holder = detail::is_holder_type<type_, T>;
+    template <typename T>
+    using is_subtype = detail::is_strict_base_of<type_, T>;
+    template <typename T>
+    using is_base = detail::is_strict_base_of<T, type_>;
+    // struct instead of using here to help MSVC:
+    template <typename T>
+    struct is_valid_class_option : detail::any_of<is_holder<T>, is_subtype<T>, is_base<T>> {};
+
+public:
+    using type = type_;
+    using type_alias = detail::exactly_one_t<is_subtype, void, options...>;
+    constexpr static bool has_alias = !std::is_void<type_alias>::value;
+    using holder_type = detail::exactly_one_t<is_holder, std::unique_ptr<type>, options...>;
+
+    static_assert(detail::all_of<is_valid_class_option<options>...>::value,
+                  "Unknown/invalid class_ template parameters provided");
+
+    static_assert(!has_alias || std::is_polymorphic<type>::value,
+                  "Cannot use an alias class with a non-polymorphic type");
+
+    PYBIND11_OBJECT(class_, generic_type, PyType_Check)
+
+    template <typename... Extra>
+    class_(handle scope, const char *name, const Extra &...extra) {
+        using namespace detail;
+
+        // MI can only be specified via class_ template options, not constructor parameters
+        static_assert(
+            none_of<is_pyobject<Extra>...>::value || // no base class arguments, or:
+                (constexpr_sum(is_pyobject<Extra>::value...) == 1 && // Exactly one base
+                 constexpr_sum(is_base<options>::value...) == 0 &&   // no template option bases
+                 // no multiple_inheritance attr
+                 none_of<std::is_same<multiple_inheritance, Extra>...>::value),
+            "Error: multiple inheritance bases must be specified via class_ template options");
+
+        type_record record;
+        record.scope = scope;
+        record.name = name;
+        record.type = &typeid(type);
+        record.type_size = sizeof(conditional_t<has_alias, type_alias, type>);
+        record.type_align = alignof(conditional_t<has_alias, type_alias, type> &);
+        record.holder_size = sizeof(holder_type);
+        record.init_instance = init_instance;
+        record.dealloc = dealloc;
+        record.default_holder = detail::is_instantiation<std::unique_ptr, holder_type>::value;
+
+        set_operator_new<type>(&record);
+
+        /* Register base classes specified via template arguments to class_, if any */
+        PYBIND11_EXPAND_SIDE_EFFECTS(add_base<options>(record));
+
+        /* Process optional arguments, if any */
+        process_attributes<Extra...>::init(extra..., &record);
+
+        generic_type::initialize(record);
+
+        if (has_alias) {
+            with_internals([&](internals &internals) {
+                auto &instances = record.module_local ? get_local_internals().registered_types_cpp
+                                                      : internals.registered_types_cpp;
+                instances[std::type_index(typeid(type_alias))]
+                    = instances[std::type_index(typeid(type))];
+            });
+        }
+        def("_pybind11_conduit_v1_", cpp_conduit_method);
+    }
+
+    template <typename Base, detail::enable_if_t<is_base<Base>::value, int> = 0>
+    static void add_base(detail::type_record &rec) {
+        rec.add_base(typeid(Base), [](void *src) -> void * {
+            return static_cast<Base *>(reinterpret_cast<type *>(src));
+        });
+    }
+
+    template <typename Base, detail::enable_if_t<!is_base<Base>::value, int> = 0>
+    static void add_base(detail::type_record &) {}
+
+    template <typename Func, typename... Extra>
+    class_ &def(const char *name_, Func &&f, const Extra &...extra) {
+        cpp_function cf(method_adaptor<type>(std::forward<Func>(f)),
+                        name(name_),
+                        is_method(*this),
+                        sibling(getattr(*this, name_, none())),
+                        extra...);
+        add_class_method(*this, name_, cf);
+        return *this;
+    }
+
+    template <typename Func, typename... Extra>
+    class_ &def_static(const char *name_, Func &&f, const Extra &...extra) {
+        static_assert(!std::is_member_function_pointer<Func>::value,
+                      "def_static(...) called with a non-static member function pointer");
+        cpp_function cf(std::forward<Func>(f),
+                        name(name_),
+                        scope(*this),
+                        sibling(getattr(*this, name_, none())),
+                        extra...);
+        auto cf_name = cf.name();
+        attr(std::move(cf_name)) = staticmethod(std::move(cf));
+        return *this;
+    }
+
+    template <typename T, typename... Extra, detail::enable_if_t<T::op_enable_if_hook, int> = 0>
+    class_ &def(const T &op, const Extra &...extra) {
+        op.execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename T, typename... Extra, detail::enable_if_t<T::op_enable_if_hook, int> = 0>
+    class_ &def_cast(const T &op, const Extra &...extra) {
+        op.execute_cast(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(const detail::initimpl::constructor<Args...> &init, const Extra &...extra) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(init);
+        init.execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(const detail::initimpl::alias_constructor<Args...> &init, const Extra &...extra) {
+        PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(init);
+        init.execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(detail::initimpl::factory<Args...> &&init, const Extra &...extra) {
+        std::move(init).execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename... Args, typename... Extra>
+    class_ &def(detail::initimpl::pickle_factory<Args...> &&pf, const Extra &...extra) {
+        std::move(pf).execute(*this, extra...);
+        return *this;
+    }
+
+    template <typename Func>
+    class_ &def_buffer(Func &&func) {
+        struct capture {
+            Func func;
+        };
+        auto *ptr = new capture{std::forward<Func>(func)};
+        install_buffer_funcs(
+            [](PyObject *obj, void *ptr) -> buffer_info * {
+                detail::make_caster<type> caster;
+                if (!caster.load(obj, false)) {
+                    return nullptr;
+                }
+                return new buffer_info(((capture *) ptr)->func(std::move(caster)));
+            },
+            ptr);
+        weakref(m_ptr, cpp_function([ptr](handle wr) {
+                    delete ptr;
+                    wr.dec_ref();
+                }))
+            .release();
+        return *this;
+    }
+
+    template <typename Return, typename Class, typename... Args>
+    class_ &def_buffer(Return (Class::*func)(Args...)) {
+        return def_buffer([func](type &obj) { return (obj.*func)(); });
+    }
+
+    template <typename Return, typename Class, typename... Args>
+    class_ &def_buffer(Return (Class::*func)(Args...) const) {
+        return def_buffer([func](const type &obj) { return (obj.*func)(); });
+    }
+
+    template <typename C, typename D, typename... Extra>
+    class_ &def_readwrite(const char *name, D C::*pm, const Extra &...extra) {
+        static_assert(std::is_same<C, type>::value || std::is_base_of<C, type>::value,
+                      "def_readwrite() requires a class member (or base class member)");
+        cpp_function fget([pm](const type &c) -> const D & { return c.*pm; }, is_method(*this)),
+            fset([pm](type &c, const D &value) { c.*pm = value; }, is_method(*this));
+        def_property(name, fget, fset, return_value_policy::reference_internal, extra...);
+        return *this;
+    }
+
+    template <typename C, typename D, typename... Extra>
+    class_ &def_readonly(const char *name, const D C::*pm, const Extra &...extra) {
+        static_assert(std::is_same<C, type>::value || std::is_base_of<C, type>::value,
+                      "def_readonly() requires a class member (or base class member)");
+        cpp_function fget([pm](const type &c) -> const D & { return c.*pm; }, is_method(*this));
+        def_property_readonly(name, fget, return_value_policy::reference_internal, extra...);
+        return *this;
+    }
+
+    template <typename D, typename... Extra>
+    class_ &def_readwrite_static(const char *name, D *pm, const Extra &...extra) {
+        cpp_function fget([pm](const object &) -> const D & { return *pm; }, scope(*this)),
+            fset([pm](const object &, const D &value) { *pm = value; }, scope(*this));
+        def_property_static(name, fget, fset, return_value_policy::reference, extra...);
+        return *this;
+    }
+
+    template <typename D, typename... Extra>
+    class_ &def_readonly_static(const char *name, const D *pm, const Extra &...extra) {
+        cpp_function fget([pm](const object &) -> const D & { return *pm; }, scope(*this));
+        def_property_readonly_static(name, fget, return_value_policy::reference, extra...);
+        return *this;
+    }
+
+    /// Uses return_value_policy::reference_internal by default
+    template <typename Getter, typename... Extra>
+    class_ &def_property_readonly(const char *name, const Getter &fget, const Extra &...extra) {
+        return def_property_readonly(name,
+                                     cpp_function(method_adaptor<type>(fget)),
+                                     return_value_policy::reference_internal,
+                                     extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &
+    def_property_readonly(const char *name, const cpp_function &fget, const Extra &...extra) {
+        return def_property(name, fget, nullptr, extra...);
+    }
+
+    /// Uses return_value_policy::reference by default
+    template <typename Getter, typename... Extra>
+    class_ &
+    def_property_readonly_static(const char *name, const Getter &fget, const Extra &...extra) {
+        return def_property_readonly_static(
+            name, cpp_function(fget), return_value_policy::reference, extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property_readonly_static(const char *name,
+                                         const cpp_function &fget,
+                                         const Extra &...extra) {
+        return def_property_static(name, fget, nullptr, extra...);
+    }
+
+    /// Uses return_value_policy::reference_internal by default
+    template <typename Getter, typename Setter, typename... Extra>
+    class_ &
+    def_property(const char *name, const Getter &fget, const Setter &fset, const Extra &...extra) {
+        return def_property(
+            name, fget, cpp_function(method_adaptor<type>(fset), is_setter()), extra...);
+    }
+    template <typename Getter, typename... Extra>
+    class_ &def_property(const char *name,
+                         const Getter &fget,
+                         const cpp_function &fset,
+                         const Extra &...extra) {
+        return def_property(name,
+                            cpp_function(method_adaptor<type>(fget)),
+                            fset,
+                            return_value_policy::reference_internal,
+                            extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property(const char *name,
+                         const cpp_function &fget,
+                         const cpp_function &fset,
+                         const Extra &...extra) {
+        return def_property_static(name, fget, fset, is_method(*this), extra...);
+    }
+
+    /// Uses return_value_policy::reference by default
+    template <typename Getter, typename... Extra>
+    class_ &def_property_static(const char *name,
+                                const Getter &fget,
+                                const cpp_function &fset,
+                                const Extra &...extra) {
+        return def_property_static(
+            name, cpp_function(fget), fset, return_value_policy::reference, extra...);
+    }
+
+    /// Uses cpp_function's return_value_policy by default
+    template <typename... Extra>
+    class_ &def_property_static(const char *name,
+                                const cpp_function &fget,
+                                const cpp_function &fset,
+                                const Extra &...extra) {
+        static_assert(0 == detail::constexpr_sum(std::is_base_of<arg, Extra>::value...),
+                      "Argument annotations are not allowed for properties");
+        auto rec_fget = get_function_record(fget), rec_fset = get_function_record(fset);
+        auto *rec_active = rec_fget;
+        if (rec_fget) {
+            char *doc_prev = rec_fget->doc; /* 'extra' field may include a property-specific
+                                               documentation string */
+            detail::process_attributes<Extra...>::init(extra..., rec_fget);
+            if (rec_fget->doc && rec_fget->doc != doc_prev) {
+                std::free(doc_prev);
+                rec_fget->doc = PYBIND11_COMPAT_STRDUP(rec_fget->doc);
+            }
+        }
+        if (rec_fset) {
+            char *doc_prev = rec_fset->doc;
+            detail::process_attributes<Extra...>::init(extra..., rec_fset);
+            if (rec_fset->doc && rec_fset->doc != doc_prev) {
+                std::free(doc_prev);
+                rec_fset->doc = PYBIND11_COMPAT_STRDUP(rec_fset->doc);
+            }
+            if (!rec_active) {
+                rec_active = rec_fset;
+            }
+        }
+        def_property_static_impl(name, fget, fset, rec_active);
+        return *this;
+    }
+
+private:
+    /// Initialize holder object, variant 1: object derives from enable_shared_from_this
+    template <typename T>
+    static void init_holder(detail::instance *inst,
+                            detail::value_and_holder &v_h,
+                            const holder_type * /* unused */,
+                            const std::enable_shared_from_this<T> * /* dummy */) {
+
+        auto sh = std::dynamic_pointer_cast<typename holder_type::element_type>(
+            detail::try_get_shared_from_this(v_h.value_ptr<type>()));
+        if (sh) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(std::move(sh));
+            v_h.set_holder_constructed();
+        }
+
+        if (!v_h.holder_constructed() && inst->owned) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(v_h.value_ptr<type>());
+            v_h.set_holder_constructed();
+        }
+    }
+
+    static void init_holder_from_existing(const detail::value_and_holder &v_h,
+                                          const holder_type *holder_ptr,
+                                          std::true_type /*is_copy_constructible*/) {
+        new (std::addressof(v_h.holder<holder_type>()))
+            holder_type(*reinterpret_cast<const holder_type *>(holder_ptr));
+    }
+
+    static void init_holder_from_existing(const detail::value_and_holder &v_h,
+                                          const holder_type *holder_ptr,
+                                          std::false_type /*is_copy_constructible*/) {
+        new (std::addressof(v_h.holder<holder_type>()))
+            holder_type(std::move(*const_cast<holder_type *>(holder_ptr)));
+    }
+
+    /// Initialize holder object, variant 2: try to construct from existing holder object, if
+    /// possible
+    static void init_holder(detail::instance *inst,
+                            detail::value_and_holder &v_h,
+                            const holder_type *holder_ptr,
+                            const void * /* dummy -- not enable_shared_from_this<T>) */) {
+        if (holder_ptr) {
+            init_holder_from_existing(v_h, holder_ptr, std::is_copy_constructible<holder_type>());
+            v_h.set_holder_constructed();
+        } else if (detail::always_construct_holder<holder_type>::value || inst->owned) {
+            new (std::addressof(v_h.holder<holder_type>())) holder_type(v_h.value_ptr<type>());
+            v_h.set_holder_constructed();
+        }
+    }
+
+    /// Performs instance initialization including constructing a holder and registering the known
+    /// instance.  Should be called as soon as the `type` value_ptr is set for an instance.  Takes
+    /// an optional pointer to an existing holder to use; if not specified and the instance is
+    /// `.owned`, a new holder will be constructed to manage the value pointer.
+    static void init_instance(detail::instance *inst, const void *holder_ptr) {
+        auto v_h = inst->get_value_and_holder(detail::get_type_info(typeid(type)));
+        if (!v_h.instance_registered()) {
+            register_instance(inst, v_h.value_ptr(), v_h.type);
+            v_h.set_instance_registered();
+        }
+        init_holder(inst, v_h, (const holder_type *) holder_ptr, v_h.value_ptr<type>());
+    }
+
+    /// Deallocates an instance; via holder, if constructed; otherwise via operator delete.
+    static void dealloc(detail::value_and_holder &v_h) {
+        // We could be deallocating because we are cleaning up after a Python exception.
+        // If so, the Python error indicator will be set. We need to clear that before
+        // running the destructor, in case the destructor code calls more Python.
+        // If we don't, the Python API will exit with an exception, and pybind11 will
+        // throw error_already_set from the C++ destructor which is forbidden and triggers
+        // std::terminate().
+        error_scope scope;
+        if (v_h.holder_constructed()) {
+            v_h.holder<holder_type>().~holder_type();
+            v_h.set_holder_constructed(false);
+        } else {
+            detail::call_operator_delete(
+                v_h.value_ptr<type>(), v_h.type->type_size, v_h.type->type_align);
+        }
+        v_h.value_ptr() = nullptr;
+    }
+
+    static detail::function_record *get_function_record(handle h) {
+        h = detail::get_function(h);
+        if (!h) {
+            return nullptr;
+        }
+
+        handle func_self = PyCFunction_GET_SELF(h.ptr());
+        if (!func_self) {
+            throw error_already_set();
+        }
+        if (!isinstance<capsule>(func_self)) {
+            return nullptr;
+        }
+        auto cap = reinterpret_borrow<capsule>(func_self);
+        if (!detail::is_function_record_capsule(cap)) {
+            return nullptr;
+        }
+        return cap.get_pointer<detail::function_record>();
+    }
+};
+
+/// Binds an existing constructor taking arguments Args...
+template <typename... Args>
+detail::initimpl::constructor<Args...> init() {
+    return {};
+}
+/// Like `init<Args...>()`, but the instance is always constructed through the alias class (even
+/// when not inheriting on the Python side).
+template <typename... Args>
+detail::initimpl::alias_constructor<Args...> init_alias() {
+    return {};
+}
+
+/// Binds a factory function as a constructor
+template <typename Func, typename Ret = detail::initimpl::factory<Func>>
+Ret init(Func &&f) {
+    return {std::forward<Func>(f)};
+}
+
+/// Dual-argument factory function: the first function is called when no alias is needed, the
+/// second when an alias is needed (i.e. due to python-side inheritance).  Arguments must be
+/// identical.
+template <typename CFunc, typename AFunc, typename Ret = detail::initimpl::factory<CFunc, AFunc>>
+Ret init(CFunc &&c, AFunc &&a) {
+    return {std::forward<CFunc>(c), std::forward<AFunc>(a)};
+}
+
+/// Binds pickling functions `__getstate__` and `__setstate__` and ensures that the type
+/// returned by `__getstate__` is the same as the argument accepted by `__setstate__`.
+template <typename GetState, typename SetState>
+detail::initimpl::pickle_factory<GetState, SetState> pickle(GetState &&g, SetState &&s) {
+    return {std::forward<GetState>(g), std::forward<SetState>(s)};
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline str enum_name(handle arg) {
+    dict entries = arg.get_type().attr("__entries");
+    for (auto kv : entries) {
+        if (handle(kv.second[int_(0)]).equal(arg)) {
+            return pybind11::str(kv.first);
+        }
+    }
+    return "???";
+}
+
+struct enum_base {
+    enum_base(const handle &base, const handle &parent) : m_base(base), m_parent(parent) {}
+
+    PYBIND11_NOINLINE void init(bool is_arithmetic, bool is_convertible) {
+        m_base.attr("__entries") = dict();
+        auto property = handle((PyObject *) &PyProperty_Type);
+        auto static_property = handle((PyObject *) get_internals().static_property_type);
+
+        m_base.attr("__repr__") = cpp_function(
+            [](const object &arg) -> str {
+                handle type = type::handle_of(arg);
+                object type_name = type.attr("__name__");
+                return pybind11::str("<{}.{}: {}>")
+                    .format(std::move(type_name), enum_name(arg), int_(arg));
+            },
+            name("__repr__"),
+            is_method(m_base));
+
+        m_base.attr("name") = property(cpp_function(&enum_name, name("name"), is_method(m_base)));
+
+        m_base.attr("__str__") = cpp_function(
+            [](handle arg) -> str {
+                object type_name = type::handle_of(arg).attr("__name__");
+                return pybind11::str("{}.{}").format(std::move(type_name), enum_name(arg));
+            },
+            name("__str__"),
+            is_method(m_base));
+
+        if (options::show_enum_members_docstring()) {
+            m_base.attr("__doc__") = static_property(
+                cpp_function(
+                    [](handle arg) -> std::string {
+                        std::string docstring;
+                        dict entries = arg.attr("__entries");
+                        if (((PyTypeObject *) arg.ptr())->tp_doc) {
+                            docstring += std::string(
+                                reinterpret_cast<PyTypeObject *>(arg.ptr())->tp_doc);
+                            docstring += "\n\n";
+                        }
+                        docstring += "Members:";
+                        for (auto kv : entries) {
+                            auto key = std::string(pybind11::str(kv.first));
+                            auto comment = kv.second[int_(1)];
+                            docstring += "\n\n  ";
+                            docstring += key;
+                            if (!comment.is_none()) {
+                                docstring += " : ";
+                                docstring += pybind11::str(comment).cast<std::string>();
+                            }
+                        }
+                        return docstring;
+                    },
+                    name("__doc__")),
+                none(),
+                none(),
+                "");
+        }
+
+        m_base.attr("__members__") = static_property(cpp_function(
+                                                         [](handle arg) -> dict {
+                                                             dict entries = arg.attr("__entries"),
+                                                                  m;
+                                                             for (auto kv : entries) {
+                                                                 m[kv.first] = kv.second[int_(0)];
+                                                             }
+                                                             return m;
+                                                         },
+                                                         name("__members__")),
+                                                     none(),
+                                                     none(),
+                                                     "");
+
+#define PYBIND11_ENUM_OP_STRICT(op, expr, strict_behavior)                                        \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a, const object &b) {                                                    \
+            if (!type::handle_of(a).is(type::handle_of(b)))                                       \
+                strict_behavior; /* NOLINT(bugprone-macro-parentheses) */                         \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+#define PYBIND11_ENUM_OP_CONV(op, expr)                                                           \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a_, const object &b_) {                                                  \
+            int_ a(a_), b(b_);                                                                    \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+#define PYBIND11_ENUM_OP_CONV_LHS(op, expr)                                                       \
+    m_base.attr(op) = cpp_function(                                                               \
+        [](const object &a_, const object &b) {                                                   \
+            int_ a(a_);                                                                           \
+            return expr;                                                                          \
+        },                                                                                        \
+        name(op),                                                                                 \
+        is_method(m_base),                                                                        \
+        arg("other"))
+
+        if (is_convertible) {
+            PYBIND11_ENUM_OP_CONV_LHS("__eq__", !b.is_none() && a.equal(b));
+            PYBIND11_ENUM_OP_CONV_LHS("__ne__", b.is_none() || !a.equal(b));
+
+            if (is_arithmetic) {
+                PYBIND11_ENUM_OP_CONV("__lt__", a < b);
+                PYBIND11_ENUM_OP_CONV("__gt__", a > b);
+                PYBIND11_ENUM_OP_CONV("__le__", a <= b);
+                PYBIND11_ENUM_OP_CONV("__ge__", a >= b);
+                PYBIND11_ENUM_OP_CONV("__and__", a & b);
+                PYBIND11_ENUM_OP_CONV("__rand__", a & b);
+                PYBIND11_ENUM_OP_CONV("__or__", a | b);
+                PYBIND11_ENUM_OP_CONV("__ror__", a | b);
+                PYBIND11_ENUM_OP_CONV("__xor__", a ^ b);
+                PYBIND11_ENUM_OP_CONV("__rxor__", a ^ b);
+                m_base.attr("__invert__")
+                    = cpp_function([](const object &arg) { return ~(int_(arg)); },
+                                   name("__invert__"),
+                                   is_method(m_base));
+            }
+        } else {
+            PYBIND11_ENUM_OP_STRICT("__eq__", int_(a).equal(int_(b)), return false);
+            PYBIND11_ENUM_OP_STRICT("__ne__", !int_(a).equal(int_(b)), return true);
+
+            if (is_arithmetic) {
+#define PYBIND11_THROW throw type_error("Expected an enumeration of matching type!");
+                PYBIND11_ENUM_OP_STRICT("__lt__", int_(a) < int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__gt__", int_(a) > int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__le__", int_(a) <= int_(b), PYBIND11_THROW);
+                PYBIND11_ENUM_OP_STRICT("__ge__", int_(a) >= int_(b), PYBIND11_THROW);
+#undef PYBIND11_THROW
+            }
+        }
+
+#undef PYBIND11_ENUM_OP_CONV_LHS
+#undef PYBIND11_ENUM_OP_CONV
+#undef PYBIND11_ENUM_OP_STRICT
+
+        m_base.attr("__getstate__") = cpp_function(
+            [](const object &arg) { return int_(arg); }, name("__getstate__"), is_method(m_base));
+
+        m_base.attr("__hash__") = cpp_function(
+            [](const object &arg) { return int_(arg); }, name("__hash__"), is_method(m_base));
+    }
+
+    PYBIND11_NOINLINE void value(char const *name_, object value, const char *doc = nullptr) {
+        dict entries = m_base.attr("__entries");
+        str name(name_);
+        if (entries.contains(name)) {
+            std::string type_name = (std::string) str(m_base.attr("__name__"));
+            throw value_error(std::move(type_name) + ": element \"" + std::string(name_)
+                              + "\" already exists!");
+        }
+
+        entries[name] = pybind11::make_tuple(value, doc);
+        m_base.attr(std::move(name)) = std::move(value);
+    }
+
+    PYBIND11_NOINLINE void export_values() {
+        dict entries = m_base.attr("__entries");
+        for (auto kv : entries) {
+            m_parent.attr(kv.first) = kv.second[int_(0)];
+        }
+    }
+
+    handle m_base;
+    handle m_parent;
+};
+
+template <bool is_signed, size_t length>
+struct equivalent_integer {};
+template <>
+struct equivalent_integer<true, 1> {
+    using type = int8_t;
+};
+template <>
+struct equivalent_integer<false, 1> {
+    using type = uint8_t;
+};
+template <>
+struct equivalent_integer<true, 2> {
+    using type = int16_t;
+};
+template <>
+struct equivalent_integer<false, 2> {
+    using type = uint16_t;
+};
+template <>
+struct equivalent_integer<true, 4> {
+    using type = int32_t;
+};
+template <>
+struct equivalent_integer<false, 4> {
+    using type = uint32_t;
+};
+template <>
+struct equivalent_integer<true, 8> {
+    using type = int64_t;
+};
+template <>
+struct equivalent_integer<false, 8> {
+    using type = uint64_t;
+};
+
+template <typename IntLike>
+using equivalent_integer_t =
+    typename equivalent_integer<std::is_signed<IntLike>::value, sizeof(IntLike)>::type;
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Binds C++ enumerations and enumeration classes to Python
+template <typename Type>
+class enum_ : public class_<Type> {
+public:
+    using Base = class_<Type>;
+    using Base::attr;
+    using Base::def;
+    using Base::def_property_readonly;
+    using Base::def_property_readonly_static;
+    using Underlying = typename std::underlying_type<Type>::type;
+    // Scalar is the integer representation of underlying type
+    using Scalar = detail::conditional_t<detail::any_of<detail::is_std_char_type<Underlying>,
+                                                        std::is_same<Underlying, bool>>::value,
+                                         detail::equivalent_integer_t<Underlying>,
+                                         Underlying>;
+
+    template <typename... Extra>
+    enum_(const handle &scope, const char *name, const Extra &...extra)
+        : class_<Type>(scope, name, extra...), m_base(*this, scope) {
+        constexpr bool is_arithmetic = detail::any_of<std::is_same<arithmetic, Extra>...>::value;
+        constexpr bool is_convertible = std::is_convertible<Type, Underlying>::value;
+        m_base.init(is_arithmetic, is_convertible);
+
+        def(init([](Scalar i) { return static_cast<Type>(i); }), arg("value"));
+        def_property_readonly("value", [](Type value) { return (Scalar) value; });
+        def("__int__", [](Type value) { return (Scalar) value; });
+        def("__index__", [](Type value) { return (Scalar) value; });
+        attr("__setstate__") = cpp_function(
+            [](detail::value_and_holder &v_h, Scalar arg) {
+                detail::initimpl::setstate<Base>(
+                    v_h, static_cast<Type>(arg), Py_TYPE(v_h.inst) != v_h.type->type);
+            },
+            detail::is_new_style_constructor(),
+            pybind11::name("__setstate__"),
+            is_method(*this),
+            arg("state"));
+    }
+
+    /// Export enumeration entries into the parent scope
+    enum_ &export_values() {
+        m_base.export_values();
+        return *this;
+    }
+
+    /// Add an enumeration entry
+    enum_ &value(char const *name, Type value, const char *doc = nullptr) {
+        m_base.value(name, pybind11::cast(value, return_value_policy::copy), doc);
+        return *this;
+    }
+
+private:
+    detail::enum_base m_base;
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+PYBIND11_NOINLINE void keep_alive_impl(handle nurse, handle patient) {
+    if (!nurse || !patient) {
+        pybind11_fail("Could not activate keep_alive!");
+    }
+
+    if (patient.is_none() || nurse.is_none()) {
+        return; /* Nothing to keep alive or nothing to be kept alive by */
+    }
+
+    auto tinfo = all_type_info(Py_TYPE(nurse.ptr()));
+    if (!tinfo.empty()) {
+        /* It's a pybind-registered type, so we can store the patient in the
+         * internal list. */
+        add_patient(nurse.ptr(), patient.ptr());
+    } else {
+        /* Fall back to clever approach based on weak references taken from
+         * Boost.Python. This is not used for pybind-registered types because
+         * the objects can be destroyed out-of-order in a GC pass. */
+        cpp_function disable_lifesupport([patient](handle weakref) {
+            patient.dec_ref();
+            weakref.dec_ref();
+        });
+
+        weakref wr(nurse, disable_lifesupport);
+
+        patient.inc_ref(); /* reference patient and leak the weak reference */
+        (void) wr.release();
+    }
+}
+
+PYBIND11_NOINLINE void
+keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret) {
+    auto get_arg = [&](size_t n) {
+        if (n == 0) {
+            return ret;
+        }
+        if (n == 1 && call.init_self) {
+            return call.init_self;
+        }
+        if (n <= call.args.size()) {
+            return call.args[n - 1];
+        }
+        return handle();
+    };
+
+    keep_alive_impl(get_arg(Nurse), get_arg(Patient));
+}
+
+inline std::pair<decltype(internals::registered_types_py)::iterator, bool>
+all_type_info_get_cache(PyTypeObject *type) {
+    auto res = with_internals([type](internals &internals) {
+        return internals
+            .registered_types_py
+#ifdef __cpp_lib_unordered_map_try_emplace
+            .try_emplace(type);
+#else
+            .emplace(type, std::vector<detail::type_info *>());
+#endif
+    });
+    if (res.second) {
+        // New cache entry created; set up a weak reference to automatically remove it if the type
+        // gets destroyed:
+        weakref((PyObject *) type, cpp_function([type](handle wr) {
+                    with_internals([type](internals &internals) {
+                        internals.registered_types_py.erase(type);
+
+                        // TODO consolidate the erasure code in pybind11_meta_dealloc() in class.h
+                        auto &cache = internals.inactive_override_cache;
+                        for (auto it = cache.begin(), last = cache.end(); it != last;) {
+                            if (it->first == reinterpret_cast<PyObject *>(type)) {
+                                it = cache.erase(it);
+                            } else {
+                                ++it;
+                            }
+                        }
+                    });
+
+                    wr.dec_ref();
+                }))
+            .release();
+    }
+
+    return res;
+}
+
+/* There are a large number of apparently unused template arguments because
+ * each combination requires a separate py::class_ registration.
+ */
+template <typename Access,
+          return_value_policy Policy,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType,
+          typename... Extra>
+struct iterator_state {
+    Iterator it;
+    Sentinel end;
+    bool first_or_done;
+};
+
+// Note: these helpers take the iterator by non-const reference because some
+// iterators in the wild can't be dereferenced when const. The & after Iterator
+// is required for MSVC < 16.9. SFINAE cannot be reused for result_type due to
+// bugs in ICC, NVCC, and PGI compilers. See PR #3293.
+template <typename Iterator, typename SFINAE = decltype(*std::declval<Iterator &>())>
+struct iterator_access {
+    using result_type = decltype(*std::declval<Iterator &>());
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    result_type operator()(Iterator &it) const { return *it; }
+};
+
+template <typename Iterator, typename SFINAE = decltype((*std::declval<Iterator &>()).first)>
+class iterator_key_access {
+private:
+    using pair_type = decltype(*std::declval<Iterator &>());
+
+public:
+    /* If either the pair itself or the element of the pair is a reference, we
+     * want to return a reference, otherwise a value. When the decltype
+     * expression is parenthesized it is based on the value category of the
+     * expression; otherwise it is the declared type of the pair member.
+     * The use of declval<pair_type> in the second branch rather than directly
+     * using *std::declval<Iterator &>() is a workaround for nvcc
+     * (it's not used in the first branch because going via decltype and back
+     * through declval does not perfectly preserve references).
+     */
+    using result_type
+        = conditional_t<std::is_reference<decltype(*std::declval<Iterator &>())>::value,
+                        decltype(((*std::declval<Iterator &>()).first)),
+                        decltype(std::declval<pair_type>().first)>;
+    result_type operator()(Iterator &it) const { return (*it).first; }
+};
+
+template <typename Iterator, typename SFINAE = decltype((*std::declval<Iterator &>()).second)>
+class iterator_value_access {
+private:
+    using pair_type = decltype(*std::declval<Iterator &>());
+
+public:
+    using result_type
+        = conditional_t<std::is_reference<decltype(*std::declval<Iterator &>())>::value,
+                        decltype(((*std::declval<Iterator &>()).second)),
+                        decltype(std::declval<pair_type>().second)>;
+    result_type operator()(Iterator &it) const { return (*it).second; }
+};
+
+template <typename Access,
+          return_value_policy Policy,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType,
+          typename... Extra>
+iterator make_iterator_impl(Iterator first, Sentinel last, Extra &&...extra) {
+    using state = detail::iterator_state<Access, Policy, Iterator, Sentinel, ValueType, Extra...>;
+    // TODO: state captures only the types of Extra, not the values
+
+    if (!detail::get_type_info(typeid(state), false)) {
+        class_<state>(handle(), "iterator", pybind11::module_local())
+            .def("__iter__", [](state &s) -> state & { return s; })
+            .def(
+                "__next__",
+                [](state &s) -> ValueType {
+                    if (!s.first_or_done) {
+                        ++s.it;
+                    } else {
+                        s.first_or_done = false;
+                    }
+                    if (s.it == s.end) {
+                        s.first_or_done = true;
+                        throw stop_iteration();
+                    }
+                    return Access()(s.it);
+                    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+                },
+                std::forward<Extra>(extra)...,
+                Policy);
+    }
+
+    return cast(state{std::forward<Iterator>(first), std::forward<Sentinel>(last), true});
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Makes a python iterator from a first and past-the-end C++ InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType = typename detail::iterator_access<Iterator>::result_type,
+          typename... Extra>
+typing::Iterator<ValueType> make_iterator(Iterator first, Sentinel last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      ValueType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes a python iterator over the keys (`.first`) of a iterator over pairs from a
+/// first and past-the-end InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename KeyType = typename detail::iterator_key_access<Iterator>::result_type,
+          typename... Extra>
+typing::Iterator<KeyType> make_key_iterator(Iterator first, Sentinel last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_key_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      KeyType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes a python iterator over the values (`.second`) of a iterator over pairs from a
+/// first and past-the-end InputIterator.
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Iterator,
+          typename Sentinel,
+          typename ValueType = typename detail::iterator_value_access<Iterator>::result_type,
+          typename... Extra>
+typing::Iterator<ValueType> make_value_iterator(Iterator first, Sentinel last, Extra &&...extra) {
+    return detail::make_iterator_impl<detail::iterator_value_access<Iterator>,
+                                      Policy,
+                                      Iterator,
+                                      Sentinel,
+                                      ValueType,
+                                      Extra...>(std::forward<Iterator>(first),
+                                                std::forward<Sentinel>(last),
+                                                std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over values of an stl container or other container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename ValueType = typename detail::iterator_access<
+              decltype(std::begin(std::declval<Type &>()))>::result_type,
+          typename... Extra>
+typing::Iterator<ValueType> make_iterator(Type &value, Extra &&...extra) {
+    return make_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over the keys (`.first`) of a stl map-like container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename KeyType = typename detail::iterator_key_access<
+              decltype(std::begin(std::declval<Type &>()))>::result_type,
+          typename... Extra>
+typing::Iterator<KeyType> make_key_iterator(Type &value, Extra &&...extra) {
+    return make_key_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+/// Makes an iterator over the values (`.second`) of a stl map-like container supporting
+/// `std::begin()`/`std::end()`
+template <return_value_policy Policy = return_value_policy::reference_internal,
+          typename Type,
+          typename ValueType = typename detail::iterator_value_access<
+              decltype(std::begin(std::declval<Type &>()))>::result_type,
+          typename... Extra>
+typing::Iterator<ValueType> make_value_iterator(Type &value, Extra &&...extra) {
+    return make_value_iterator<Policy>(
+        std::begin(value), std::end(value), std::forward<Extra>(extra)...);
+}
+
+template <typename InputType, typename OutputType>
+void implicitly_convertible() {
+    struct set_flag {
+        bool &flag;
+        explicit set_flag(bool &flag_) : flag(flag_) { flag_ = true; }
+        ~set_flag() { flag = false; }
+    };
+    auto implicit_caster = [](PyObject *obj, PyTypeObject *type) -> PyObject * {
+#ifdef Py_GIL_DISABLED
+        thread_local bool currently_used = false;
+#else
+        static bool currently_used = false;
+#endif
+        if (currently_used) { // implicit conversions are non-reentrant
+            return nullptr;
+        }
+        set_flag flag_helper(currently_used);
+        if (!detail::make_caster<InputType>().load(obj, false)) {
+            return nullptr;
+        }
+        tuple args(1);
+        args[0] = obj;
+        PyObject *result = PyObject_Call((PyObject *) type, args.ptr(), nullptr);
+        if (result == nullptr) {
+            PyErr_Clear();
+        }
+        return result;
+    };
+
+    if (auto *tinfo = detail::get_type_info(typeid(OutputType))) {
+        tinfo->implicit_conversions.emplace_back(std::move(implicit_caster));
+    } else {
+        pybind11_fail("implicitly_convertible: Unable to find type " + type_id<OutputType>());
+    }
+}
+
+inline void register_exception_translator(ExceptionTranslator &&translator) {
+    detail::with_internals([&](detail::internals &internals) {
+        internals.registered_exception_translators.push_front(
+            std::forward<ExceptionTranslator>(translator));
+    });
+}
+
+/**
+ * Add a new module-local exception translator. Locally registered functions
+ * will be tried before any globally registered exception translators, which
+ * will only be invoked if the module-local handlers do not deal with
+ * the exception.
+ */
+inline void register_local_exception_translator(ExceptionTranslator &&translator) {
+    detail::with_internals([&](detail::internals &internals) {
+        (void) internals;
+        detail::get_local_internals().registered_exception_translators.push_front(
+            std::forward<ExceptionTranslator>(translator));
+    });
+}
+
+/**
+ * Wrapper to generate a new Python exception type.
+ *
+ * This should only be used with py::set_error() for now.
+ * It is not (yet) possible to use as a py::base.
+ * Template type argument is reserved for future use.
+ */
+template <typename type>
+class exception : public object {
+public:
+    exception() = default;
+    exception(handle scope, const char *name, handle base = PyExc_Exception) {
+        std::string full_name
+            = scope.attr("__name__").cast<std::string>() + std::string(".") + name;
+        m_ptr = PyErr_NewException(const_cast<char *>(full_name.c_str()), base.ptr(), nullptr);
+        if (hasattr(scope, "__dict__") && scope.attr("__dict__").contains(name)) {
+            pybind11_fail("Error during initialization: multiple incompatible "
+                          "definitions with name \""
+                          + std::string(name) + "\"");
+        }
+        scope.attr(name) = *this;
+    }
+
+    // Sets the current python exception to this exception object with the given message
+    PYBIND11_DEPRECATED("Please use py::set_error() instead "
+                        "(https://github.com/pybind/pybind11/pull/4772)")
+    void operator()(const char *message) const { set_error(*this, message); }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+struct handle_type_name<exception<void>> {
+    static constexpr auto name = const_name("Exception");
+};
+
+// Helper function for register_exception and register_local_exception
+template <typename CppException>
+exception<CppException> &
+register_exception_impl(handle scope, const char *name, handle base, bool isLocal) {
+    PYBIND11_CONSTINIT static gil_safe_call_once_and_store<exception<CppException>> exc_storage;
+    exc_storage.call_once_and_store_result(
+        [&]() { return exception<CppException>(scope, name, base); });
+
+    auto register_func
+        = isLocal ? &register_local_exception_translator : &register_exception_translator;
+
+    register_func([](std::exception_ptr p) {
+        if (!p) {
+            return;
+        }
+        try {
+            std::rethrow_exception(p);
+        } catch (const CppException &e) {
+            set_error(exc_storage.get_stored(), e.what());
+        }
+    });
+    return exc_storage.get_stored();
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/**
+ * Registers a Python exception in `m` of the given `name` and installs a translator to
+ * translate the C++ exception to the created Python exception using the what() method.
+ * This is intended for simple exception translations; for more complex translation, register the
+ * exception object and translator directly.
+ */
+template <typename CppException>
+exception<CppException> &
+register_exception(handle scope, const char *name, handle base = PyExc_Exception) {
+    return detail::register_exception_impl<CppException>(scope, name, base, false /* isLocal */);
+}
+
+/**
+ * Registers a Python exception in `m` of the given `name` and installs a translator to
+ * translate the C++ exception to the created Python exception using the what() method.
+ * This translator will only be used for exceptions that are thrown in this module and will be
+ * tried before global exception translators, including those registered with register_exception.
+ * This is intended for simple exception translations; for more complex translation, register the
+ * exception object and translator directly.
+ */
+template <typename CppException>
+exception<CppException> &
+register_local_exception(handle scope, const char *name, handle base = PyExc_Exception) {
+    return detail::register_exception_impl<CppException>(scope, name, base, true /* isLocal */);
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+PYBIND11_NOINLINE void print(const tuple &args, const dict &kwargs) {
+    auto strings = tuple(args.size());
+    for (size_t i = 0; i < args.size(); ++i) {
+        strings[i] = str(args[i]);
+    }
+    auto sep = kwargs.contains("sep") ? kwargs["sep"] : str(" ");
+    auto line = sep.attr("join")(std::move(strings));
+
+    object file;
+    if (kwargs.contains("file")) {
+        file = kwargs["file"].cast<object>();
+    } else {
+        try {
+            file = module_::import("sys").attr("stdout");
+        } catch (const error_already_set &) {
+            /* If print() is called from code that is executed as
+               part of garbage collection during interpreter shutdown,
+               importing 'sys' can fail. Give up rather than crashing the
+               interpreter in this case. */
+            return;
+        }
+    }
+
+    auto write = file.attr("write");
+    write(std::move(line));
+    write(kwargs.contains("end") ? kwargs["end"] : str("\n"));
+
+    if (kwargs.contains("flush") && kwargs["flush"].cast<bool>()) {
+        file.attr("flush")();
+    }
+}
+PYBIND11_NAMESPACE_END(detail)
+
+template <return_value_policy policy = return_value_policy::automatic_reference, typename... Args>
+void print(Args &&...args) {
+    auto c = detail::collect_arguments<policy>(std::forward<Args>(args)...);
+    detail::print(c.args(), c.kwargs());
+}
+
+inline void
+error_already_set::m_fetched_error_deleter(detail::error_fetch_and_normalize *raw_ptr) {
+    gil_scoped_acquire gil;
+    error_scope scope;
+    delete raw_ptr;
+}
+
+inline const char *error_already_set::what() const noexcept {
+    gil_scoped_acquire gil;
+    error_scope scope;
+    return m_fetched_error->error_string().c_str();
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+inline function
+get_type_override(const void *this_ptr, const type_info *this_type, const char *name) {
+    handle self = get_object_handle(this_ptr, this_type);
+    if (!self) {
+        return function();
+    }
+    handle type = type::handle_of(self);
+    auto key = std::make_pair(type.ptr(), name);
+
+    /* Cache functions that aren't overridden in Python to avoid
+       many costly Python dictionary lookups below */
+    bool not_overridden = with_internals([&key](internals &internals) {
+        auto &cache = internals.inactive_override_cache;
+        return cache.find(key) != cache.end();
+    });
+    if (not_overridden) {
+        return function();
+    }
+
+    function override = getattr(self, name, function());
+    if (override.is_cpp_function()) {
+        with_internals([&](internals &internals) {
+            internals.inactive_override_cache.insert(std::move(key));
+        });
+        return function();
+    }
+
+    /* Don't call dispatch code if invoked from overridden function.
+       Unfortunately this doesn't work on PyPy. */
+#if !defined(PYPY_VERSION)
+#    if PY_VERSION_HEX >= 0x03090000
+    PyFrameObject *frame = PyThreadState_GetFrame(PyThreadState_Get());
+    if (frame != nullptr) {
+        PyCodeObject *f_code = PyFrame_GetCode(frame);
+        // f_code is guaranteed to not be NULL
+        if ((std::string) str(f_code->co_name) == name && f_code->co_argcount > 0) {
+#        if PY_VERSION_HEX >= 0x030d0000
+            PyObject *locals = PyEval_GetFrameLocals();
+#        else
+            PyObject *locals = PyEval_GetLocals();
+            Py_XINCREF(locals);
+#        endif
+            if (locals != nullptr) {
+#        if PY_VERSION_HEX >= 0x030b0000
+                PyObject *co_varnames = PyCode_GetVarnames(f_code);
+#        else
+                PyObject *co_varnames = PyObject_GetAttrString((PyObject *) f_code, "co_varnames");
+#        endif
+                PyObject *self_arg = PyTuple_GET_ITEM(co_varnames, 0);
+                Py_DECREF(co_varnames);
+                PyObject *self_caller = dict_getitem(locals, self_arg);
+                Py_DECREF(locals);
+                if (self_caller == self.ptr()) {
+                    Py_DECREF(f_code);
+                    Py_DECREF(frame);
+                    return function();
+                }
+            }
+        }
+        Py_DECREF(f_code);
+        Py_DECREF(frame);
+    }
+#    else
+    PyFrameObject *frame = PyThreadState_Get()->frame;
+    if (frame != nullptr && (std::string) str(frame->f_code->co_name) == name
+        && frame->f_code->co_argcount > 0) {
+        PyFrame_FastToLocals(frame);
+        PyObject *self_caller
+            = dict_getitem(frame->f_locals, PyTuple_GET_ITEM(frame->f_code->co_varnames, 0));
+        if (self_caller == self.ptr()) {
+            return function();
+        }
+    }
+#    endif
+
+#else
+    /* PyPy currently doesn't provide a detailed cpyext emulation of
+       frame objects, so we have to emulate this using Python. This
+       is going to be slow..*/
+    dict d;
+    d["self"] = self;
+    d["name"] = pybind11::str(name);
+    PyObject *result
+        = PyRun_String("import inspect\n"
+                       "frame = inspect.currentframe()\n"
+                       "if frame is not None:\n"
+                       "    frame = frame.f_back\n"
+                       "    if frame is not None and str(frame.f_code.co_name) == name and "
+                       "frame.f_code.co_argcount > 0:\n"
+                       "        self_caller = frame.f_locals[frame.f_code.co_varnames[0]]\n"
+                       "        if self_caller == self:\n"
+                       "            self = None\n",
+                       Py_file_input,
+                       d.ptr(),
+                       d.ptr());
+    if (result == nullptr)
+        throw error_already_set();
+    Py_DECREF(result);
+    if (d["self"].is_none())
+        return function();
+#endif
+
+    return override;
+}
+PYBIND11_NAMESPACE_END(detail)
+
+/** \rst
+  Try to retrieve a python method by the provided name from the instance pointed to by the
+  this_ptr.
+
+  :this_ptr: The pointer to the object the overridden method should be retrieved for. This should
+             be the first non-trampoline class encountered in the inheritance chain.
+  :name: The name of the overridden Python method to retrieve.
+  :return: The Python method by this name from the object or an empty function wrapper.
+ \endrst */
+template <class T>
+function get_override(const T *this_ptr, const char *name) {
+    auto *tinfo = detail::get_type_info(typeid(T));
+    return tinfo ? detail::get_type_override(this_ptr, tinfo, name) : function();
+}
+
+#define PYBIND11_OVERRIDE_IMPL(ret_type, cname, name, ...)                                        \
+    do {                                                                                          \
+        pybind11::gil_scoped_acquire gil;                                                         \
+        pybind11::function override                                                               \
+            = pybind11::get_override(static_cast<const cname *>(this), name);                     \
+        if (override) {                                                                           \
+            auto o = override(__VA_ARGS__);                                                       \
+            PYBIND11_WARNING_PUSH                                                                 \
+            PYBIND11_WARNING_DISABLE_MSVC(4127)                                                   \
+            if (pybind11::detail::cast_is_temporary_value_reference<ret_type>::value              \
+                && !pybind11::detail::is_same_ignoring_cvref<ret_type, PyObject *>::value) {      \
+                static pybind11::detail::override_caster_t<ret_type> caster;                      \
+                return pybind11::detail::cast_ref<ret_type>(std::move(o), caster);                \
+            }                                                                                     \
+            PYBIND11_WARNING_POP                                                                  \
+            return pybind11::detail::cast_safe<ret_type>(std::move(o));                           \
+        }                                                                                         \
+    } while (false)
+
+/** \rst
+    Macro to populate the virtual method in the trampoline class. This macro tries to look up a
+    method named 'fn' from the Python side, deals with the :ref:`gil` and necessary argument
+    conversions to call this method and return the appropriate type.
+    See :ref:`overriding_virtuals` for more information. This macro should be used when the method
+    name in C is not the same as the method name in Python. For example with `__str__`.
+
+    .. code-block:: cpp
+
+      std::string toString() override {
+        PYBIND11_OVERRIDE_NAME(
+            std::string, // Return type (ret_type)
+            Animal,      // Parent class (cname)
+            "__str__",   // Name of method in Python (name)
+            toString,    // Name of function in C++ (fn)
+        );
+      }
+\endrst */
+#define PYBIND11_OVERRIDE_NAME(ret_type, cname, name, fn, ...)                                    \
+    do {                                                                                          \
+        PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__); \
+        return cname::fn(__VA_ARGS__);                                                            \
+    } while (false)
+
+/** \rst
+    Macro for pure virtual functions, this function is identical to
+    :c:macro:`PYBIND11_OVERRIDE_NAME`, except that it throws if no override can be found.
+\endrst */
+#define PYBIND11_OVERRIDE_PURE_NAME(ret_type, cname, name, fn, ...)                               \
+    do {                                                                                          \
+        PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__); \
+        pybind11::pybind11_fail(                                                                  \
+            "Tried to call pure virtual function \"" PYBIND11_STRINGIFY(cname) "::" name "\"");   \
+    } while (false)
+
+/** \rst
+    Macro to populate the virtual method in the trampoline class. This macro tries to look up the
+    method from the Python side, deals with the :ref:`gil` and necessary argument conversions to
+    call this method and return the appropriate type. This macro should be used if the method name
+    in C and in Python are identical.
+    See :ref:`overriding_virtuals` for more information.
+
+    .. code-block:: cpp
+
+      class PyAnimal : public Animal {
+      public:
+          // Inherit the constructors
+          using Animal::Animal;
+
+          // Trampoline (need one for each virtual function)
+          std::string go(int n_times) override {
+              PYBIND11_OVERRIDE_PURE(
+                  std::string, // Return type (ret_type)
+                  Animal,      // Parent class (cname)
+                  go,          // Name of function in C++ (must match Python name) (fn)
+                  n_times      // Argument(s) (...)
+              );
+          }
+      };
+\endrst */
+#define PYBIND11_OVERRIDE(ret_type, cname, fn, ...)                                               \
+    PYBIND11_OVERRIDE_NAME(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), #fn, fn, __VA_ARGS__)
+
+/** \rst
+    Macro for pure virtual functions, this function is identical to :c:macro:`PYBIND11_OVERRIDE`,
+    except that it throws if no override can be found.
+\endrst */
+#define PYBIND11_OVERRIDE_PURE(ret_type, cname, fn, ...)                                          \
+    PYBIND11_OVERRIDE_PURE_NAME(                                                                  \
+        PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), #fn, fn, __VA_ARGS__)
+
+// Deprecated versions
+
+PYBIND11_DEPRECATED("get_type_overload has been deprecated")
+inline function
+get_type_overload(const void *this_ptr, const detail::type_info *this_type, const char *name) {
+    return detail::get_type_override(this_ptr, this_type, name);
+}
+
+template <class T>
+inline function get_overload(const T *this_ptr, const char *name) {
+    return get_override(this_ptr, name);
+}
+
+#define PYBIND11_OVERLOAD_INT(ret_type, cname, name, ...)                                         \
+    PYBIND11_OVERRIDE_IMPL(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_NAME(ret_type, cname, name, fn, ...)                                    \
+    PYBIND11_OVERRIDE_NAME(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, fn, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_PURE_NAME(ret_type, cname, name, fn, ...)                               \
+    PYBIND11_OVERRIDE_PURE_NAME(                                                                  \
+        PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), name, fn, __VA_ARGS__);
+#define PYBIND11_OVERLOAD(ret_type, cname, fn, ...)                                               \
+    PYBIND11_OVERRIDE(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), fn, __VA_ARGS__)
+#define PYBIND11_OVERLOAD_PURE(ret_type, cname, fn, ...)                                          \
+    PYBIND11_OVERRIDE_PURE(PYBIND11_TYPE(ret_type), PYBIND11_TYPE(cname), fn, __VA_ARGS__);
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/pytypes.h b/lib/python3.11/site-packages/torch/include/pybind11/pytypes.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e76d7bc136c6e3de2ec693161dc9328eda52cd6
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/pytypes.h
@@ -0,0 +1,2606 @@
+/*
+    pybind11/pytypes.h: Convenience wrapper classes for basic Python types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "buffer_info.h"
+
+#include <assert.h>
+#include <cstddef>
+#include <exception>
+#include <frameobject.h>
+#include <iterator>
+#include <memory>
+#include <string>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+
+#if defined(PYBIND11_HAS_OPTIONAL)
+#    include <optional>
+#endif
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+#    include <string_view>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+/* A few forward declarations */
+class handle;
+class object;
+class str;
+class iterator;
+class type;
+struct arg;
+struct arg_v;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+class args_proxy;
+bool isinstance_generic(handle obj, const std::type_info &tp);
+
+// Accessor forward declarations
+template <typename Policy>
+class accessor;
+namespace accessor_policies {
+struct obj_attr;
+struct str_attr;
+struct generic_item;
+struct sequence_item;
+struct list_item;
+struct tuple_item;
+} // namespace accessor_policies
+// PLEASE KEEP handle_type_name SPECIALIZATIONS IN SYNC.
+using obj_attr_accessor = accessor<accessor_policies::obj_attr>;
+using str_attr_accessor = accessor<accessor_policies::str_attr>;
+using item_accessor = accessor<accessor_policies::generic_item>;
+using sequence_accessor = accessor<accessor_policies::sequence_item>;
+using list_accessor = accessor<accessor_policies::list_item>;
+using tuple_accessor = accessor<accessor_policies::tuple_item>;
+
+/// Tag and check to identify a class which implements the Python object API
+class pyobject_tag {};
+template <typename T>
+using is_pyobject = std::is_base_of<pyobject_tag, remove_reference_t<T>>;
+
+/** \rst
+    A mixin class which adds common functions to `handle`, `object` and various accessors.
+    The only requirement for `Derived` is to implement ``PyObject *Derived::ptr() const``.
+\endrst */
+template <typename Derived>
+class object_api : public pyobject_tag {
+    const Derived &derived() const { return static_cast<const Derived &>(*this); }
+
+public:
+    /** \rst
+        Return an iterator equivalent to calling ``iter()`` in Python. The object
+        must be a collection which supports the iteration protocol.
+    \endrst */
+    iterator begin() const;
+    /// Return a sentinel which ends iteration.
+    iterator end() const;
+
+    /** \rst
+        Return an internal functor to invoke the object's sequence protocol. Casting
+        the returned ``detail::item_accessor`` instance to a `handle` or `object`
+        subclass causes a corresponding call to ``__getitem__``. Assigning a `handle`
+        or `object` subclass causes a call to ``__setitem__``.
+    \endrst */
+    item_accessor operator[](handle key) const;
+    /// See above (the only difference is that the key's reference is stolen)
+    item_accessor operator[](object &&key) const;
+    /// See above (the only difference is that the key is provided as a string literal)
+    item_accessor operator[](const char *key) const;
+
+    /** \rst
+        Return an internal functor to access the object's attributes. Casting the
+        returned ``detail::obj_attr_accessor`` instance to a `handle` or `object`
+        subclass causes a corresponding call to ``getattr``. Assigning a `handle`
+        or `object` subclass causes a call to ``setattr``.
+    \endrst */
+    obj_attr_accessor attr(handle key) const;
+    /// See above (the only difference is that the key's reference is stolen)
+    obj_attr_accessor attr(object &&key) const;
+    /// See above (the only difference is that the key is provided as a string literal)
+    str_attr_accessor attr(const char *key) const;
+
+    /** \rst
+        Matches * unpacking in Python, e.g. to unpack arguments out of a ``tuple``
+        or ``list`` for a function call. Applying another * to the result yields
+        ** unpacking, e.g. to unpack a dict as function keyword arguments.
+        See :ref:`calling_python_functions`.
+    \endrst */
+    args_proxy operator*() const;
+
+    /// Check if the given item is contained within this object, i.e. ``item in obj``.
+    template <typename T>
+    bool contains(T &&item) const;
+
+    /** \rst
+        Assuming the Python object is a function or implements the ``__call__``
+        protocol, ``operator()`` invokes the underlying function, passing an
+        arbitrary set of parameters. The result is returned as a `object` and
+        may need to be converted back into a Python object using `handle::cast()`.
+
+        When some of the arguments cannot be converted to Python objects, the
+        function will throw a `cast_error` exception. When the Python function
+        call fails, a `error_already_set` exception is thrown.
+    \endrst */
+    template <return_value_policy policy = return_value_policy::automatic_reference,
+              typename... Args>
+    object operator()(Args &&...args) const;
+    template <return_value_policy policy = return_value_policy::automatic_reference,
+              typename... Args>
+    PYBIND11_DEPRECATED("call(...) was deprecated in favor of operator()(...)")
+    object call(Args &&...args) const;
+
+    /// Equivalent to ``obj is other`` in Python.
+    bool is(object_api const &other) const { return derived().ptr() == other.derived().ptr(); }
+    /// Equivalent to ``obj is None`` in Python.
+    bool is_none() const { return derived().ptr() == Py_None; }
+    /// Equivalent to obj == other in Python
+    bool equal(object_api const &other) const { return rich_compare(other, Py_EQ); }
+    bool not_equal(object_api const &other) const { return rich_compare(other, Py_NE); }
+    bool operator<(object_api const &other) const { return rich_compare(other, Py_LT); }
+    bool operator<=(object_api const &other) const { return rich_compare(other, Py_LE); }
+    bool operator>(object_api const &other) const { return rich_compare(other, Py_GT); }
+    bool operator>=(object_api const &other) const { return rich_compare(other, Py_GE); }
+
+    object operator-() const;
+    object operator~() const;
+    object operator+(object_api const &other) const;
+    object operator+=(object_api const &other);
+    object operator-(object_api const &other) const;
+    object operator-=(object_api const &other);
+    object operator*(object_api const &other) const;
+    object operator*=(object_api const &other);
+    object operator/(object_api const &other) const;
+    object operator/=(object_api const &other);
+    object operator|(object_api const &other) const;
+    object operator|=(object_api const &other);
+    object operator&(object_api const &other) const;
+    object operator&=(object_api const &other);
+    object operator^(object_api const &other) const;
+    object operator^=(object_api const &other);
+    object operator<<(object_api const &other) const;
+    object operator<<=(object_api const &other);
+    object operator>>(object_api const &other) const;
+    object operator>>=(object_api const &other);
+
+    PYBIND11_DEPRECATED("Use py::str(obj) instead")
+    pybind11::str str() const;
+
+    /// Get or set the object's docstring, i.e. ``obj.__doc__``.
+    str_attr_accessor doc() const;
+
+    /// Return the object's current reference count
+    ssize_t ref_count() const {
+#ifdef PYPY_VERSION
+        // PyPy uses the top few bits for REFCNT_FROM_PYPY & REFCNT_FROM_PYPY_LIGHT
+        // Following pybind11 2.12.1 and older behavior and removing this part
+        return static_cast<ssize_t>(static_cast<int>(Py_REFCNT(derived().ptr())));
+#else
+        return Py_REFCNT(derived().ptr());
+#endif
+    }
+
+    // TODO PYBIND11_DEPRECATED(
+    //     "Call py::type::handle_of(h) or py::type::of(h) instead of h.get_type()")
+    handle get_type() const;
+
+private:
+    bool rich_compare(object_api const &other, int value) const;
+};
+
+template <typename T>
+using is_pyobj_ptr_or_nullptr_t = detail::any_of<std::is_same<T, PyObject *>,
+                                                 std::is_same<T, PyObject *const>,
+                                                 std::is_same<T, std::nullptr_t>>;
+
+PYBIND11_NAMESPACE_END(detail)
+
+#if !defined(PYBIND11_HANDLE_REF_DEBUG) && !defined(NDEBUG)
+#    define PYBIND11_HANDLE_REF_DEBUG
+#endif
+
+/** \rst
+    Holds a reference to a Python object (no reference counting)
+
+    The `handle` class is a thin wrapper around an arbitrary Python object (i.e. a
+    ``PyObject *`` in Python's C API). It does not perform any automatic reference
+    counting and merely provides a basic C++ interface to various Python API functions.
+
+    .. seealso::
+        The `object` class inherits from `handle` and adds automatic reference
+        counting features.
+\endrst */
+class handle : public detail::object_api<handle> {
+public:
+    /// The default constructor creates a handle with a ``nullptr``-valued pointer
+    handle() = default;
+
+    /// Enable implicit conversion from ``PyObject *`` and ``nullptr``.
+    /// Not using ``handle(PyObject *ptr)`` to avoid implicit conversion from ``0``.
+    template <typename T,
+              detail::enable_if_t<detail::is_pyobj_ptr_or_nullptr_t<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    handle(T ptr) : m_ptr(ptr) {}
+
+    /// Enable implicit conversion through ``T::operator PyObject *()``.
+    template <
+        typename T,
+        detail::enable_if_t<detail::all_of<detail::none_of<std::is_base_of<handle, T>,
+                                                           detail::is_pyobj_ptr_or_nullptr_t<T>>,
+                                           std::is_convertible<T, PyObject *>>::value,
+                            int>
+        = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    handle(T &obj) : m_ptr(obj) {}
+
+    /// Return the underlying ``PyObject *`` pointer
+    PyObject *ptr() const { return m_ptr; }
+    PyObject *&ptr() { return m_ptr; }
+
+    /** \rst
+        Manually increase the reference count of the Python object. Usually, it is
+        preferable to use the `object` class which derives from `handle` and calls
+        this function automatically. Returns a reference to itself.
+    \endrst */
+    const handle &inc_ref() const & {
+#ifdef PYBIND11_HANDLE_REF_DEBUG
+        inc_ref_counter(1);
+#endif
+#ifdef PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF
+        if (m_ptr != nullptr && !PyGILState_Check()) {
+            throw_gilstate_error("pybind11::handle::inc_ref()");
+        }
+#endif
+        Py_XINCREF(m_ptr);
+        return *this;
+    }
+
+    /** \rst
+        Manually decrease the reference count of the Python object. Usually, it is
+        preferable to use the `object` class which derives from `handle` and calls
+        this function automatically. Returns a reference to itself.
+    \endrst */
+    const handle &dec_ref() const & {
+#ifdef PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF
+        if (m_ptr != nullptr && !PyGILState_Check()) {
+            throw_gilstate_error("pybind11::handle::dec_ref()");
+        }
+#endif
+        Py_XDECREF(m_ptr);
+        return *this;
+    }
+
+    /** \rst
+        Attempt to cast the Python object into the given C++ type. A `cast_error`
+        will be throw upon failure.
+    \endrst */
+    template <typename T>
+    T cast() const;
+    /// Return ``true`` when the `handle` wraps a valid Python object
+    explicit operator bool() const { return m_ptr != nullptr; }
+    /** \rst
+        Deprecated: Check that the underlying pointers are the same.
+        Equivalent to ``obj1 is obj2`` in Python.
+    \endrst */
+    PYBIND11_DEPRECATED("Use obj1.is(obj2) instead")
+    bool operator==(const handle &h) const { return m_ptr == h.m_ptr; }
+    PYBIND11_DEPRECATED("Use !obj1.is(obj2) instead")
+    bool operator!=(const handle &h) const { return m_ptr != h.m_ptr; }
+    PYBIND11_DEPRECATED("Use handle::operator bool() instead")
+    bool check() const { return m_ptr != nullptr; }
+
+protected:
+    PyObject *m_ptr = nullptr;
+
+private:
+#ifdef PYBIND11_ASSERT_GIL_HELD_INCREF_DECREF
+    void throw_gilstate_error(const std::string &function_name) const {
+        fprintf(
+            stderr,
+            "%s is being called while the GIL is either not held or invalid. Please see "
+            "https://pybind11.readthedocs.io/en/stable/advanced/"
+            "misc.html#common-sources-of-global-interpreter-lock-errors for debugging advice.\n"
+            "If you are convinced there is no bug in your code, you can #define "
+            "PYBIND11_NO_ASSERT_GIL_HELD_INCREF_DECREF "
+            "to disable this check. In that case you have to ensure this #define is consistently "
+            "used for all translation units linked into a given pybind11 extension, otherwise "
+            "there will be ODR violations.",
+            function_name.c_str());
+        if (Py_TYPE(m_ptr)->tp_name != nullptr) {
+            fprintf(stderr,
+                    " The failing %s call was triggered on a %s object.",
+                    function_name.c_str(),
+                    Py_TYPE(m_ptr)->tp_name);
+        }
+        fprintf(stderr, "\n");
+        fflush(stderr);
+        throw std::runtime_error(function_name + " PyGILState_Check() failure.");
+    }
+#endif
+
+#ifdef PYBIND11_HANDLE_REF_DEBUG
+    static std::size_t inc_ref_counter(std::size_t add) {
+        thread_local std::size_t counter = 0;
+        counter += add;
+        return counter;
+    }
+
+public:
+    static std::size_t inc_ref_counter() { return inc_ref_counter(0); }
+#endif
+};
+
+inline void set_error(const handle &type, const char *message) {
+    PyErr_SetString(type.ptr(), message);
+}
+
+inline void set_error(const handle &type, const handle &value) {
+    PyErr_SetObject(type.ptr(), value.ptr());
+}
+
+/** \rst
+    Holds a reference to a Python object (with reference counting)
+
+    Like `handle`, the `object` class is a thin wrapper around an arbitrary Python
+    object (i.e. a ``PyObject *`` in Python's C API). In contrast to `handle`, it
+    optionally increases the object's reference count upon construction, and it
+    *always* decreases the reference count when the `object` instance goes out of
+    scope and is destructed. When using `object` instances consistently, it is much
+    easier to get reference counting right at the first attempt.
+\endrst */
+class object : public handle {
+public:
+    object() = default;
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<object>() or reinterpret_steal<object>()")
+    object(handle h, bool is_borrowed) : handle(h) {
+        if (is_borrowed) {
+            inc_ref();
+        }
+    }
+    /// Copy constructor; always increases the reference count
+    object(const object &o) : handle(o) { inc_ref(); }
+    /// Move constructor; steals the object from ``other`` and preserves its reference count
+    object(object &&other) noexcept : handle(other) { other.m_ptr = nullptr; }
+    /// Destructor; automatically calls `handle::dec_ref()`
+    ~object() { dec_ref(); }
+
+    /** \rst
+        Resets the internal pointer to ``nullptr`` without decreasing the
+        object's reference count. The function returns a raw handle to the original
+        Python object.
+    \endrst */
+    handle release() {
+        PyObject *tmp = m_ptr;
+        m_ptr = nullptr;
+        return handle(tmp);
+    }
+
+    object &operator=(const object &other) {
+        // Skip inc_ref and dec_ref if both objects are the same
+        if (!this->is(other)) {
+            other.inc_ref();
+            // Use temporary variable to ensure `*this` remains valid while
+            // `Py_XDECREF` executes, in case `*this` is accessible from Python.
+            handle temp(m_ptr);
+            m_ptr = other.m_ptr;
+            temp.dec_ref();
+        }
+        return *this;
+    }
+
+    object &operator=(object &&other) noexcept {
+        if (this != &other) {
+            handle temp(m_ptr);
+            m_ptr = other.m_ptr;
+            other.m_ptr = nullptr;
+            temp.dec_ref();
+        }
+        return *this;
+    }
+
+#define PYBIND11_INPLACE_OP(iop)                                                                  \
+    object iop(object_api const &other) { return operator=(handle::iop(other)); }
+
+    PYBIND11_INPLACE_OP(operator+=)
+    PYBIND11_INPLACE_OP(operator-=)
+    PYBIND11_INPLACE_OP(operator*=)
+    PYBIND11_INPLACE_OP(operator/=)
+    PYBIND11_INPLACE_OP(operator|=)
+    PYBIND11_INPLACE_OP(operator&=)
+    PYBIND11_INPLACE_OP(operator^=)
+    PYBIND11_INPLACE_OP(operator<<=)
+    PYBIND11_INPLACE_OP(operator>>=)
+#undef PYBIND11_INPLACE_OP
+
+    // Calling cast() on an object lvalue just copies (via handle::cast)
+    template <typename T>
+    T cast() const &;
+    // Calling on an object rvalue does a move, if needed and/or possible
+    template <typename T>
+    T cast() &&;
+
+protected:
+    // Tags for choosing constructors from raw PyObject *
+    struct borrowed_t {};
+    struct stolen_t {};
+
+    /// @cond BROKEN
+    template <typename T>
+    friend T reinterpret_borrow(handle);
+    template <typename T>
+    friend T reinterpret_steal(handle);
+    /// @endcond
+
+public:
+    // Only accessible from derived classes and the reinterpret_* functions
+    object(handle h, borrowed_t) : handle(h) { inc_ref(); }
+    object(handle h, stolen_t) : handle(h) {}
+};
+
+/** \rst
+    Declare that a `handle` or ``PyObject *`` is a certain type and borrow the reference.
+    The target type ``T`` must be `object` or one of its derived classes. The function
+    doesn't do any conversions or checks. It's up to the user to make sure that the
+    target type is correct.
+
+    .. code-block:: cpp
+
+        PyObject *p = PyList_GetItem(obj, index);
+        py::object o = reinterpret_borrow<py::object>(p);
+        // or
+        py::tuple t = reinterpret_borrow<py::tuple>(p); // <-- `p` must be already be a `tuple`
+\endrst */
+template <typename T>
+T reinterpret_borrow(handle h) {
+    return {h, object::borrowed_t{}};
+}
+
+/** \rst
+    Like `reinterpret_borrow`, but steals the reference.
+
+     .. code-block:: cpp
+
+        PyObject *p = PyObject_Str(obj);
+        py::str s = reinterpret_steal<py::str>(p); // <-- `p` must be already be a `str`
+\endrst */
+template <typename T>
+T reinterpret_steal(handle h) {
+    return {h, object::stolen_t{}};
+}
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+// Equivalent to obj.__class__.__name__ (or obj.__name__ if obj is a class).
+inline const char *obj_class_name(PyObject *obj) {
+    if (PyType_Check(obj)) {
+        return reinterpret_cast<PyTypeObject *>(obj)->tp_name;
+    }
+    return Py_TYPE(obj)->tp_name;
+}
+
+std::string error_string();
+
+// The code in this struct is very unusual, to minimize the chances of
+// masking bugs (elsewhere) by errors during the error handling (here).
+// This is meant to be a lifeline for troubleshooting long-running processes
+// that crash under conditions that are virtually impossible to reproduce.
+// Low-level implementation alternatives are preferred to higher-level ones
+// that might raise cascading exceptions. Last-ditch-kind-of attempts are made
+// to report as much of the original error as possible, even if there are
+// secondary issues obtaining some of the details.
+struct error_fetch_and_normalize {
+    // This comment only applies to Python <= 3.11:
+    //     Immediate normalization is long-established behavior (starting with
+    //     https://github.com/pybind/pybind11/commit/135ba8deafb8bf64a15b24d1513899eb600e2011
+    //     from Sep 2016) and safest. Normalization could be deferred, but this could mask
+    //     errors elsewhere, the performance gain is very minor in typical situations
+    //     (usually the dominant bottleneck is EH unwinding), and the implementation here
+    //     would be more complex.
+    // Starting with Python 3.12, PyErr_Fetch() normalizes exceptions immediately.
+    // Any errors during normalization are tracked under __notes__.
+    explicit error_fetch_and_normalize(const char *called) {
+        PyErr_Fetch(&m_type.ptr(), &m_value.ptr(), &m_trace.ptr());
+        if (!m_type) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " called while "
+                            "Python error indicator not set.");
+        }
+        const char *exc_type_name_orig = detail::obj_class_name(m_type.ptr());
+        if (exc_type_name_orig == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to obtain the name "
+                            "of the original active exception type.");
+        }
+        m_lazy_error_string = exc_type_name_orig;
+#if PY_VERSION_HEX >= 0x030C0000
+        // The presence of __notes__ is likely due to exception normalization
+        // errors, although that is not necessarily true, therefore insert a
+        // hint only:
+        if (PyObject_HasAttrString(m_value.ptr(), "__notes__")) {
+            m_lazy_error_string += "[WITH __notes__]";
+        }
+#else
+        // PyErr_NormalizeException() may change the exception type if there are cascading
+        // failures. This can potentially be extremely confusing.
+        PyErr_NormalizeException(&m_type.ptr(), &m_value.ptr(), &m_trace.ptr());
+        if (m_type.ptr() == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to normalize the "
+                            "active exception.");
+        }
+        const char *exc_type_name_norm = detail::obj_class_name(m_type.ptr());
+        if (exc_type_name_norm == nullptr) {
+            pybind11_fail("Internal error: " + std::string(called)
+                          + " failed to obtain the name "
+                            "of the normalized active exception type.");
+        }
+#    if defined(PYPY_VERSION_NUM) && PYPY_VERSION_NUM < 0x07030a00
+        // This behavior runs the risk of masking errors in the error handling, but avoids a
+        // conflict with PyPy, which relies on the normalization here to change OSError to
+        // FileNotFoundError (https://github.com/pybind/pybind11/issues/4075).
+        m_lazy_error_string = exc_type_name_norm;
+#    else
+        if (exc_type_name_norm != m_lazy_error_string) {
+            std::string msg = std::string(called)
+                              + ": MISMATCH of original and normalized "
+                                "active exception types: ";
+            msg += "ORIGINAL ";
+            msg += m_lazy_error_string;
+            msg += " REPLACED BY ";
+            msg += exc_type_name_norm;
+            msg += ": " + format_value_and_trace();
+            pybind11_fail(msg);
+        }
+#    endif
+#endif
+    }
+
+    error_fetch_and_normalize(const error_fetch_and_normalize &) = delete;
+    error_fetch_and_normalize(error_fetch_and_normalize &&) = delete;
+
+    std::string format_value_and_trace() const {
+        std::string result;
+        std::string message_error_string;
+        if (m_value) {
+            auto value_str = reinterpret_steal<object>(PyObject_Str(m_value.ptr()));
+            constexpr const char *message_unavailable_exc
+                = "<MESSAGE UNAVAILABLE DUE TO ANOTHER EXCEPTION>";
+            if (!value_str) {
+                message_error_string = detail::error_string();
+                result = message_unavailable_exc;
+            } else {
+                // Not using `value_str.cast<std::string>()`, to not potentially throw a secondary
+                // error_already_set that will then result in process termination (#4288).
+                auto value_bytes = reinterpret_steal<object>(
+                    PyUnicode_AsEncodedString(value_str.ptr(), "utf-8", "backslashreplace"));
+                if (!value_bytes) {
+                    message_error_string = detail::error_string();
+                    result = message_unavailable_exc;
+                } else {
+                    char *buffer = nullptr;
+                    Py_ssize_t length = 0;
+                    if (PyBytes_AsStringAndSize(value_bytes.ptr(), &buffer, &length) == -1) {
+                        message_error_string = detail::error_string();
+                        result = message_unavailable_exc;
+                    } else {
+                        result = std::string(buffer, static_cast<std::size_t>(length));
+                    }
+                }
+            }
+#if PY_VERSION_HEX >= 0x030B0000
+            auto notes
+                = reinterpret_steal<object>(PyObject_GetAttrString(m_value.ptr(), "__notes__"));
+            if (!notes) {
+                PyErr_Clear(); // No notes is good news.
+            } else {
+                auto len_notes = PyList_Size(notes.ptr());
+                if (len_notes < 0) {
+                    result += "\nFAILURE obtaining len(__notes__): " + detail::error_string();
+                } else {
+                    result += "\n__notes__ (len=" + std::to_string(len_notes) + "):";
+                    for (ssize_t i = 0; i < len_notes; i++) {
+                        PyObject *note = PyList_GET_ITEM(notes.ptr(), i);
+                        auto note_bytes = reinterpret_steal<object>(
+                            PyUnicode_AsEncodedString(note, "utf-8", "backslashreplace"));
+                        if (!note_bytes) {
+                            result += "\nFAILURE obtaining __notes__[" + std::to_string(i)
+                                      + "]: " + detail::error_string();
+                        } else {
+                            char *buffer = nullptr;
+                            Py_ssize_t length = 0;
+                            if (PyBytes_AsStringAndSize(note_bytes.ptr(), &buffer, &length)
+                                == -1) {
+                                result += "\nFAILURE formatting __notes__[" + std::to_string(i)
+                                          + "]: " + detail::error_string();
+                            } else {
+                                result += '\n';
+                                result += std::string(buffer, static_cast<std::size_t>(length));
+                            }
+                        }
+                    }
+                }
+            }
+#endif
+        } else {
+            result = "<MESSAGE UNAVAILABLE>";
+        }
+        if (result.empty()) {
+            result = "<EMPTY MESSAGE>";
+        }
+
+        bool have_trace = false;
+        if (m_trace) {
+#if !defined(PYPY_VERSION)
+            auto *tb = reinterpret_cast<PyTracebackObject *>(m_trace.ptr());
+
+            // Get the deepest trace possible.
+            while (tb->tb_next) {
+                tb = tb->tb_next;
+            }
+
+            PyFrameObject *frame = tb->tb_frame;
+            Py_XINCREF(frame);
+            result += "\n\nAt:\n";
+            while (frame) {
+#    if PY_VERSION_HEX >= 0x030900B1
+                PyCodeObject *f_code = PyFrame_GetCode(frame);
+#    else
+                PyCodeObject *f_code = frame->f_code;
+                Py_INCREF(f_code);
+#    endif
+                int lineno = PyFrame_GetLineNumber(frame);
+                result += "  ";
+                result += handle(f_code->co_filename).cast<std::string>();
+                result += '(';
+                result += std::to_string(lineno);
+                result += "): ";
+                result += handle(f_code->co_name).cast<std::string>();
+                result += '\n';
+                Py_DECREF(f_code);
+#    if PY_VERSION_HEX >= 0x030900B1
+                auto *b_frame = PyFrame_GetBack(frame);
+#    else
+                auto *b_frame = frame->f_back;
+                Py_XINCREF(b_frame);
+#    endif
+                Py_DECREF(frame);
+                frame = b_frame;
+            }
+
+            have_trace = true;
+#endif //! defined(PYPY_VERSION)
+        }
+
+        if (!message_error_string.empty()) {
+            if (!have_trace) {
+                result += '\n';
+            }
+            result += "\nMESSAGE UNAVAILABLE DUE TO EXCEPTION: " + message_error_string;
+        }
+
+        return result;
+    }
+
+    std::string const &error_string() const {
+        if (!m_lazy_error_string_completed) {
+            m_lazy_error_string += ": " + format_value_and_trace();
+            m_lazy_error_string_completed = true;
+        }
+        return m_lazy_error_string;
+    }
+
+    void restore() {
+        if (m_restore_called) {
+            pybind11_fail("Internal error: pybind11::detail::error_fetch_and_normalize::restore() "
+                          "called a second time. ORIGINAL ERROR: "
+                          + error_string());
+        }
+        PyErr_Restore(m_type.inc_ref().ptr(), m_value.inc_ref().ptr(), m_trace.inc_ref().ptr());
+        m_restore_called = true;
+    }
+
+    bool matches(handle exc) const {
+        return (PyErr_GivenExceptionMatches(m_type.ptr(), exc.ptr()) != 0);
+    }
+
+    // Not protecting these for simplicity.
+    object m_type, m_value, m_trace;
+
+private:
+    // Only protecting invariants.
+    mutable std::string m_lazy_error_string;
+    mutable bool m_lazy_error_string_completed = false;
+    mutable bool m_restore_called = false;
+};
+
+inline std::string error_string() {
+    return error_fetch_and_normalize("pybind11::detail::error_string").error_string();
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+/// Fetch and hold an error which was already set in Python.  An instance of this is typically
+/// thrown to propagate python-side errors back through C++ which can either be caught manually or
+/// else falls back to the function dispatcher (which then raises the captured error back to
+/// python).
+class PYBIND11_EXPORT_EXCEPTION error_already_set : public std::exception {
+public:
+    /// Fetches the current Python exception (using PyErr_Fetch()), which will clear the
+    /// current Python error indicator.
+    error_already_set()
+        : m_fetched_error{new detail::error_fetch_and_normalize("pybind11::error_already_set"),
+                          m_fetched_error_deleter} {}
+
+    /// The what() result is built lazily on demand.
+    /// WARNING: This member function needs to acquire the Python GIL. This can lead to
+    ///          crashes (undefined behavior) if the Python interpreter is finalizing.
+    const char *what() const noexcept override;
+
+    /// Restores the currently-held Python error (which will clear the Python error indicator first
+    /// if already set).
+    /// NOTE: This member function will always restore the normalized exception, which may or may
+    ///       not be the original Python exception.
+    /// WARNING: The GIL must be held when this member function is called!
+    void restore() { m_fetched_error->restore(); }
+
+    /// If it is impossible to raise the currently-held error, such as in a destructor, we can
+    /// write it out using Python's unraisable hook (`sys.unraisablehook`). The error context
+    /// should be some object whose `repr()` helps identify the location of the error. Python
+    /// already knows the type and value of the error, so there is no need to repeat that.
+    void discard_as_unraisable(object err_context) {
+        restore();
+        PyErr_WriteUnraisable(err_context.ptr());
+    }
+    /// An alternate version of `discard_as_unraisable()`, where a string provides information on
+    /// the location of the error. For example, `__func__` could be helpful.
+    /// WARNING: The GIL must be held when this member function is called!
+    void discard_as_unraisable(const char *err_context) {
+        discard_as_unraisable(reinterpret_steal<object>(PYBIND11_FROM_STRING(err_context)));
+    }
+
+    // Does nothing; provided for backwards compatibility.
+    PYBIND11_DEPRECATED("Use of error_already_set.clear() is deprecated")
+    void clear() {}
+
+    /// Check if the currently trapped error type matches the given Python exception class (or a
+    /// subclass thereof).  May also be passed a tuple to search for any exception class matches in
+    /// the given tuple.
+    bool matches(handle exc) const { return m_fetched_error->matches(exc); }
+
+    const object &type() const { return m_fetched_error->m_type; }
+    const object &value() const { return m_fetched_error->m_value; }
+    const object &trace() const { return m_fetched_error->m_trace; }
+
+private:
+    std::shared_ptr<detail::error_fetch_and_normalize> m_fetched_error;
+
+    /// WARNING: This custom deleter needs to acquire the Python GIL. This can lead to
+    ///          crashes (undefined behavior) if the Python interpreter is finalizing.
+    static void m_fetched_error_deleter(detail::error_fetch_and_normalize *raw_ptr);
+};
+
+/// Replaces the current Python error indicator with the chosen error, performing a
+/// 'raise from' to indicate that the chosen error was caused by the original error.
+inline void raise_from(PyObject *type, const char *message) {
+    // Based on _PyErr_FormatVFromCause:
+    // https://github.com/python/cpython/blob/467ab194fc6189d9f7310c89937c51abeac56839/Python/errors.c#L405
+    // See https://github.com/pybind/pybind11/pull/2112 for details.
+    PyObject *exc = nullptr, *val = nullptr, *val2 = nullptr, *tb = nullptr;
+
+    assert(PyErr_Occurred());
+    PyErr_Fetch(&exc, &val, &tb);
+    PyErr_NormalizeException(&exc, &val, &tb);
+    if (tb != nullptr) {
+        PyException_SetTraceback(val, tb);
+        Py_DECREF(tb);
+    }
+    Py_DECREF(exc);
+    assert(!PyErr_Occurred());
+
+    PyErr_SetString(type, message);
+
+    PyErr_Fetch(&exc, &val2, &tb);
+    PyErr_NormalizeException(&exc, &val2, &tb);
+    Py_INCREF(val);
+    PyException_SetCause(val2, val);
+    PyException_SetContext(val2, val);
+    PyErr_Restore(exc, val2, tb);
+}
+
+/// Sets the current Python error indicator with the chosen error, performing a 'raise from'
+/// from the error contained in error_already_set to indicate that the chosen error was
+/// caused by the original error.
+inline void raise_from(error_already_set &err, PyObject *type, const char *message) {
+    err.restore();
+    raise_from(type, message);
+}
+
+/** \defgroup python_builtins const_name
+    Unless stated otherwise, the following C++ functions behave the same
+    as their Python counterparts.
+ */
+
+/** \ingroup python_builtins
+    \rst
+    Return true if ``obj`` is an instance of ``T``. Type ``T`` must be a subclass of
+    `object` or a class which was exposed to Python as ``py::class_<T>``.
+\endrst */
+template <typename T, detail::enable_if_t<std::is_base_of<object, T>::value, int> = 0>
+bool isinstance(handle obj) {
+    return T::check_(obj);
+}
+
+template <typename T, detail::enable_if_t<!std::is_base_of<object, T>::value, int> = 0>
+bool isinstance(handle obj) {
+    return detail::isinstance_generic(obj, typeid(T));
+}
+
+template <>
+inline bool isinstance<handle>(handle) = delete;
+template <>
+inline bool isinstance<object>(handle obj) {
+    return obj.ptr() != nullptr;
+}
+
+/// \ingroup python_builtins
+/// Return true if ``obj`` is an instance of the ``type``.
+inline bool isinstance(handle obj, handle type) {
+    const auto result = PyObject_IsInstance(obj.ptr(), type.ptr());
+    if (result == -1) {
+        throw error_already_set();
+    }
+    return result != 0;
+}
+
+/// \addtogroup python_builtins
+/// @{
+inline bool hasattr(handle obj, handle name) {
+    return PyObject_HasAttr(obj.ptr(), name.ptr()) == 1;
+}
+
+inline bool hasattr(handle obj, const char *name) {
+    return PyObject_HasAttrString(obj.ptr(), name) == 1;
+}
+
+inline void delattr(handle obj, handle name) {
+    if (PyObject_DelAttr(obj.ptr(), name.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline void delattr(handle obj, const char *name) {
+    if (PyObject_DelAttrString(obj.ptr(), name) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline object getattr(handle obj, handle name) {
+    PyObject *result = PyObject_GetAttr(obj.ptr(), name.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+inline object getattr(handle obj, const char *name) {
+    PyObject *result = PyObject_GetAttrString(obj.ptr(), name);
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<object>(result);
+}
+
+inline object getattr(handle obj, handle name, handle default_) {
+    if (PyObject *result = PyObject_GetAttr(obj.ptr(), name.ptr())) {
+        return reinterpret_steal<object>(result);
+    }
+    PyErr_Clear();
+    return reinterpret_borrow<object>(default_);
+}
+
+inline object getattr(handle obj, const char *name, handle default_) {
+    if (PyObject *result = PyObject_GetAttrString(obj.ptr(), name)) {
+        return reinterpret_steal<object>(result);
+    }
+    PyErr_Clear();
+    return reinterpret_borrow<object>(default_);
+}
+
+inline void setattr(handle obj, handle name, handle value) {
+    if (PyObject_SetAttr(obj.ptr(), name.ptr(), value.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline void setattr(handle obj, const char *name, handle value) {
+    if (PyObject_SetAttrString(obj.ptr(), name, value.ptr()) != 0) {
+        throw error_already_set();
+    }
+}
+
+inline ssize_t hash(handle obj) {
+    auto h = PyObject_Hash(obj.ptr());
+    if (h == -1) {
+        throw error_already_set();
+    }
+    return h;
+}
+
+/// @} python_builtins
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+inline handle get_function(handle value) {
+    if (value) {
+        if (PyInstanceMethod_Check(value.ptr())) {
+            value = PyInstanceMethod_GET_FUNCTION(value.ptr());
+        } else if (PyMethod_Check(value.ptr())) {
+            value = PyMethod_GET_FUNCTION(value.ptr());
+        }
+    }
+    return value;
+}
+
+// Reimplementation of python's dict helper functions to ensure that exceptions
+// aren't swallowed (see #2862)
+
+// copied from cpython _PyDict_GetItemStringWithError
+inline PyObject *dict_getitemstring(PyObject *v, const char *key) {
+    PyObject *kv = nullptr, *rv = nullptr;
+    kv = PyUnicode_FromString(key);
+    if (kv == nullptr) {
+        throw error_already_set();
+    }
+
+    rv = PyDict_GetItemWithError(v, kv);
+    Py_DECREF(kv);
+    if (rv == nullptr && PyErr_Occurred()) {
+        throw error_already_set();
+    }
+    return rv;
+}
+
+inline PyObject *dict_getitem(PyObject *v, PyObject *key) {
+    PyObject *rv = PyDict_GetItemWithError(v, key);
+    if (rv == nullptr && PyErr_Occurred()) {
+        throw error_already_set();
+    }
+    return rv;
+}
+
+inline PyObject *dict_getitemstringref(PyObject *v, const char *key) {
+#if PY_VERSION_HEX >= 0x030D0000
+    PyObject *rv;
+    if (PyDict_GetItemStringRef(v, key, &rv) < 0) {
+        throw error_already_set();
+    }
+    return rv;
+#else
+    PyObject *rv = dict_getitemstring(v, key);
+    if (rv == nullptr && PyErr_Occurred()) {
+        throw error_already_set();
+    }
+    Py_XINCREF(rv);
+    return rv;
+#endif
+}
+
+// Helper aliases/functions to support implicit casting of values given to python
+// accessors/methods. When given a pyobject, this simply returns the pyobject as-is; for other C++
+// type, the value goes through pybind11::cast(obj) to convert it to an `object`.
+template <typename T, enable_if_t<is_pyobject<T>::value, int> = 0>
+auto object_or_cast(T &&o) -> decltype(std::forward<T>(o)) {
+    return std::forward<T>(o);
+}
+// The following casting version is implemented in cast.h:
+template <typename T, enable_if_t<!is_pyobject<T>::value, int> = 0>
+object object_or_cast(T &&o);
+// Match a PyObject*, which we want to convert directly to handle via its converting constructor
+inline handle object_or_cast(PyObject *ptr) { return ptr; }
+
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(4522) // warning C4522: multiple assignment operators specified
+template <typename Policy>
+class accessor : public object_api<accessor<Policy>> {
+    using key_type = typename Policy::key_type;
+
+public:
+    accessor(handle obj, key_type key) : obj(obj), key(std::move(key)) {}
+    accessor(const accessor &) = default;
+    accessor(accessor &&) noexcept = default;
+
+    // accessor overload required to override default assignment operator (templates are not
+    // allowed to replace default compiler-generated assignments).
+    void operator=(const accessor &a) && { std::move(*this).operator=(handle(a)); }
+    void operator=(const accessor &a) & { operator=(handle(a)); }
+
+    template <typename T>
+    void operator=(T &&value) && {
+        Policy::set(obj, key, object_or_cast(std::forward<T>(value)));
+    }
+    template <typename T>
+    void operator=(T &&value) & {
+        get_cache() = ensure_object(object_or_cast(std::forward<T>(value)));
+    }
+
+    template <typename T = Policy>
+    PYBIND11_DEPRECATED(
+        "Use of obj.attr(...) as bool is deprecated in favor of pybind11::hasattr(obj, ...)")
+    explicit
+    operator enable_if_t<std::is_same<T, accessor_policies::str_attr>::value
+                             || std::is_same<T, accessor_policies::obj_attr>::value,
+                         bool>() const {
+        return hasattr(obj, key);
+    }
+    template <typename T = Policy>
+    PYBIND11_DEPRECATED("Use of obj[key] as bool is deprecated in favor of obj.contains(key)")
+    explicit
+    operator enable_if_t<std::is_same<T, accessor_policies::generic_item>::value, bool>() const {
+        return obj.contains(key);
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator object() const { return get_cache(); }
+    PyObject *ptr() const { return get_cache().ptr(); }
+    template <typename T>
+    T cast() const {
+        return get_cache().template cast<T>();
+    }
+
+private:
+    static object ensure_object(object &&o) { return std::move(o); }
+    static object ensure_object(handle h) { return reinterpret_borrow<object>(h); }
+
+    object &get_cache() const {
+        if (!cache) {
+            cache = Policy::get(obj, key);
+        }
+        return cache;
+    }
+
+private:
+    handle obj;
+    key_type key;
+    mutable object cache;
+};
+PYBIND11_WARNING_POP
+
+PYBIND11_NAMESPACE_BEGIN(accessor_policies)
+struct obj_attr {
+    using key_type = object;
+    static object get(handle obj, handle key) { return getattr(obj, key); }
+    static void set(handle obj, handle key, handle val) { setattr(obj, key, val); }
+};
+
+struct str_attr {
+    using key_type = const char *;
+    static object get(handle obj, const char *key) { return getattr(obj, key); }
+    static void set(handle obj, const char *key, handle val) { setattr(obj, key, val); }
+};
+
+struct generic_item {
+    using key_type = object;
+
+    static object get(handle obj, handle key) {
+        PyObject *result = PyObject_GetItem(obj.ptr(), key.ptr());
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+    static void set(handle obj, handle key, handle val) {
+        if (PyObject_SetItem(obj.ptr(), key.ptr(), val.ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct sequence_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PySequence_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_steal<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PySequence_SetItem does not steal a reference to 'val'
+        if (PySequence_SetItem(obj.ptr(), ssize_t_cast(index), val.ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct list_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PyList_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_borrow<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PyList_SetItem steals a reference to 'val'
+        if (PyList_SetItem(obj.ptr(), ssize_t_cast(index), val.inc_ref().ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+struct tuple_item {
+    using key_type = size_t;
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static object get(handle obj, const IdxType &index) {
+        PyObject *result = PyTuple_GetItem(obj.ptr(), ssize_t_cast(index));
+        if (!result) {
+            throw error_already_set();
+        }
+        return reinterpret_borrow<object>(result);
+    }
+
+    template <typename IdxType, detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    static void set(handle obj, const IdxType &index, handle val) {
+        // PyTuple_SetItem steals a reference to 'val'
+        if (PyTuple_SetItem(obj.ptr(), ssize_t_cast(index), val.inc_ref().ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+PYBIND11_NAMESPACE_END(accessor_policies)
+
+/// STL iterator template used for tuple, list, sequence and dict
+template <typename Policy>
+class generic_iterator : public Policy {
+    using It = generic_iterator;
+
+public:
+    using difference_type = ssize_t;
+    using iterator_category = typename Policy::iterator_category;
+    using value_type = typename Policy::value_type;
+    using reference = typename Policy::reference;
+    using pointer = typename Policy::pointer;
+
+    generic_iterator() = default;
+    generic_iterator(handle seq, ssize_t index) : Policy(seq, index) {}
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator*() const { return Policy::dereference(); }
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator[](difference_type n) const { return *(*this + n); }
+    pointer operator->() const { return **this; }
+
+    It &operator++() {
+        Policy::increment();
+        return *this;
+    }
+    It operator++(int) {
+        auto copy = *this;
+        Policy::increment();
+        return copy;
+    }
+    It &operator--() {
+        Policy::decrement();
+        return *this;
+    }
+    It operator--(int) {
+        auto copy = *this;
+        Policy::decrement();
+        return copy;
+    }
+    It &operator+=(difference_type n) {
+        Policy::advance(n);
+        return *this;
+    }
+    It &operator-=(difference_type n) {
+        Policy::advance(-n);
+        return *this;
+    }
+
+    friend It operator+(const It &a, difference_type n) {
+        auto copy = a;
+        return copy += n;
+    }
+    friend It operator+(difference_type n, const It &b) { return b + n; }
+    friend It operator-(const It &a, difference_type n) {
+        auto copy = a;
+        return copy -= n;
+    }
+    friend difference_type operator-(const It &a, const It &b) { return a.distance_to(b); }
+
+    friend bool operator==(const It &a, const It &b) { return a.equal(b); }
+    friend bool operator!=(const It &a, const It &b) { return !(a == b); }
+    friend bool operator<(const It &a, const It &b) { return b - a > 0; }
+    friend bool operator>(const It &a, const It &b) { return b < a; }
+    friend bool operator>=(const It &a, const It &b) { return !(a < b); }
+    friend bool operator<=(const It &a, const It &b) { return !(a > b); }
+};
+
+PYBIND11_NAMESPACE_BEGIN(iterator_policies)
+/// Quick proxy class needed to implement ``operator->`` for iterators which can't return pointers
+template <typename T>
+struct arrow_proxy {
+    T value;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    arrow_proxy(T &&value) noexcept : value(std::move(value)) {}
+    T *operator->() const { return &value; }
+};
+
+/// Lightweight iterator policy using just a simple pointer: see ``PySequence_Fast_ITEMS``
+class sequence_fast_readonly {
+protected:
+    using iterator_category = std::random_access_iterator_tag;
+    using value_type = handle;
+    using reference = const handle; // PR #3263
+    using pointer = arrow_proxy<const handle>;
+
+    sequence_fast_readonly(handle obj, ssize_t n) : ptr(PySequence_Fast_ITEMS(obj.ptr()) + n) {}
+    sequence_fast_readonly() = default;
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference dereference() const { return *ptr; }
+    void increment() { ++ptr; }
+    void decrement() { --ptr; }
+    void advance(ssize_t n) { ptr += n; }
+    bool equal(const sequence_fast_readonly &b) const { return ptr == b.ptr; }
+    ssize_t distance_to(const sequence_fast_readonly &b) const { return ptr - b.ptr; }
+
+private:
+    PyObject **ptr;
+};
+
+/// Full read and write access using the sequence protocol: see ``detail::sequence_accessor``
+class sequence_slow_readwrite {
+protected:
+    using iterator_category = std::random_access_iterator_tag;
+    using value_type = object;
+    using reference = sequence_accessor;
+    using pointer = arrow_proxy<const sequence_accessor>;
+
+    sequence_slow_readwrite(handle obj, ssize_t index) : obj(obj), index(index) {}
+    sequence_slow_readwrite() = default;
+
+    reference dereference() const { return {obj, static_cast<size_t>(index)}; }
+    void increment() { ++index; }
+    void decrement() { --index; }
+    void advance(ssize_t n) { index += n; }
+    bool equal(const sequence_slow_readwrite &b) const { return index == b.index; }
+    ssize_t distance_to(const sequence_slow_readwrite &b) const { return index - b.index; }
+
+private:
+    handle obj;
+    ssize_t index;
+};
+
+/// Python's dictionary protocol permits this to be a forward iterator
+class dict_readonly {
+protected:
+    using iterator_category = std::forward_iterator_tag;
+    using value_type = std::pair<handle, handle>;
+    using reference = const value_type; // PR #3263
+    using pointer = arrow_proxy<const value_type>;
+
+    dict_readonly() = default;
+    dict_readonly(handle obj, ssize_t pos) : obj(obj), pos(pos) { increment(); }
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference dereference() const { return {key, value}; }
+    void increment() {
+        if (PyDict_Next(obj.ptr(), &pos, &key, &value) == 0) {
+            pos = -1;
+        }
+    }
+    bool equal(const dict_readonly &b) const { return pos == b.pos; }
+
+private:
+    handle obj;
+    PyObject *key = nullptr, *value = nullptr;
+    ssize_t pos = -1;
+};
+PYBIND11_NAMESPACE_END(iterator_policies)
+
+#if !defined(PYPY_VERSION)
+using tuple_iterator = generic_iterator<iterator_policies::sequence_fast_readonly>;
+using list_iterator = generic_iterator<iterator_policies::sequence_fast_readonly>;
+#else
+using tuple_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+using list_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+#endif
+
+using sequence_iterator = generic_iterator<iterator_policies::sequence_slow_readwrite>;
+using dict_iterator = generic_iterator<iterator_policies::dict_readonly>;
+
+inline bool PyIterable_Check(PyObject *obj) {
+    PyObject *iter = PyObject_GetIter(obj);
+    if (iter) {
+        Py_DECREF(iter);
+        return true;
+    }
+    PyErr_Clear();
+    return false;
+}
+
+inline bool PyNone_Check(PyObject *o) { return o == Py_None; }
+inline bool PyEllipsis_Check(PyObject *o) { return o == Py_Ellipsis; }
+
+#ifdef PYBIND11_STR_LEGACY_PERMISSIVE
+inline bool PyUnicode_Check_Permissive(PyObject *o) {
+    return PyUnicode_Check(o) || PYBIND11_BYTES_CHECK(o);
+}
+#    define PYBIND11_STR_CHECK_FUN detail::PyUnicode_Check_Permissive
+#else
+#    define PYBIND11_STR_CHECK_FUN PyUnicode_Check
+#endif
+
+inline bool PyStaticMethod_Check(PyObject *o) { return o->ob_type == &PyStaticMethod_Type; }
+
+class kwargs_proxy : public handle {
+public:
+    explicit kwargs_proxy(handle h) : handle(h) {}
+};
+
+class args_proxy : public handle {
+public:
+    explicit args_proxy(handle h) : handle(h) {}
+    kwargs_proxy operator*() const { return kwargs_proxy(*this); }
+};
+
+/// Python argument categories (using PEP 448 terms)
+template <typename T>
+using is_keyword = std::is_base_of<arg, T>;
+template <typename T>
+using is_s_unpacking = std::is_same<args_proxy, T>; // * unpacking
+template <typename T>
+using is_ds_unpacking = std::is_same<kwargs_proxy, T>; // ** unpacking
+template <typename T>
+using is_positional = satisfies_none_of<T, is_keyword, is_s_unpacking, is_ds_unpacking>;
+template <typename T>
+using is_keyword_or_ds = satisfies_any_of<T, is_keyword, is_ds_unpacking>;
+
+// Call argument collector forward declarations
+template <return_value_policy policy = return_value_policy::automatic_reference>
+class simple_collector;
+template <return_value_policy policy = return_value_policy::automatic_reference>
+class unpacking_collector;
+
+PYBIND11_NAMESPACE_END(detail)
+
+// TODO: After the deprecated constructors are removed, this macro can be simplified by
+//       inheriting ctors: `using Parent::Parent`. It's not an option right now because
+//       the `using` statement triggers the parent deprecation warning even if the ctor
+//       isn't even used.
+#define PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                            \
+public:                                                                                           \
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<" #Name ">() or reinterpret_steal<" #Name ">()")  \
+    Name(handle h, bool is_borrowed)                                                              \
+        : Parent(is_borrowed ? Parent(h, borrowed_t{}) : Parent(h, stolen_t{})) {}                \
+    Name(handle h, borrowed_t) : Parent(h, borrowed_t{}) {}                                       \
+    Name(handle h, stolen_t) : Parent(h, stolen_t{}) {}                                           \
+    PYBIND11_DEPRECATED("Use py::isinstance<py::python_type>(obj) instead")                       \
+    bool check() const { return m_ptr != nullptr && (CheckFun(m_ptr) != 0); }                     \
+    static bool check_(handle h) { return h.ptr() != nullptr && CheckFun(h.ptr()); }              \
+    template <typename Policy_> /* NOLINTNEXTLINE(google-explicit-constructor) */                 \
+    Name(const ::pybind11::detail::accessor<Policy_> &a) : Name(object(a)) {}
+
+#define PYBIND11_OBJECT_CVT(Name, Parent, CheckFun, ConvertFun)                                   \
+    PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                                \
+    /* This is deliberately not 'explicit' to allow implicit conversion from object: */           \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(const object &o)                                                                         \
+        : Parent(check_(o) ? o.inc_ref().ptr() : ConvertFun(o.ptr()), stolen_t{}) {               \
+        if (!m_ptr)                                                                               \
+            throw ::pybind11::error_already_set();                                                \
+    }                                                                                             \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(object &&o) : Parent(check_(o) ? o.release().ptr() : ConvertFun(o.ptr()), stolen_t{}) {  \
+        if (!m_ptr)                                                                               \
+            throw ::pybind11::error_already_set();                                                \
+    }
+
+#define PYBIND11_OBJECT_CVT_DEFAULT(Name, Parent, CheckFun, ConvertFun)                           \
+    PYBIND11_OBJECT_CVT(Name, Parent, CheckFun, ConvertFun)                                       \
+    Name() = default;
+
+#define PYBIND11_OBJECT_CHECK_FAILED(Name, o_ptr)                                                 \
+    ::pybind11::type_error("Object of type '"                                                     \
+                           + ::pybind11::detail::get_fully_qualified_tp_name(Py_TYPE(o_ptr))      \
+                           + "' is not an instance of '" #Name "'")
+
+#define PYBIND11_OBJECT(Name, Parent, CheckFun)                                                   \
+    PYBIND11_OBJECT_COMMON(Name, Parent, CheckFun)                                                \
+    /* This is deliberately not 'explicit' to allow implicit conversion from object: */           \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(const object &o) : Parent(o) {                                                           \
+        if (m_ptr && !check_(m_ptr))                                                              \
+            throw PYBIND11_OBJECT_CHECK_FAILED(Name, m_ptr);                                      \
+    }                                                                                             \
+    /* NOLINTNEXTLINE(google-explicit-constructor) */                                             \
+    Name(object &&o) : Parent(std::move(o)) {                                                     \
+        if (m_ptr && !check_(m_ptr))                                                              \
+            throw PYBIND11_OBJECT_CHECK_FAILED(Name, m_ptr);                                      \
+    }
+
+#define PYBIND11_OBJECT_DEFAULT(Name, Parent, CheckFun)                                           \
+    PYBIND11_OBJECT(Name, Parent, CheckFun)                                                       \
+    Name() = default;
+
+/// \addtogroup pytypes
+/// @{
+
+/** \rst
+    Wraps a Python iterator so that it can also be used as a C++ input iterator
+
+    Caveat: copying an iterator does not (and cannot) clone the internal
+    state of the Python iterable. This also applies to the post-increment
+    operator. This iterator should only be used to retrieve the current
+    value using ``operator*()``.
+\endrst */
+class iterator : public object {
+public:
+    using iterator_category = std::input_iterator_tag;
+    using difference_type = ssize_t;
+    using value_type = handle;
+    using reference = const handle; // PR #3263
+    using pointer = const handle *;
+
+    PYBIND11_OBJECT_DEFAULT(iterator, object, PyIter_Check)
+
+    iterator &operator++() {
+        advance();
+        return *this;
+    }
+
+    iterator operator++(int) {
+        auto rv = *this;
+        advance();
+        return rv;
+    }
+
+    // NOLINTNEXTLINE(readability-const-return-type) // PR #3263
+    reference operator*() const {
+        if (m_ptr && !value.ptr()) {
+            auto &self = const_cast<iterator &>(*this);
+            self.advance();
+        }
+        return value;
+    }
+
+    pointer operator->() const {
+        operator*();
+        return &value;
+    }
+
+    /** \rst
+         The value which marks the end of the iteration. ``it == iterator::sentinel()``
+         is equivalent to catching ``StopIteration`` in Python.
+
+         .. code-block:: cpp
+
+             void foo(py::iterator it) {
+                 while (it != py::iterator::sentinel()) {
+                    // use `*it`
+                    ++it;
+                 }
+             }
+    \endrst */
+    static iterator sentinel() { return {}; }
+
+    friend bool operator==(const iterator &a, const iterator &b) { return a->ptr() == b->ptr(); }
+    friend bool operator!=(const iterator &a, const iterator &b) { return a->ptr() != b->ptr(); }
+
+private:
+    void advance() {
+        value = reinterpret_steal<object>(PyIter_Next(m_ptr));
+        if (value.ptr() == nullptr && PyErr_Occurred()) {
+            throw error_already_set();
+        }
+    }
+
+private:
+    object value = {};
+};
+
+class type : public object {
+public:
+    PYBIND11_OBJECT(type, object, PyType_Check)
+
+    /// Return a type handle from a handle or an object
+    static handle handle_of(handle h) { return handle((PyObject *) Py_TYPE(h.ptr())); }
+
+    /// Return a type object from a handle or an object
+    static type of(handle h) { return type(type::handle_of(h), borrowed_t{}); }
+
+    // Defined in pybind11/cast.h
+    /// Convert C++ type to handle if previously registered. Does not convert
+    /// standard types, like int, float. etc. yet.
+    /// See https://github.com/pybind/pybind11/issues/2486
+    template <typename T>
+    static handle handle_of();
+
+    /// Convert C++ type to type if previously registered. Does not convert
+    /// standard types, like int, float. etc. yet.
+    /// See https://github.com/pybind/pybind11/issues/2486
+    template <typename T>
+    static type of() {
+        return type(type::handle_of<T>(), borrowed_t{});
+    }
+};
+
+class iterable : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(iterable, object, detail::PyIterable_Check)
+};
+
+class bytes;
+
+class str : public object {
+public:
+    PYBIND11_OBJECT_CVT(str, object, PYBIND11_STR_CHECK_FUN, raw_str)
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    str(const char *c, const SzType &n)
+        : object(PyUnicode_FromStringAndSize(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Could not allocate string object!");
+        }
+    }
+
+    // 'explicit' is explicitly omitted from the following constructors to allow implicit
+    // conversion to py::str from C++ string-like objects
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(const char *c = "") : object(PyUnicode_FromString(c), stolen_t{}) {
+        if (!m_ptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Could not allocate string object!");
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(const std::string &s) : str(s.data(), s.size()) {}
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    // enable_if is needed to avoid "ambiguous conversion" errors (see PR #3521).
+    template <typename T, detail::enable_if_t<std::is_same<T, std::string_view>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(T s) : str(s.data(), s.size()) {}
+
+#    ifdef PYBIND11_HAS_U8STRING
+    // reinterpret_cast here is safe (C++20 guarantees char8_t has the same size/alignment as char)
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    str(std::u8string_view s) : str(reinterpret_cast<const char *>(s.data()), s.size()) {}
+#    endif
+
+#endif
+
+    explicit str(const bytes &b);
+
+    /** \rst
+        Return a string representation of the object. This is analogous to
+        the ``str()`` function in Python.
+    \endrst */
+    explicit str(handle h) : object(raw_str(h.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string() const {
+        object temp = *this;
+        if (PyUnicode_Check(m_ptr)) {
+            temp = reinterpret_steal<object>(PyUnicode_AsUTF8String(m_ptr));
+            if (!temp) {
+                throw error_already_set();
+            }
+        }
+        char *buffer = nullptr;
+        ssize_t length = 0;
+        if (PyBytes_AsStringAndSize(temp.ptr(), &buffer, &length) != 0) {
+            throw error_already_set();
+        }
+        return std::string(buffer, (size_t) length);
+    }
+
+    template <typename... Args>
+    str format(Args &&...args) const {
+        return attr("format")(std::forward<Args>(args)...);
+    }
+
+private:
+    /// Return string representation -- always returns a new reference, even if already a str
+    static PyObject *raw_str(PyObject *op) {
+        PyObject *str_value = PyObject_Str(op);
+        return str_value;
+    }
+};
+/// @} pytypes
+
+inline namespace literals {
+/** \rst
+    String literal version of `str`
+ \endrst */
+inline str
+#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ < 5
+operator"" _s // gcc 4.8.5 insists on having a space (hard error).
+#else
+operator""_s // clang 17 generates a deprecation warning if there is a space.
+#endif
+    (const char *s, size_t size) {
+    return {s, size};
+}
+} // namespace literals
+
+/// \addtogroup pytypes
+/// @{
+class bytes : public object {
+public:
+    PYBIND11_OBJECT(bytes, object, PYBIND11_BYTES_CHECK)
+
+    // Allow implicit conversion:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(const char *c = "") : object(PYBIND11_BYTES_FROM_STRING(c), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytes object!");
+        }
+    }
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    bytes(const char *c, const SzType &n)
+        : object(PYBIND11_BYTES_FROM_STRING_AND_SIZE(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytes object!");
+        }
+    }
+
+    // Allow implicit conversion:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(const std::string &s) : bytes(s.data(), s.size()) {}
+
+    explicit bytes(const pybind11::str &s);
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string() const { return string_op<std::string>(); }
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    // enable_if is needed to avoid "ambiguous conversion" errors (see PR #3521).
+    template <typename T, detail::enable_if_t<std::is_same<T, std::string_view>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bytes(T s) : bytes(s.data(), s.size()) {}
+
+    // Obtain a string view that views the current `bytes` buffer value.  Note that this is only
+    // valid so long as the `bytes` instance remains alive and so generally should not outlive the
+    // lifetime of the `bytes` instance.
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator std::string_view() const { return string_op<std::string_view>(); }
+#endif
+private:
+    template <typename T>
+    T string_op() const {
+        char *buffer = nullptr;
+        ssize_t length = 0;
+        if (PyBytes_AsStringAndSize(m_ptr, &buffer, &length) != 0) {
+            throw error_already_set();
+        }
+        return {buffer, static_cast<size_t>(length)};
+    }
+};
+// Note: breathe >= 4.17.0 will fail to build docs if the below two constructors
+// are included in the doxygen group; close here and reopen after as a workaround
+/// @} pytypes
+
+inline bytes::bytes(const pybind11::str &s) {
+    object temp = s;
+    if (PyUnicode_Check(s.ptr())) {
+        temp = reinterpret_steal<object>(PyUnicode_AsUTF8String(s.ptr()));
+        if (!temp) {
+            throw error_already_set();
+        }
+    }
+    char *buffer = nullptr;
+    ssize_t length = 0;
+    if (PyBytes_AsStringAndSize(temp.ptr(), &buffer, &length) != 0) {
+        throw error_already_set();
+    }
+    auto obj = reinterpret_steal<object>(PYBIND11_BYTES_FROM_STRING_AND_SIZE(buffer, length));
+    if (!obj) {
+        pybind11_fail("Could not allocate bytes object!");
+    }
+    m_ptr = obj.release().ptr();
+}
+
+inline str::str(const bytes &b) {
+    char *buffer = nullptr;
+    ssize_t length = 0;
+    if (PyBytes_AsStringAndSize(b.ptr(), &buffer, &length) != 0) {
+        throw error_already_set();
+    }
+    auto obj = reinterpret_steal<object>(PyUnicode_FromStringAndSize(buffer, length));
+    if (!obj) {
+        if (PyErr_Occurred()) {
+            throw error_already_set();
+        }
+        pybind11_fail("Could not allocate string object!");
+    }
+    m_ptr = obj.release().ptr();
+}
+
+/// \addtogroup pytypes
+/// @{
+class bytearray : public object {
+public:
+    PYBIND11_OBJECT_CVT(bytearray, object, PyByteArray_Check, PyByteArray_FromObject)
+
+    template <typename SzType, detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    bytearray(const char *c, const SzType &n)
+        : object(PyByteArray_FromStringAndSize(c, ssize_t_cast(n)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate bytearray object!");
+        }
+    }
+
+    bytearray() : bytearray("", 0) {}
+
+    explicit bytearray(const std::string &s) : bytearray(s.data(), s.size()) {}
+
+    size_t size() const { return static_cast<size_t>(PyByteArray_Size(m_ptr)); }
+
+    explicit operator std::string() const {
+        char *buffer = PyByteArray_AS_STRING(m_ptr);
+        ssize_t size = PyByteArray_GET_SIZE(m_ptr);
+        return std::string(buffer, static_cast<size_t>(size));
+    }
+};
+// Note: breathe >= 4.17.0 will fail to build docs if the below two constructors
+// are included in the doxygen group; close here and reopen after as a workaround
+/// @} pytypes
+
+/// \addtogroup pytypes
+/// @{
+class none : public object {
+public:
+    PYBIND11_OBJECT(none, object, detail::PyNone_Check)
+    none() : object(Py_None, borrowed_t{}) {}
+};
+
+class ellipsis : public object {
+public:
+    PYBIND11_OBJECT(ellipsis, object, detail::PyEllipsis_Check)
+    ellipsis() : object(Py_Ellipsis, borrowed_t{}) {}
+};
+
+class bool_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(bool_, object, PyBool_Check, raw_bool)
+    bool_() : object(Py_False, borrowed_t{}) {}
+    // Allow implicit conversion from and to `bool`:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    bool_(bool value) : object(value ? Py_True : Py_False, borrowed_t{}) {}
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return (m_ptr != nullptr) && PyLong_AsLong(m_ptr) != 0; }
+
+private:
+    /// Return the truth value of an object -- always returns a new reference
+    static PyObject *raw_bool(PyObject *op) {
+        const auto value = PyObject_IsTrue(op);
+        if (value == -1) {
+            return nullptr;
+        }
+        return handle(value != 0 ? Py_True : Py_False).inc_ref().ptr();
+    }
+};
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+// Converts a value to the given unsigned type.  If an error occurs, you get back (Unsigned) -1;
+// otherwise you get back the unsigned long or unsigned long long value cast to (Unsigned).
+// (The distinction is critically important when casting a returned -1 error value to some other
+// unsigned type: (A)-1 != (B)-1 when A and B are unsigned types of different sizes).
+template <typename Unsigned>
+Unsigned as_unsigned(PyObject *o) {
+    if (sizeof(Unsigned) <= sizeof(unsigned long)) {
+        unsigned long v = PyLong_AsUnsignedLong(o);
+        return v == (unsigned long) -1 && PyErr_Occurred() ? (Unsigned) -1 : (Unsigned) v;
+    }
+    unsigned long long v = PyLong_AsUnsignedLongLong(o);
+    return v == (unsigned long long) -1 && PyErr_Occurred() ? (Unsigned) -1 : (Unsigned) v;
+}
+PYBIND11_NAMESPACE_END(detail)
+
+class int_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(int_, object, PYBIND11_LONG_CHECK, PyNumber_Long)
+    int_() : object(PyLong_FromLong(0), stolen_t{}) {}
+    // Allow implicit conversion from C++ integral types:
+    template <typename T, detail::enable_if_t<std::is_integral<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    int_(T value) {
+        if (sizeof(T) <= sizeof(long)) {
+            if (std::is_signed<T>::value) {
+                m_ptr = PyLong_FromLong((long) value);
+            } else {
+                m_ptr = PyLong_FromUnsignedLong((unsigned long) value);
+            }
+        } else {
+            if (std::is_signed<T>::value) {
+                m_ptr = PyLong_FromLongLong((long long) value);
+            } else {
+                m_ptr = PyLong_FromUnsignedLongLong((unsigned long long) value);
+            }
+        }
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate int object!");
+        }
+    }
+
+    template <typename T, detail::enable_if_t<std::is_integral<T>::value, int> = 0>
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator T() const {
+        return std::is_unsigned<T>::value  ? detail::as_unsigned<T>(m_ptr)
+               : sizeof(T) <= sizeof(long) ? (T) PyLong_AsLong(m_ptr)
+                                           : (T) PYBIND11_LONG_AS_LONGLONG(m_ptr);
+    }
+};
+
+class float_ : public object {
+public:
+    PYBIND11_OBJECT_CVT(float_, object, PyFloat_Check, PyNumber_Float)
+    // Allow implicit conversion from float/double:
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    float_(float value) : object(PyFloat_FromDouble((double) value), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate float object!");
+        }
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    float_(double value = .0) : object(PyFloat_FromDouble((double) value), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate float object!");
+        }
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator float() const { return (float) PyFloat_AsDouble(m_ptr); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator double() const { return (double) PyFloat_AsDouble(m_ptr); }
+};
+
+class weakref : public object {
+public:
+    PYBIND11_OBJECT_CVT_DEFAULT(weakref, object, PyWeakref_Check, raw_weakref)
+    explicit weakref(handle obj, handle callback = {})
+        : object(PyWeakref_NewRef(obj.ptr(), callback.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            if (PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            pybind11_fail("Could not allocate weak reference!");
+        }
+    }
+
+private:
+    static PyObject *raw_weakref(PyObject *o) { return PyWeakref_NewRef(o, nullptr); }
+};
+
+class slice : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(slice, object, PySlice_Check)
+    slice(handle start, handle stop, handle step)
+        : object(PySlice_New(start.ptr(), stop.ptr(), step.ptr()), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate slice object!");
+        }
+    }
+
+#ifdef PYBIND11_HAS_OPTIONAL
+    slice(std::optional<ssize_t> start, std::optional<ssize_t> stop, std::optional<ssize_t> step)
+        : slice(index_to_object(start), index_to_object(stop), index_to_object(step)) {}
+#else
+    slice(ssize_t start_, ssize_t stop_, ssize_t step_)
+        : slice(int_(start_), int_(stop_), int_(step_)) {}
+#endif
+
+    bool
+    compute(size_t length, size_t *start, size_t *stop, size_t *step, size_t *slicelength) const {
+        return PySlice_GetIndicesEx((PYBIND11_SLICE_OBJECT *) m_ptr,
+                                    (ssize_t) length,
+                                    (ssize_t *) start,
+                                    (ssize_t *) stop,
+                                    (ssize_t *) step,
+                                    (ssize_t *) slicelength)
+               == 0;
+    }
+    bool compute(
+        ssize_t length, ssize_t *start, ssize_t *stop, ssize_t *step, ssize_t *slicelength) const {
+        return PySlice_GetIndicesEx(
+                   (PYBIND11_SLICE_OBJECT *) m_ptr, length, start, stop, step, slicelength)
+               == 0;
+    }
+
+private:
+    template <typename T>
+    static object index_to_object(T index) {
+        return index ? object(int_(*index)) : object(none());
+    }
+};
+
+class capsule : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(capsule, object, PyCapsule_CheckExact)
+    PYBIND11_DEPRECATED("Use reinterpret_borrow<capsule>() or reinterpret_steal<capsule>()")
+    capsule(PyObject *ptr, bool is_borrowed)
+        : object(is_borrowed ? object(ptr, borrowed_t{}) : object(ptr, stolen_t{})) {}
+
+    explicit capsule(const void *value,
+                     const char *name = nullptr,
+                     PyCapsule_Destructor destructor = nullptr)
+        : object(PyCapsule_New(const_cast<void *>(value), name, destructor), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    PYBIND11_DEPRECATED("Please use the ctor with value, name, destructor args")
+    capsule(const void *value, PyCapsule_Destructor destructor)
+        : object(PyCapsule_New(const_cast<void *>(value), nullptr, destructor), stolen_t{}) {
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    /// Capsule name is nullptr.
+    capsule(const void *value, void (*destructor)(void *)) {
+        initialize_with_void_ptr_destructor(value, nullptr, destructor);
+    }
+
+    capsule(const void *value, const char *name, void (*destructor)(void *)) {
+        initialize_with_void_ptr_destructor(value, name, destructor);
+    }
+
+    explicit capsule(void (*destructor)()) {
+        m_ptr = PyCapsule_New(reinterpret_cast<void *>(destructor), nullptr, [](PyObject *o) {
+            const char *name = get_name_in_error_scope(o);
+            auto destructor = reinterpret_cast<void (*)()>(PyCapsule_GetPointer(o, name));
+            if (destructor == nullptr) {
+                throw error_already_set();
+            }
+            destructor();
+        });
+
+        if (!m_ptr) {
+            throw error_already_set();
+        }
+    }
+
+    template <typename T>
+    operator T *() const { // NOLINT(google-explicit-constructor)
+        return get_pointer<T>();
+    }
+
+    /// Get the pointer the capsule holds.
+    template <typename T = void>
+    T *get_pointer() const {
+        const auto *name = this->name();
+        T *result = static_cast<T *>(PyCapsule_GetPointer(m_ptr, name));
+        if (!result) {
+            throw error_already_set();
+        }
+        return result;
+    }
+
+    /// Replaces a capsule's pointer *without* calling the destructor on the existing one.
+    void set_pointer(const void *value) {
+        if (PyCapsule_SetPointer(m_ptr, const_cast<void *>(value)) != 0) {
+            throw error_already_set();
+        }
+    }
+
+    const char *name() const {
+        const char *name = PyCapsule_GetName(m_ptr);
+        if ((name == nullptr) && PyErr_Occurred()) {
+            throw error_already_set();
+        }
+        return name;
+    }
+
+    /// Replaces a capsule's name *without* calling the destructor on the existing one.
+    void set_name(const char *new_name) {
+        if (PyCapsule_SetName(m_ptr, new_name) != 0) {
+            throw error_already_set();
+        }
+    }
+
+private:
+    static const char *get_name_in_error_scope(PyObject *o) {
+        error_scope error_guard;
+
+        const char *name = PyCapsule_GetName(o);
+        if ((name == nullptr) && PyErr_Occurred()) {
+            // write out and consume error raised by call to PyCapsule_GetName
+            PyErr_WriteUnraisable(o);
+        }
+
+        return name;
+    }
+
+    void initialize_with_void_ptr_destructor(const void *value,
+                                             const char *name,
+                                             void (*destructor)(void *)) {
+        m_ptr = PyCapsule_New(const_cast<void *>(value), name, [](PyObject *o) {
+            // guard if destructor called while err indicator is set
+            error_scope error_guard;
+            auto destructor = reinterpret_cast<void (*)(void *)>(PyCapsule_GetContext(o));
+            if (destructor == nullptr && PyErr_Occurred()) {
+                throw error_already_set();
+            }
+            const char *name = get_name_in_error_scope(o);
+            void *ptr = PyCapsule_GetPointer(o, name);
+            if (ptr == nullptr) {
+                throw error_already_set();
+            }
+
+            if (destructor != nullptr) {
+                destructor(ptr);
+            }
+        });
+
+        if (!m_ptr || PyCapsule_SetContext(m_ptr, reinterpret_cast<void *>(destructor)) != 0) {
+            throw error_already_set();
+        }
+    }
+};
+
+class tuple : public object {
+public:
+    PYBIND11_OBJECT_CVT(tuple, object, PyTuple_Check, PySequence_Tuple)
+    template <typename SzType = ssize_t,
+              detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    // Some compilers generate link errors when using `const SzType &` here:
+    explicit tuple(SzType size = 0) : object(PyTuple_New(ssize_t_cast(size)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate tuple object!");
+        }
+    }
+    size_t size() const { return (size_t) PyTuple_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::tuple_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::tuple_iterator begin() const { return {*this, 0}; }
+    detail::tuple_iterator end() const { return {*this, PyTuple_GET_SIZE(m_ptr)}; }
+};
+
+// We need to put this into a separate function because the Intel compiler
+// fails to compile enable_if_t<all_of<is_keyword_or_ds<Args>...>::value> part below
+// (tested with ICC 2021.1 Beta 20200827).
+template <typename... Args>
+constexpr bool args_are_all_keyword_or_ds() {
+    return detail::all_of<detail::is_keyword_or_ds<Args>...>::value;
+}
+
+class dict : public object {
+public:
+    PYBIND11_OBJECT_CVT(dict, object, PyDict_Check, raw_dict)
+    dict() : object(PyDict_New(), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate dict object!");
+        }
+    }
+    template <typename... Args,
+              typename = detail::enable_if_t<args_are_all_keyword_or_ds<Args...>()>,
+              // MSVC workaround: it can't compile an out-of-line definition, so defer the
+              // collector
+              typename collector = detail::deferred_t<detail::unpacking_collector<>, Args...>>
+    explicit dict(Args &&...args) : dict(collector(std::forward<Args>(args)...).kwargs()) {}
+
+    size_t size() const { return (size_t) PyDict_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::dict_iterator begin() const { return {*this, 0}; }
+    detail::dict_iterator end() const { return {}; }
+    void clear() /* py-non-const */ { PyDict_Clear(ptr()); }
+    template <typename T>
+    bool contains(T &&key) const {
+        auto result = PyDict_Contains(m_ptr, detail::object_or_cast(std::forward<T>(key)).ptr());
+        if (result == -1) {
+            throw error_already_set();
+        }
+        return result == 1;
+    }
+
+private:
+    /// Call the `dict` Python type -- always returns a new reference
+    static PyObject *raw_dict(PyObject *op) {
+        if (PyDict_Check(op)) {
+            return handle(op).inc_ref().ptr();
+        }
+        return PyObject_CallFunctionObjArgs((PyObject *) &PyDict_Type, op, nullptr);
+    }
+};
+
+class sequence : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(sequence, object, PySequence_Check)
+    size_t size() const {
+        ssize_t result = PySequence_Size(m_ptr);
+        if (result == -1) {
+            throw error_already_set();
+        }
+        return (size_t) result;
+    }
+    bool empty() const { return size() == 0; }
+    detail::sequence_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::sequence_iterator begin() const { return {*this, 0}; }
+    detail::sequence_iterator end() const { return {*this, PySequence_Size(m_ptr)}; }
+};
+
+class list : public object {
+public:
+    PYBIND11_OBJECT_CVT(list, object, PyList_Check, PySequence_List)
+    template <typename SzType = ssize_t,
+              detail::enable_if_t<std::is_integral<SzType>::value, int> = 0>
+    // Some compilers generate link errors when using `const SzType &` here:
+    explicit list(SzType size = 0) : object(PyList_New(ssize_t_cast(size)), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate list object!");
+        }
+    }
+    size_t size() const { return (size_t) PyList_Size(m_ptr); }
+    bool empty() const { return size() == 0; }
+    detail::list_accessor operator[](size_t index) const { return {*this, index}; }
+    template <typename T, detail::enable_if_t<detail::is_pyobject<T>::value, int> = 0>
+    detail::item_accessor operator[](T &&o) const {
+        return object::operator[](std::forward<T>(o));
+    }
+    detail::list_iterator begin() const { return {*this, 0}; }
+    detail::list_iterator end() const { return {*this, PyList_GET_SIZE(m_ptr)}; }
+    template <typename T>
+    void append(T &&val) /* py-non-const */ {
+        if (PyList_Append(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr()) != 0) {
+            throw error_already_set();
+        }
+    }
+    template <typename IdxType,
+              typename ValType,
+              detail::enable_if_t<std::is_integral<IdxType>::value, int> = 0>
+    void insert(const IdxType &index, ValType &&val) /* py-non-const */ {
+        if (PyList_Insert(m_ptr,
+                          ssize_t_cast(index),
+                          detail::object_or_cast(std::forward<ValType>(val)).ptr())
+            != 0) {
+            throw error_already_set();
+        }
+    }
+    void clear() /* py-non-const */ {
+        if (PyList_SetSlice(m_ptr, 0, PyList_Size(m_ptr), nullptr) == -1) {
+            throw error_already_set();
+        }
+    }
+};
+
+class args : public tuple {
+    PYBIND11_OBJECT_DEFAULT(args, tuple, PyTuple_Check)
+};
+class kwargs : public dict {
+    PYBIND11_OBJECT_DEFAULT(kwargs, dict, PyDict_Check)
+};
+
+class anyset : public object {
+public:
+    PYBIND11_OBJECT(anyset, object, PyAnySet_Check)
+    size_t size() const { return static_cast<size_t>(PySet_Size(m_ptr)); }
+    bool empty() const { return size() == 0; }
+    template <typename T>
+    bool contains(T &&val) const {
+        auto result = PySet_Contains(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr());
+        if (result == -1) {
+            throw error_already_set();
+        }
+        return result == 1;
+    }
+};
+
+class set : public anyset {
+public:
+    PYBIND11_OBJECT_CVT(set, anyset, PySet_Check, PySet_New)
+    set() : anyset(PySet_New(nullptr), stolen_t{}) {
+        if (!m_ptr) {
+            pybind11_fail("Could not allocate set object!");
+        }
+    }
+    template <typename T>
+    bool add(T &&val) /* py-non-const */ {
+        return PySet_Add(m_ptr, detail::object_or_cast(std::forward<T>(val)).ptr()) == 0;
+    }
+    void clear() /* py-non-const */ { PySet_Clear(m_ptr); }
+};
+
+class frozenset : public anyset {
+public:
+    PYBIND11_OBJECT_CVT(frozenset, anyset, PyFrozenSet_Check, PyFrozenSet_New)
+};
+
+class function : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(function, object, PyCallable_Check)
+    handle cpp_function() const {
+        handle fun = detail::get_function(m_ptr);
+        if (fun && PyCFunction_Check(fun.ptr())) {
+            return fun;
+        }
+        return handle();
+    }
+    bool is_cpp_function() const { return (bool) cpp_function(); }
+};
+
+class staticmethod : public object {
+public:
+    PYBIND11_OBJECT_CVT(staticmethod, object, detail::PyStaticMethod_Check, PyStaticMethod_New)
+};
+
+class buffer : public object {
+public:
+    PYBIND11_OBJECT_DEFAULT(buffer, object, PyObject_CheckBuffer)
+
+    buffer_info request(bool writable = false) const {
+        int flags = PyBUF_STRIDES | PyBUF_FORMAT;
+        if (writable) {
+            flags |= PyBUF_WRITABLE;
+        }
+        auto *view = new Py_buffer();
+        if (PyObject_GetBuffer(m_ptr, view, flags) != 0) {
+            delete view;
+            throw error_already_set();
+        }
+        return buffer_info(view);
+    }
+};
+
+class memoryview : public object {
+public:
+    PYBIND11_OBJECT_CVT(memoryview, object, PyMemoryView_Check, PyMemoryView_FromObject)
+
+    /** \rst
+        Creates ``memoryview`` from ``buffer_info``.
+
+        ``buffer_info`` must be created from ``buffer::request()``. Otherwise
+        throws an exception.
+
+        For creating a ``memoryview`` from objects that support buffer protocol,
+        use ``memoryview(const object& obj)`` instead of this constructor.
+     \endrst */
+    explicit memoryview(const buffer_info &info) {
+        if (!info.view()) {
+            pybind11_fail("Prohibited to create memoryview without Py_buffer");
+        }
+        // Note: PyMemoryView_FromBuffer never increments obj reference.
+        m_ptr = (info.view()->obj) ? PyMemoryView_FromObject(info.view()->obj)
+                                   : PyMemoryView_FromBuffer(info.view());
+        if (!m_ptr) {
+            pybind11_fail("Unable to create memoryview from buffer descriptor");
+        }
+    }
+
+    /** \rst
+        Creates ``memoryview`` from static buffer.
+
+        This method is meant for providing a ``memoryview`` for C/C++ buffer not
+        managed by Python. The caller is responsible for managing the lifetime
+        of ``ptr`` and ``format``, which MUST outlive the memoryview constructed
+        here.
+
+        See also: Python C API documentation for `PyMemoryView_FromBuffer`_.
+
+        .. _PyMemoryView_FromBuffer:
+           https://docs.python.org/c-api/memoryview.html#c.PyMemoryView_FromBuffer
+
+        :param ptr: Pointer to the buffer.
+        :param itemsize: Byte size of an element.
+        :param format: Pointer to the null-terminated format string. For
+            homogeneous Buffers, this should be set to
+            ``format_descriptor<T>::value``.
+        :param shape: Shape of the tensor (1 entry per dimension).
+        :param strides: Number of bytes between adjacent entries (for each
+            per dimension).
+        :param readonly: Flag to indicate if the underlying storage may be
+            written to.
+     \endrst */
+    static memoryview from_buffer(void *ptr,
+                                  ssize_t itemsize,
+                                  const char *format,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides,
+                                  bool readonly = false);
+
+    static memoryview from_buffer(const void *ptr,
+                                  ssize_t itemsize,
+                                  const char *format,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides) {
+        return memoryview::from_buffer(
+            const_cast<void *>(ptr), itemsize, format, std::move(shape), std::move(strides), true);
+    }
+
+    template <typename T>
+    static memoryview from_buffer(T *ptr,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides,
+                                  bool readonly = false) {
+        return memoryview::from_buffer(reinterpret_cast<void *>(ptr),
+                                       sizeof(T),
+                                       format_descriptor<T>::value,
+                                       std::move(shape),
+                                       std::move(strides),
+                                       readonly);
+    }
+
+    template <typename T>
+    static memoryview from_buffer(const T *ptr,
+                                  detail::any_container<ssize_t> shape,
+                                  detail::any_container<ssize_t> strides) {
+        return memoryview::from_buffer(
+            const_cast<T *>(ptr), std::move(shape), std::move(strides), true);
+    }
+
+    /** \rst
+        Creates ``memoryview`` from static memory.
+
+        This method is meant for providing a ``memoryview`` for C/C++ buffer not
+        managed by Python. The caller is responsible for managing the lifetime
+        of ``mem``, which MUST outlive the memoryview constructed here.
+
+        See also: Python C API documentation for `PyMemoryView_FromBuffer`_.
+
+        .. _PyMemoryView_FromMemory:
+           https://docs.python.org/c-api/memoryview.html#c.PyMemoryView_FromMemory
+     \endrst */
+    static memoryview from_memory(void *mem, ssize_t size, bool readonly = false) {
+        PyObject *ptr = PyMemoryView_FromMemory(
+            reinterpret_cast<char *>(mem), size, (readonly) ? PyBUF_READ : PyBUF_WRITE);
+        if (!ptr) {
+            pybind11_fail("Could not allocate memoryview object!");
+        }
+        return memoryview(object(ptr, stolen_t{}));
+    }
+
+    static memoryview from_memory(const void *mem, ssize_t size) {
+        return memoryview::from_memory(const_cast<void *>(mem), size, true);
+    }
+
+#ifdef PYBIND11_HAS_STRING_VIEW
+    static memoryview from_memory(std::string_view mem) {
+        return from_memory(const_cast<char *>(mem.data()), static_cast<ssize_t>(mem.size()), true);
+    }
+#endif
+};
+
+/// @cond DUPLICATE
+inline memoryview memoryview::from_buffer(void *ptr,
+                                          ssize_t itemsize,
+                                          const char *format,
+                                          detail::any_container<ssize_t> shape,
+                                          detail::any_container<ssize_t> strides,
+                                          bool readonly) {
+    size_t ndim = shape->size();
+    if (ndim != strides->size()) {
+        pybind11_fail("memoryview: shape length doesn't match strides length");
+    }
+    ssize_t size = ndim != 0u ? 1 : 0;
+    for (size_t i = 0; i < ndim; ++i) {
+        size *= (*shape)[i];
+    }
+    Py_buffer view;
+    view.buf = ptr;
+    view.obj = nullptr;
+    view.len = size * itemsize;
+    view.readonly = static_cast<int>(readonly);
+    view.itemsize = itemsize;
+    view.format = const_cast<char *>(format);
+    view.ndim = static_cast<int>(ndim);
+    view.shape = shape->data();
+    view.strides = strides->data();
+    view.suboffsets = nullptr;
+    view.internal = nullptr;
+    PyObject *obj = PyMemoryView_FromBuffer(&view);
+    if (!obj) {
+        throw error_already_set();
+    }
+    return memoryview(object(obj, stolen_t{}));
+}
+/// @endcond
+/// @} pytypes
+
+/// \addtogroup python_builtins
+/// @{
+
+/// Get the length of a Python object.
+inline size_t len(handle h) {
+    ssize_t result = PyObject_Length(h.ptr());
+    if (result < 0) {
+        throw error_already_set();
+    }
+    return (size_t) result;
+}
+
+/// Get the length hint of a Python object.
+/// Returns 0 when this cannot be determined.
+inline size_t len_hint(handle h) {
+    ssize_t result = PyObject_LengthHint(h.ptr(), 0);
+    if (result < 0) {
+        // Sometimes a length can't be determined at all (eg generators)
+        // In which case simply return 0
+        PyErr_Clear();
+        return 0;
+    }
+    return (size_t) result;
+}
+
+inline str repr(handle h) {
+    PyObject *str_value = PyObject_Repr(h.ptr());
+    if (!str_value) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<str>(str_value);
+}
+
+inline iterator iter(handle obj) {
+    PyObject *result = PyObject_GetIter(obj.ptr());
+    if (!result) {
+        throw error_already_set();
+    }
+    return reinterpret_steal<iterator>(result);
+}
+/// @} python_builtins
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+template <typename D>
+iterator object_api<D>::begin() const {
+    return iter(derived());
+}
+template <typename D>
+iterator object_api<D>::end() const {
+    return iterator::sentinel();
+}
+template <typename D>
+item_accessor object_api<D>::operator[](handle key) const {
+    return {derived(), reinterpret_borrow<object>(key)};
+}
+template <typename D>
+item_accessor object_api<D>::operator[](object &&key) const {
+    return {derived(), std::move(key)};
+}
+template <typename D>
+item_accessor object_api<D>::operator[](const char *key) const {
+    return {derived(), pybind11::str(key)};
+}
+template <typename D>
+obj_attr_accessor object_api<D>::attr(handle key) const {
+    return {derived(), reinterpret_borrow<object>(key)};
+}
+template <typename D>
+obj_attr_accessor object_api<D>::attr(object &&key) const {
+    return {derived(), std::move(key)};
+}
+template <typename D>
+str_attr_accessor object_api<D>::attr(const char *key) const {
+    return {derived(), key};
+}
+template <typename D>
+args_proxy object_api<D>::operator*() const {
+    return args_proxy(derived().ptr());
+}
+template <typename D>
+template <typename T>
+bool object_api<D>::contains(T &&item) const {
+    return attr("__contains__")(std::forward<T>(item)).template cast<bool>();
+}
+
+template <typename D>
+pybind11::str object_api<D>::str() const {
+    return pybind11::str(derived());
+}
+
+template <typename D>
+str_attr_accessor object_api<D>::doc() const {
+    return attr("__doc__");
+}
+
+template <typename D>
+handle object_api<D>::get_type() const {
+    return type::handle_of(derived());
+}
+
+template <typename D>
+bool object_api<D>::rich_compare(object_api const &other, int value) const {
+    int rv = PyObject_RichCompareBool(derived().ptr(), other.derived().ptr(), value);
+    if (rv == -1) {
+        throw error_already_set();
+    }
+    return rv == 1;
+}
+
+#define PYBIND11_MATH_OPERATOR_UNARY(op, fn)                                                      \
+    template <typename D>                                                                         \
+    object object_api<D>::op() const {                                                            \
+        object result = reinterpret_steal<object>(fn(derived().ptr()));                           \
+        if (!result.ptr())                                                                        \
+            throw error_already_set();                                                            \
+        return result;                                                                            \
+    }
+
+#define PYBIND11_MATH_OPERATOR_BINARY(op, fn)                                                     \
+    template <typename D>                                                                         \
+    object object_api<D>::op(object_api const &other) const {                                     \
+        object result = reinterpret_steal<object>(fn(derived().ptr(), other.derived().ptr()));    \
+        if (!result.ptr())                                                                        \
+            throw error_already_set();                                                            \
+        return result;                                                                            \
+    }
+
+#define PYBIND11_MATH_OPERATOR_BINARY_INPLACE(iop, fn)                                            \
+    template <typename D>                                                                         \
+    object object_api<D>::iop(object_api const &other) {                                          \
+        object result = reinterpret_steal<object>(fn(derived().ptr(), other.derived().ptr()));    \
+        if (!result.ptr())                                                                        \
+            throw error_already_set();                                                            \
+        return result;                                                                            \
+    }
+
+PYBIND11_MATH_OPERATOR_UNARY(operator~, PyNumber_Invert)
+PYBIND11_MATH_OPERATOR_UNARY(operator-, PyNumber_Negative)
+PYBIND11_MATH_OPERATOR_BINARY(operator+, PyNumber_Add)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator+=, PyNumber_InPlaceAdd)
+PYBIND11_MATH_OPERATOR_BINARY(operator-, PyNumber_Subtract)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator-=, PyNumber_InPlaceSubtract)
+PYBIND11_MATH_OPERATOR_BINARY(operator*, PyNumber_Multiply)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator*=, PyNumber_InPlaceMultiply)
+PYBIND11_MATH_OPERATOR_BINARY(operator/, PyNumber_TrueDivide)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator/=, PyNumber_InPlaceTrueDivide)
+PYBIND11_MATH_OPERATOR_BINARY(operator|, PyNumber_Or)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator|=, PyNumber_InPlaceOr)
+PYBIND11_MATH_OPERATOR_BINARY(operator&, PyNumber_And)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator&=, PyNumber_InPlaceAnd)
+PYBIND11_MATH_OPERATOR_BINARY(operator^, PyNumber_Xor)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator^=, PyNumber_InPlaceXor)
+PYBIND11_MATH_OPERATOR_BINARY(operator<<, PyNumber_Lshift)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator<<=, PyNumber_InPlaceLshift)
+PYBIND11_MATH_OPERATOR_BINARY(operator>>, PyNumber_Rshift)
+PYBIND11_MATH_OPERATOR_BINARY_INPLACE(operator>>=, PyNumber_InPlaceRshift)
+
+#undef PYBIND11_MATH_OPERATOR_UNARY
+#undef PYBIND11_MATH_OPERATOR_BINARY
+#undef PYBIND11_MATH_OPERATOR_BINARY_INPLACE
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/stl.h b/lib/python3.11/site-packages/torch/include/pybind11/stl.h
new file mode 100644
index 0000000000000000000000000000000000000000..71bc5902efbc1fb4085d2b42055acd87d1cd7334
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/stl.h
@@ -0,0 +1,448 @@
+/*
+    pybind11/stl.h: Transparent conversion for STL data types
+
+    Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "pybind11.h"
+#include "detail/common.h"
+
+#include <deque>
+#include <list>
+#include <map>
+#include <ostream>
+#include <set>
+#include <unordered_map>
+#include <unordered_set>
+#include <valarray>
+
+// See `detail/common.h` for implementation of these guards.
+#if defined(PYBIND11_HAS_OPTIONAL)
+#    include <optional>
+#elif defined(PYBIND11_HAS_EXP_OPTIONAL)
+#    include <experimental/optional>
+#endif
+
+#if defined(PYBIND11_HAS_VARIANT)
+#    include <variant>
+#endif
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/// Extracts an const lvalue reference or rvalue reference for U based on the type of T (e.g. for
+/// forwarding a container element).  Typically used indirect via forwarded_type(), below.
+template <typename T, typename U>
+using forwarded_type = conditional_t<std::is_lvalue_reference<T>::value,
+                                     remove_reference_t<U> &,
+                                     remove_reference_t<U> &&>;
+
+/// Forwards a value U as rvalue or lvalue according to whether T is rvalue or lvalue; typically
+/// used for forwarding a container's elements.
+template <typename T, typename U>
+constexpr forwarded_type<T, U> forward_like(U &&u) {
+    return std::forward<detail::forwarded_type<T, U>>(std::forward<U>(u));
+}
+
+// Checks if a container has a STL style reserve method.
+// This will only return true for a `reserve()` with a `void` return.
+template <typename C>
+using has_reserve_method = std::is_same<decltype(std::declval<C>().reserve(0)), void>;
+
+template <typename Type, typename Key>
+struct set_caster {
+    using type = Type;
+    using key_conv = make_caster<Key>;
+
+private:
+    template <typename T = Type, enable_if_t<has_reserve_method<T>::value, int> = 0>
+    void reserve_maybe(const anyset &s, Type *) {
+        value.reserve(s.size());
+    }
+    void reserve_maybe(const anyset &, void *) {}
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<anyset>(src)) {
+            return false;
+        }
+        auto s = reinterpret_borrow<anyset>(src);
+        value.clear();
+        reserve_maybe(s, &value);
+        for (auto entry : s) {
+            key_conv conv;
+            if (!conv.load(entry, convert)) {
+                return false;
+            }
+            value.insert(cast_op<Key &&>(std::move(conv)));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Key>::policy(policy);
+        }
+        pybind11::set s;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                key_conv::cast(detail::forward_like<T>(value), policy, parent));
+            if (!value_ || !s.add(std::move(value_))) {
+                return handle();
+            }
+        }
+        return s.release();
+    }
+
+    PYBIND11_TYPE_CASTER(type, const_name("set[") + key_conv::name + const_name("]"));
+};
+
+template <typename Type, typename Key, typename Value>
+struct map_caster {
+    using key_conv = make_caster<Key>;
+    using value_conv = make_caster<Value>;
+
+private:
+    template <typename T = Type, enable_if_t<has_reserve_method<T>::value, int> = 0>
+    void reserve_maybe(const dict &d, Type *) {
+        value.reserve(d.size());
+    }
+    void reserve_maybe(const dict &, void *) {}
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<dict>(src)) {
+            return false;
+        }
+        auto d = reinterpret_borrow<dict>(src);
+        value.clear();
+        reserve_maybe(d, &value);
+        for (auto it : d) {
+            key_conv kconv;
+            value_conv vconv;
+            if (!kconv.load(it.first.ptr(), convert) || !vconv.load(it.second.ptr(), convert)) {
+                return false;
+            }
+            value.emplace(cast_op<Key &&>(std::move(kconv)), cast_op<Value &&>(std::move(vconv)));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        dict d;
+        return_value_policy policy_key = policy;
+        return_value_policy policy_value = policy;
+        if (!std::is_lvalue_reference<T>::value) {
+            policy_key = return_value_policy_override<Key>::policy(policy_key);
+            policy_value = return_value_policy_override<Value>::policy(policy_value);
+        }
+        for (auto &&kv : src) {
+            auto key = reinterpret_steal<object>(
+                key_conv::cast(detail::forward_like<T>(kv.first), policy_key, parent));
+            auto value = reinterpret_steal<object>(
+                value_conv::cast(detail::forward_like<T>(kv.second), policy_value, parent));
+            if (!key || !value) {
+                return handle();
+            }
+            d[std::move(key)] = std::move(value);
+        }
+        return d.release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name("dict[") + key_conv::name + const_name(", ") + value_conv::name
+                             + const_name("]"));
+};
+
+template <typename Type, typename Value>
+struct list_caster {
+    using value_conv = make_caster<Value>;
+
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src) || isinstance<bytes>(src) || isinstance<str>(src)) {
+            return false;
+        }
+        auto s = reinterpret_borrow<sequence>(src);
+        value.clear();
+        reserve_maybe(s, &value);
+        for (const auto &it : s) {
+            value_conv conv;
+            if (!conv.load(it, convert)) {
+                return false;
+            }
+            value.push_back(cast_op<Value &&>(std::move(conv)));
+        }
+        return true;
+    }
+
+private:
+    template <typename T = Type, enable_if_t<has_reserve_method<T>::value, int> = 0>
+    void reserve_maybe(const sequence &s, Type *) {
+        value.reserve(s.size());
+    }
+    void reserve_maybe(const sequence &, void *) {}
+
+public:
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Value>::policy(policy);
+        }
+        list l(src.size());
+        ssize_t index = 0;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                value_conv::cast(detail::forward_like<T>(value), policy, parent));
+            if (!value_) {
+                return handle();
+            }
+            PyList_SET_ITEM(l.ptr(), index++, value_.release().ptr()); // steals a reference
+        }
+        return l.release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type, const_name("list[") + value_conv::name + const_name("]"));
+};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::vector<Type, Alloc>> : list_caster<std::vector<Type, Alloc>, Type> {};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::deque<Type, Alloc>> : list_caster<std::deque<Type, Alloc>, Type> {};
+
+template <typename Type, typename Alloc>
+struct type_caster<std::list<Type, Alloc>> : list_caster<std::list<Type, Alloc>, Type> {};
+
+template <typename ArrayType, typename Value, bool Resizable, size_t Size = 0>
+struct array_caster {
+    using value_conv = make_caster<Value>;
+
+private:
+    template <bool R = Resizable>
+    bool require_size(enable_if_t<R, size_t> size) {
+        if (value.size() != size) {
+            value.resize(size);
+        }
+        return true;
+    }
+    template <bool R = Resizable>
+    bool require_size(enable_if_t<!R, size_t> size) {
+        return size == Size;
+    }
+
+public:
+    bool load(handle src, bool convert) {
+        if (!isinstance<sequence>(src)) {
+            return false;
+        }
+        auto l = reinterpret_borrow<sequence>(src);
+        if (!require_size(l.size())) {
+            return false;
+        }
+        size_t ctr = 0;
+        for (const auto &it : l) {
+            value_conv conv;
+            if (!conv.load(it, convert)) {
+                return false;
+            }
+            value[ctr++] = cast_op<Value &&>(std::move(conv));
+        }
+        return true;
+    }
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        list l(src.size());
+        ssize_t index = 0;
+        for (auto &&value : src) {
+            auto value_ = reinterpret_steal<object>(
+                value_conv::cast(detail::forward_like<T>(value), policy, parent));
+            if (!value_) {
+                return handle();
+            }
+            PyList_SET_ITEM(l.ptr(), index++, value_.release().ptr()); // steals a reference
+        }
+        return l.release();
+    }
+
+    PYBIND11_TYPE_CASTER(ArrayType,
+                         const_name<Resizable>(const_name(""), const_name("Annotated["))
+                             + const_name("list[") + value_conv::name + const_name("]")
+                             + const_name<Resizable>(const_name(""),
+                                                     const_name(", FixedSize(")
+                                                         + const_name<Size>() + const_name(")]")));
+};
+
+template <typename Type, size_t Size>
+struct type_caster<std::array<Type, Size>>
+    : array_caster<std::array<Type, Size>, Type, false, Size> {};
+
+template <typename Type>
+struct type_caster<std::valarray<Type>> : array_caster<std::valarray<Type>, Type, true> {};
+
+template <typename Key, typename Compare, typename Alloc>
+struct type_caster<std::set<Key, Compare, Alloc>>
+    : set_caster<std::set<Key, Compare, Alloc>, Key> {};
+
+template <typename Key, typename Hash, typename Equal, typename Alloc>
+struct type_caster<std::unordered_set<Key, Hash, Equal, Alloc>>
+    : set_caster<std::unordered_set<Key, Hash, Equal, Alloc>, Key> {};
+
+template <typename Key, typename Value, typename Compare, typename Alloc>
+struct type_caster<std::map<Key, Value, Compare, Alloc>>
+    : map_caster<std::map<Key, Value, Compare, Alloc>, Key, Value> {};
+
+template <typename Key, typename Value, typename Hash, typename Equal, typename Alloc>
+struct type_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>>
+    : map_caster<std::unordered_map<Key, Value, Hash, Equal, Alloc>, Key, Value> {};
+
+// This type caster is intended to be used for std::optional and std::experimental::optional
+template <typename Type, typename Value = typename Type::value_type>
+struct optional_caster {
+    using value_conv = make_caster<Value>;
+
+    template <typename T>
+    static handle cast(T &&src, return_value_policy policy, handle parent) {
+        if (!src) {
+            return none().release();
+        }
+        if (!std::is_lvalue_reference<T>::value) {
+            policy = return_value_policy_override<Value>::policy(policy);
+        }
+        // NOLINTNEXTLINE(bugprone-unchecked-optional-access)
+        return value_conv::cast(*std::forward<T>(src), policy, parent);
+    }
+
+    bool load(handle src, bool convert) {
+        if (!src) {
+            return false;
+        }
+        if (src.is_none()) {
+            return true; // default-constructed value is already empty
+        }
+        value_conv inner_caster;
+        if (!inner_caster.load(src, convert)) {
+            return false;
+        }
+
+        value.emplace(cast_op<Value &&>(std::move(inner_caster)));
+        return true;
+    }
+
+    PYBIND11_TYPE_CASTER(Type, const_name("Optional[") + value_conv::name + const_name("]"));
+};
+
+#if defined(PYBIND11_HAS_OPTIONAL)
+template <typename T>
+struct type_caster<std::optional<T>> : public optional_caster<std::optional<T>> {};
+
+template <>
+struct type_caster<std::nullopt_t> : public void_caster<std::nullopt_t> {};
+#endif
+
+#if defined(PYBIND11_HAS_EXP_OPTIONAL)
+template <typename T>
+struct type_caster<std::experimental::optional<T>>
+    : public optional_caster<std::experimental::optional<T>> {};
+
+template <>
+struct type_caster<std::experimental::nullopt_t>
+    : public void_caster<std::experimental::nullopt_t> {};
+#endif
+
+/// Visit a variant and cast any found type to Python
+struct variant_caster_visitor {
+    return_value_policy policy;
+    handle parent;
+
+    using result_type = handle; // required by boost::variant in C++11
+
+    template <typename T>
+    result_type operator()(T &&src) const {
+        return make_caster<T>::cast(std::forward<T>(src), policy, parent);
+    }
+};
+
+/// Helper class which abstracts away variant's `visit` function. `std::variant` and similar
+/// `namespace::variant` types which provide a `namespace::visit()` function are handled here
+/// automatically using argument-dependent lookup. Users can provide specializations for other
+/// variant-like classes, e.g. `boost::variant` and `boost::apply_visitor`.
+template <template <typename...> class Variant>
+struct visit_helper {
+    template <typename... Args>
+    static auto call(Args &&...args) -> decltype(visit(std::forward<Args>(args)...)) {
+        return visit(std::forward<Args>(args)...);
+    }
+};
+
+/// Generic variant caster
+template <typename Variant>
+struct variant_caster;
+
+template <template <typename...> class V, typename... Ts>
+struct variant_caster<V<Ts...>> {
+    static_assert(sizeof...(Ts) > 0, "Variant must consist of at least one alternative.");
+
+    template <typename U, typename... Us>
+    bool load_alternative(handle src, bool convert, type_list<U, Us...>) {
+        auto caster = make_caster<U>();
+        if (caster.load(src, convert)) {
+            value = cast_op<U>(std::move(caster));
+            return true;
+        }
+        return load_alternative(src, convert, type_list<Us...>{});
+    }
+
+    bool load_alternative(handle, bool, type_list<>) { return false; }
+
+    bool load(handle src, bool convert) {
+        // Do a first pass without conversions to improve constructor resolution.
+        // E.g. `py::int_(1).cast<variant<double, int>>()` needs to fill the `int`
+        // slot of the variant. Without two-pass loading `double` would be filled
+        // because it appears first and a conversion is possible.
+        if (convert && load_alternative(src, false, type_list<Ts...>{})) {
+            return true;
+        }
+        return load_alternative(src, convert, type_list<Ts...>{});
+    }
+
+    template <typename Variant>
+    static handle cast(Variant &&src, return_value_policy policy, handle parent) {
+        return visit_helper<V>::call(variant_caster_visitor{policy, parent},
+                                     std::forward<Variant>(src));
+    }
+
+    using Type = V<Ts...>;
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name("Union[")
+                             + ::pybind11::detail::concat(make_caster<Ts>::name...)
+                             + const_name("]"));
+};
+
+#if defined(PYBIND11_HAS_VARIANT)
+template <typename... Ts>
+struct type_caster<std::variant<Ts...>> : variant_caster<std::variant<Ts...>> {};
+
+template <>
+struct type_caster<std::monostate> : public void_caster<std::monostate> {};
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+
+inline std::ostream &operator<<(std::ostream &os, const handle &obj) {
+#ifdef PYBIND11_HAS_STRING_VIEW
+    os << str(obj).cast<std::string_view>();
+#else
+    os << (std::string) str(obj);
+#endif
+    return os;
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/stl_bind.h b/lib/python3.11/site-packages/torch/include/pybind11/stl_bind.h
new file mode 100644
index 0000000000000000000000000000000000000000..fcb48dea3396435fb6321a376d4cbfc099f8542a
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/stl_bind.h
@@ -0,0 +1,822 @@
+/*
+    pybind11/std_bind.h: Binding generators for STL data types
+
+    Copyright (c) 2016 Sergey Lyskov and Wenzel Jakob
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "detail/type_caster_base.h"
+#include "cast.h"
+#include "operators.h"
+
+#include <algorithm>
+#include <sstream>
+#include <type_traits>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/* SFINAE helper class used by 'is_comparable */
+template <typename T>
+struct container_traits {
+    template <typename T2>
+    static std::true_type
+    test_comparable(decltype(std::declval<const T2 &>() == std::declval<const T2 &>()) *);
+    template <typename T2>
+    static std::false_type test_comparable(...);
+    template <typename T2>
+    static std::true_type test_value(typename T2::value_type *);
+    template <typename T2>
+    static std::false_type test_value(...);
+    template <typename T2>
+    static std::true_type test_pair(typename T2::first_type *, typename T2::second_type *);
+    template <typename T2>
+    static std::false_type test_pair(...);
+
+    static constexpr const bool is_comparable
+        = std::is_same<std::true_type, decltype(test_comparable<T>(nullptr))>::value;
+    static constexpr const bool is_pair
+        = std::is_same<std::true_type, decltype(test_pair<T>(nullptr, nullptr))>::value;
+    static constexpr const bool is_vector
+        = std::is_same<std::true_type, decltype(test_value<T>(nullptr))>::value;
+    static constexpr const bool is_element = !is_pair && !is_vector;
+};
+
+/* Default: is_comparable -> std::false_type */
+template <typename T, typename SFINAE = void>
+struct is_comparable : std::false_type {};
+
+/* For non-map data structures, check whether operator== can be instantiated */
+template <typename T>
+struct is_comparable<
+    T,
+    enable_if_t<container_traits<T>::is_element && container_traits<T>::is_comparable>>
+    : std::true_type {};
+
+/* For a vector/map data structure, recursively check the value type
+   (which is std::pair for maps) */
+template <typename T>
+struct is_comparable<T, enable_if_t<container_traits<T>::is_vector>>
+    : is_comparable<typename recursive_container_traits<T>::type_to_check_recursively> {};
+
+template <>
+struct is_comparable<recursive_bottom> : std::true_type {};
+
+/* For pairs, recursively check the two data types */
+template <typename T>
+struct is_comparable<T, enable_if_t<container_traits<T>::is_pair>> {
+    static constexpr const bool value = is_comparable<typename T::first_type>::value
+                                        && is_comparable<typename T::second_type>::value;
+};
+
+/* Fallback functions */
+template <typename, typename, typename... Args>
+void vector_if_copy_constructible(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_if_equal_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_if_insertion_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void vector_modifiers(const Args &...) {}
+
+template <typename Vector, typename Class_>
+void vector_if_copy_constructible(enable_if_t<is_copy_constructible<Vector>::value, Class_> &cl) {
+    cl.def(init<const Vector &>(), "Copy constructor");
+}
+
+template <typename Vector, typename Class_>
+void vector_if_equal_operator(enable_if_t<is_comparable<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+
+    cl.def(self == self);
+    cl.def(self != self);
+
+    cl.def(
+        "count",
+        [](const Vector &v, const T &x) { return std::count(v.begin(), v.end(), x); },
+        arg("x"),
+        "Return the number of times ``x`` appears in the list");
+
+    cl.def(
+        "remove",
+        [](Vector &v, const T &x) {
+            auto p = std::find(v.begin(), v.end(), x);
+            if (p != v.end()) {
+                v.erase(p);
+            } else {
+                throw value_error();
+            }
+        },
+        arg("x"),
+        "Remove the first item from the list whose value is x. "
+        "It is an error if there is no such item.");
+
+    cl.def(
+        "__contains__",
+        [](const Vector &v, const T &x) { return std::find(v.begin(), v.end(), x) != v.end(); },
+        arg("x"),
+        "Return true the container contains ``x``");
+}
+
+// Vector modifiers -- requires a copyable vector_type:
+// (Technically, some of these (pop and __delitem__) don't actually require copyability, but it
+// seems silly to allow deletion but not insertion, so include them here too.)
+template <typename Vector, typename Class_>
+void vector_modifiers(
+    enable_if_t<is_copy_constructible<typename Vector::value_type>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+
+    auto wrap_i = [](DiffType i, SizeType n) {
+        if (i < 0) {
+            i += n;
+        }
+        if (i < 0 || (SizeType) i >= n) {
+            throw index_error();
+        }
+        return i;
+    };
+
+    cl.def(
+        "append",
+        [](Vector &v, const T &value) { v.push_back(value); },
+        arg("x"),
+        "Add an item to the end of the list");
+
+    cl.def(init([](const iterable &it) {
+        auto v = std::unique_ptr<Vector>(new Vector());
+        v->reserve(len_hint(it));
+        for (handle h : it) {
+            v->push_back(h.cast<T>());
+        }
+        return v.release();
+    }));
+
+    cl.def("clear", [](Vector &v) { v.clear(); }, "Clear the contents");
+
+    cl.def(
+        "extend",
+        [](Vector &v, const Vector &src) { v.insert(v.end(), src.begin(), src.end()); },
+        arg("L"),
+        "Extend the list by appending all the items in the given list");
+
+    cl.def(
+        "extend",
+        [](Vector &v, const iterable &it) {
+            const size_t old_size = v.size();
+            v.reserve(old_size + len_hint(it));
+            try {
+                for (handle h : it) {
+                    v.push_back(h.cast<T>());
+                }
+            } catch (const cast_error &) {
+                v.erase(v.begin() + static_cast<typename Vector::difference_type>(old_size),
+                        v.end());
+                try {
+                    v.shrink_to_fit();
+                } catch (const std::exception &) { // NOLINT(bugprone-empty-catch)
+                    // Do nothing
+                }
+                throw;
+            }
+        },
+        arg("L"),
+        "Extend the list by appending all the items in the given list");
+
+    cl.def(
+        "insert",
+        [](Vector &v, DiffType i, const T &x) {
+            // Can't use wrap_i; i == v.size() is OK
+            if (i < 0) {
+                i += v.size();
+            }
+            if (i < 0 || (SizeType) i > v.size()) {
+                throw index_error();
+            }
+            v.insert(v.begin() + i, x);
+        },
+        arg("i"),
+        arg("x"),
+        "Insert an item at a given position.");
+
+    cl.def(
+        "pop",
+        [](Vector &v) {
+            if (v.empty()) {
+                throw index_error();
+            }
+            T t = std::move(v.back());
+            v.pop_back();
+            return t;
+        },
+        "Remove and return the last item");
+
+    cl.def(
+        "pop",
+        [wrap_i](Vector &v, DiffType i) {
+            i = wrap_i(i, v.size());
+            T t = std::move(v[(SizeType) i]);
+            v.erase(std::next(v.begin(), i));
+            return t;
+        },
+        arg("i"),
+        "Remove and return the item at index ``i``");
+
+    cl.def("__setitem__", [wrap_i](Vector &v, DiffType i, const T &t) {
+        i = wrap_i(i, v.size());
+        v[(SizeType) i] = t;
+    });
+
+    /// Slicing protocol
+    cl.def(
+        "__getitem__",
+        [](const Vector &v, const slice &slice) -> Vector * {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            auto *seq = new Vector();
+            seq->reserve((size_t) slicelength);
+
+            for (size_t i = 0; i < slicelength; ++i) {
+                seq->push_back(v[start]);
+                start += step;
+            }
+            return seq;
+        },
+        arg("s"),
+        "Retrieve list elements using a slice object");
+
+    cl.def(
+        "__setitem__",
+        [](Vector &v, const slice &slice, const Vector &value) {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            if (slicelength != value.size()) {
+                throw std::runtime_error(
+                    "Left and right hand size of slice assignment have different sizes!");
+            }
+
+            for (size_t i = 0; i < slicelength; ++i) {
+                v[start] = value[i];
+                start += step;
+            }
+        },
+        "Assign list elements using a slice object");
+
+    cl.def(
+        "__delitem__",
+        [wrap_i](Vector &v, DiffType i) {
+            i = wrap_i(i, v.size());
+            v.erase(v.begin() + i);
+        },
+        "Delete the list elements at index ``i``");
+
+    cl.def(
+        "__delitem__",
+        [](Vector &v, const slice &slice) {
+            size_t start = 0, stop = 0, step = 0, slicelength = 0;
+
+            if (!slice.compute(v.size(), &start, &stop, &step, &slicelength)) {
+                throw error_already_set();
+            }
+
+            if (step == 1 && false) {
+                v.erase(v.begin() + (DiffType) start, v.begin() + DiffType(start + slicelength));
+            } else {
+                for (size_t i = 0; i < slicelength; ++i) {
+                    v.erase(v.begin() + DiffType(start));
+                    start += step - 1;
+                }
+            }
+        },
+        "Delete list elements using a slice object");
+}
+
+// If the type has an operator[] that doesn't return a reference (most notably std::vector<bool>),
+// we have to access by copying; otherwise we return by reference.
+template <typename Vector>
+using vector_needs_copy
+    = negation<std::is_same<decltype(std::declval<Vector>()[typename Vector::size_type()]),
+                            typename Vector::value_type &>>;
+
+// The usual case: access and iterate by reference
+template <typename Vector, typename Class_>
+void vector_accessor(enable_if_t<!vector_needs_copy<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+    using ItType = typename Vector::iterator;
+
+    auto wrap_i = [](DiffType i, SizeType n) {
+        if (i < 0) {
+            i += n;
+        }
+        if (i < 0 || (SizeType) i >= n) {
+            throw index_error();
+        }
+        return i;
+    };
+
+    cl.def(
+        "__getitem__",
+        [wrap_i](Vector &v, DiffType i) -> T & {
+            i = wrap_i(i, v.size());
+            return v[(SizeType) i];
+        },
+        return_value_policy::reference_internal // ref + keepalive
+    );
+
+    cl.def(
+        "__iter__",
+        [](Vector &v) {
+            return make_iterator<return_value_policy::reference_internal, ItType, ItType, T &>(
+                v.begin(), v.end());
+        },
+        keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
+    );
+}
+
+// The case for special objects, like std::vector<bool>, that have to be returned-by-copy:
+template <typename Vector, typename Class_>
+void vector_accessor(enable_if_t<vector_needs_copy<Vector>::value, Class_> &cl) {
+    using T = typename Vector::value_type;
+    using SizeType = typename Vector::size_type;
+    using DiffType = typename Vector::difference_type;
+    using ItType = typename Vector::iterator;
+    cl.def("__getitem__", [](const Vector &v, DiffType i) -> T {
+        if (i < 0) {
+            i += v.size();
+            if (i < 0) {
+                throw index_error();
+            }
+        }
+        auto i_st = static_cast<SizeType>(i);
+        if (i_st >= v.size()) {
+            throw index_error();
+        }
+        return v[i_st];
+    });
+
+    cl.def(
+        "__iter__",
+        [](Vector &v) {
+            return make_iterator<return_value_policy::copy, ItType, ItType, T>(v.begin(), v.end());
+        },
+        keep_alive<0, 1>() /* Essential: keep list alive while iterator exists */
+    );
+}
+
+template <typename Vector, typename Class_>
+auto vector_if_insertion_operator(Class_ &cl, std::string const &name)
+    -> decltype(std::declval<std::ostream &>() << std::declval<typename Vector::value_type>(),
+                void()) {
+    using size_type = typename Vector::size_type;
+
+    cl.def(
+        "__repr__",
+        [name](Vector &v) {
+            std::ostringstream s;
+            s << name << '[';
+            for (size_type i = 0; i < v.size(); ++i) {
+                s << v[i];
+                if (i != v.size() - 1) {
+                    s << ", ";
+                }
+            }
+            s << ']';
+            return s.str();
+        },
+        "Return the canonical string representation of this list.");
+}
+
+// Provide the buffer interface for vectors if we have data() and we have a format for it
+// GCC seems to have "void std::vector<bool>::data()" - doing SFINAE on the existence of data()
+// is insufficient, we need to check it returns an appropriate pointer
+template <typename Vector, typename = void>
+struct vector_has_data_and_format : std::false_type {};
+template <typename Vector>
+struct vector_has_data_and_format<
+    Vector,
+    enable_if_t<std::is_same<decltype(format_descriptor<typename Vector::value_type>::format(),
+                                      std::declval<Vector>().data()),
+                             typename Vector::value_type *>::value>> : std::true_type {};
+
+// [workaround(intel)] Separate function required here
+// Workaround as the Intel compiler does not compile the enable_if_t part below
+// (tested with icc (ICC) 2021.1 Beta 20200827)
+template <typename... Args>
+constexpr bool args_any_are_buffer() {
+    return detail::any_of<std::is_same<Args, buffer_protocol>...>::value;
+}
+
+// [workaround(intel)] Separate function required here
+// [workaround(msvc)] Can't use constexpr bool in return type
+
+// Add the buffer interface to a vector
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer_impl(Class_ &cl, std::true_type) {
+    using T = typename Vector::value_type;
+
+    static_assert(vector_has_data_and_format<Vector>::value,
+                  "There is not an appropriate format descriptor for this vector");
+
+    // numpy.h declares this for arbitrary types, but it may raise an exception and crash hard
+    // at runtime if PYBIND11_NUMPY_DTYPE hasn't been called, so check here
+    format_descriptor<T>::format();
+
+    cl.def_buffer([](Vector &v) -> buffer_info {
+        return buffer_info(v.data(),
+                           static_cast<ssize_t>(sizeof(T)),
+                           format_descriptor<T>::format(),
+                           1,
+                           {v.size()},
+                           {sizeof(T)});
+    });
+
+    cl.def(init([](const buffer &buf) {
+        auto info = buf.request();
+        if (info.ndim != 1 || info.strides[0] % static_cast<ssize_t>(sizeof(T))) {
+            throw type_error("Only valid 1D buffers can be copied to a vector");
+        }
+        if (!detail::compare_buffer_info<T>::compare(info)
+            || (ssize_t) sizeof(T) != info.itemsize) {
+            throw type_error("Format mismatch (Python: " + info.format
+                             + " C++: " + format_descriptor<T>::format() + ")");
+        }
+
+        T *p = static_cast<T *>(info.ptr);
+        ssize_t step = info.strides[0] / static_cast<ssize_t>(sizeof(T));
+        T *end = p + info.shape[0] * step;
+        if (step == 1) {
+            return Vector(p, end);
+        }
+        Vector vec;
+        vec.reserve((size_t) info.shape[0]);
+        for (; p != end; p += step) {
+            vec.push_back(*p);
+        }
+        return vec;
+    }));
+
+    return;
+}
+
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer_impl(Class_ &, std::false_type) {}
+
+template <typename Vector, typename Class_, typename... Args>
+void vector_buffer(Class_ &cl) {
+    vector_buffer_impl<Vector, Class_, Args...>(
+        cl, detail::any_of<std::is_same<Args, buffer_protocol>...>{});
+}
+
+PYBIND11_NAMESPACE_END(detail)
+
+//
+// std::vector
+//
+template <typename Vector, typename holder_type = std::unique_ptr<Vector>, typename... Args>
+class_<Vector, holder_type> bind_vector(handle scope, std::string const &name, Args &&...args) {
+    using Class_ = class_<Vector, holder_type>;
+
+    // If the value_type is unregistered (e.g. a converting type) or is itself registered
+    // module-local then make the vector binding module-local as well:
+    using vtype = typename Vector::value_type;
+    auto *vtype_info = detail::get_type_info(typeid(vtype));
+    bool local = !vtype_info || vtype_info->module_local;
+
+    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
+
+    // Declare the buffer interface if a buffer_protocol() is passed in
+    detail::vector_buffer<Vector, Class_, Args...>(cl);
+
+    cl.def(init<>());
+
+    // Register copy constructor (if possible)
+    detail::vector_if_copy_constructible<Vector, Class_>(cl);
+
+    // Register comparison-related operators and functions (if possible)
+    detail::vector_if_equal_operator<Vector, Class_>(cl);
+
+    // Register stream insertion operator (if possible)
+    detail::vector_if_insertion_operator<Vector, Class_>(cl, name);
+
+    // Modifiers require copyable vector value type
+    detail::vector_modifiers<Vector, Class_>(cl);
+
+    // Accessor and iterator; return by value if copyable, otherwise we return by ref + keep-alive
+    detail::vector_accessor<Vector, Class_>(cl);
+
+    cl.def(
+        "__bool__",
+        [](const Vector &v) -> bool { return !v.empty(); },
+        "Check whether the list is nonempty");
+
+    cl.def("__len__", [](const Vector &vec) { return vec.size(); });
+
+#if 0
+    // C++ style functions deprecated, leaving it here as an example
+    cl.def(init<size_type>());
+
+    cl.def("resize",
+         (void (Vector::*) (size_type count)) & Vector::resize,
+         "changes the number of elements stored");
+
+    cl.def("erase",
+        [](Vector &v, SizeType i) {
+        if (i >= v.size())
+            throw index_error();
+        v.erase(v.begin() + i);
+    }, "erases element at index ``i``");
+
+    cl.def("empty",         &Vector::empty,         "checks whether the container is empty");
+    cl.def("size",          &Vector::size,          "returns the number of elements");
+    cl.def("push_back", (void (Vector::*)(const T&)) &Vector::push_back, "adds an element to the end");
+    cl.def("pop_back",                               &Vector::pop_back, "removes the last element");
+
+    cl.def("max_size",      &Vector::max_size,      "returns the maximum possible number of elements");
+    cl.def("reserve",       &Vector::reserve,       "reserves storage");
+    cl.def("capacity",      &Vector::capacity,      "returns the number of elements that can be held in currently allocated storage");
+    cl.def("shrink_to_fit", &Vector::shrink_to_fit, "reduces memory usage by freeing unused memory");
+
+    cl.def("clear", &Vector::clear, "clears the contents");
+    cl.def("swap",   &Vector::swap, "swaps the contents");
+
+    cl.def("front", [](Vector &v) {
+        if (v.size()) return v.front();
+        else throw index_error();
+    }, "access the first element");
+
+    cl.def("back", [](Vector &v) {
+        if (v.size()) return v.back();
+        else throw index_error();
+    }, "access the last element ");
+
+#endif
+
+    return cl;
+}
+
+//
+// std::map, std::unordered_map
+//
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+/* Fallback functions */
+template <typename, typename, typename... Args>
+void map_if_insertion_operator(const Args &...) {}
+template <typename, typename, typename... Args>
+void map_assignment(const Args &...) {}
+
+// Map assignment when copy-assignable: just copy the value
+template <typename Map, typename Class_>
+void map_assignment(
+    enable_if_t<is_copy_assignable<typename Map::mapped_type>::value, Class_> &cl) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+
+    cl.def("__setitem__", [](Map &m, const KeyType &k, const MappedType &v) {
+        auto it = m.find(k);
+        if (it != m.end()) {
+            it->second = v;
+        } else {
+            m.emplace(k, v);
+        }
+    });
+}
+
+// Not copy-assignable, but still copy-constructible: we can update the value by erasing and
+// reinserting
+template <typename Map, typename Class_>
+void map_assignment(enable_if_t<!is_copy_assignable<typename Map::mapped_type>::value
+                                    && is_copy_constructible<typename Map::mapped_type>::value,
+                                Class_> &cl) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+
+    cl.def("__setitem__", [](Map &m, const KeyType &k, const MappedType &v) {
+        // We can't use m[k] = v; because value type might not be default constructable
+        auto r = m.emplace(k, v);
+        if (!r.second) {
+            // value type is not copy assignable so the only way to insert it is to erase it
+            // first...
+            m.erase(r.first);
+            m.emplace(k, v);
+        }
+    });
+}
+
+template <typename Map, typename Class_>
+auto map_if_insertion_operator(Class_ &cl, std::string const &name)
+    -> decltype(std::declval<std::ostream &>() << std::declval<typename Map::key_type>()
+                                               << std::declval<typename Map::mapped_type>(),
+                void()) {
+
+    cl.def(
+        "__repr__",
+        [name](Map &m) {
+            std::ostringstream s;
+            s << name << '{';
+            bool f = false;
+            for (auto const &kv : m) {
+                if (f) {
+                    s << ", ";
+                }
+                s << kv.first << ": " << kv.second;
+                f = true;
+            }
+            s << '}';
+            return s.str();
+        },
+        "Return the canonical string representation of this map.");
+}
+
+struct keys_view {
+    virtual size_t len() = 0;
+    virtual iterator iter() = 0;
+    virtual bool contains(const handle &k) = 0;
+    virtual ~keys_view() = default;
+};
+
+struct values_view {
+    virtual size_t len() = 0;
+    virtual iterator iter() = 0;
+    virtual ~values_view() = default;
+};
+
+struct items_view {
+    virtual size_t len() = 0;
+    virtual iterator iter() = 0;
+    virtual ~items_view() = default;
+};
+
+template <typename Map>
+struct KeysViewImpl : public detail::keys_view {
+    explicit KeysViewImpl(Map &map) : map(map) {}
+    size_t len() override { return map.size(); }
+    iterator iter() override { return make_key_iterator(map.begin(), map.end()); }
+    bool contains(const handle &k) override {
+        try {
+            return map.find(k.template cast<typename Map::key_type>()) != map.end();
+        } catch (const cast_error &) {
+            return false;
+        }
+    }
+    Map &map;
+};
+
+template <typename Map>
+struct ValuesViewImpl : public detail::values_view {
+    explicit ValuesViewImpl(Map &map) : map(map) {}
+    size_t len() override { return map.size(); }
+    iterator iter() override { return make_value_iterator(map.begin(), map.end()); }
+    Map &map;
+};
+
+template <typename Map>
+struct ItemsViewImpl : public detail::items_view {
+    explicit ItemsViewImpl(Map &map) : map(map) {}
+    size_t len() override { return map.size(); }
+    iterator iter() override { return make_iterator(map.begin(), map.end()); }
+    Map &map;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+
+template <typename Map, typename holder_type = std::unique_ptr<Map>, typename... Args>
+class_<Map, holder_type> bind_map(handle scope, const std::string &name, Args &&...args) {
+    using KeyType = typename Map::key_type;
+    using MappedType = typename Map::mapped_type;
+    using KeysView = detail::keys_view;
+    using ValuesView = detail::values_view;
+    using ItemsView = detail::items_view;
+    using Class_ = class_<Map, holder_type>;
+
+    // If either type is a non-module-local bound type then make the map binding non-local as well;
+    // otherwise (e.g. both types are either module-local or converting) the map will be
+    // module-local.
+    auto *tinfo = detail::get_type_info(typeid(MappedType));
+    bool local = !tinfo || tinfo->module_local;
+    if (local) {
+        tinfo = detail::get_type_info(typeid(KeyType));
+        local = !tinfo || tinfo->module_local;
+    }
+
+    Class_ cl(scope, name.c_str(), pybind11::module_local(local), std::forward<Args>(args)...);
+
+    // Wrap KeysView if it wasn't already wrapped
+    if (!detail::get_type_info(typeid(KeysView))) {
+        class_<KeysView> keys_view(scope, "KeysView", pybind11::module_local(local));
+        keys_view.def("__len__", &KeysView::len);
+        keys_view.def("__iter__",
+                      &KeysView::iter,
+                      keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+        );
+        keys_view.def("__contains__", &KeysView::contains);
+    }
+    // Similarly for ValuesView:
+    if (!detail::get_type_info(typeid(ValuesView))) {
+        class_<ValuesView> values_view(scope, "ValuesView", pybind11::module_local(local));
+        values_view.def("__len__", &ValuesView::len);
+        values_view.def("__iter__",
+                        &ValuesView::iter,
+                        keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+        );
+    }
+    // Similarly for ItemsView:
+    if (!detail::get_type_info(typeid(ItemsView))) {
+        class_<ItemsView> items_view(scope, "ItemsView", pybind11::module_local(local));
+        items_view.def("__len__", &ItemsView::len);
+        items_view.def("__iter__",
+                       &ItemsView::iter,
+                       keep_alive<0, 1>() /* Essential: keep view alive while iterator exists */
+        );
+    }
+
+    cl.def(init<>());
+
+    // Register stream insertion operator (if possible)
+    detail::map_if_insertion_operator<Map, Class_>(cl, name);
+
+    cl.def(
+        "__bool__",
+        [](const Map &m) -> bool { return !m.empty(); },
+        "Check whether the map is nonempty");
+
+    cl.def(
+        "__iter__",
+        [](Map &m) { return make_key_iterator(m.begin(), m.end()); },
+        keep_alive<0, 1>() /* Essential: keep map alive while iterator exists */
+    );
+
+    cl.def(
+        "keys",
+        [](Map &m) { return std::unique_ptr<KeysView>(new detail::KeysViewImpl<Map>(m)); },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "values",
+        [](Map &m) { return std::unique_ptr<ValuesView>(new detail::ValuesViewImpl<Map>(m)); },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "items",
+        [](Map &m) { return std::unique_ptr<ItemsView>(new detail::ItemsViewImpl<Map>(m)); },
+        keep_alive<0, 1>() /* Essential: keep map alive while view exists */
+    );
+
+    cl.def(
+        "__getitem__",
+        [](Map &m, const KeyType &k) -> MappedType & {
+            auto it = m.find(k);
+            if (it == m.end()) {
+                throw key_error();
+            }
+            return it->second;
+        },
+        return_value_policy::reference_internal // ref + keepalive
+    );
+
+    cl.def("__contains__", [](Map &m, const KeyType &k) -> bool {
+        auto it = m.find(k);
+        if (it == m.end()) {
+            return false;
+        }
+        return true;
+    });
+    // Fallback for when the object is not of the key type
+    cl.def("__contains__", [](Map &, const object &) -> bool { return false; });
+
+    // Assignment provided only if the type is copyable
+    detail::map_assignment<Map, Class_>(cl);
+
+    cl.def("__delitem__", [](Map &m, const KeyType &k) {
+        auto it = m.find(k);
+        if (it == m.end()) {
+            throw key_error();
+        }
+        m.erase(it);
+    });
+
+    // Always use a lambda in case of `using` declaration
+    cl.def("__len__", [](const Map &m) { return m.size(); });
+
+    return cl;
+}
+
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/type_caster_pyobject_ptr.h b/lib/python3.11/site-packages/torch/include/pybind11/type_caster_pyobject_ptr.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa914f9e1563b3480481865f50adb7358f1a7134
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/type_caster_pyobject_ptr.h
@@ -0,0 +1,61 @@
+// Copyright (c) 2023 The pybind Community.
+
+#pragma once
+
+#include "detail/common.h"
+#include "detail/descr.h"
+#include "cast.h"
+#include "pytypes.h"
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <>
+class type_caster<PyObject> {
+public:
+    static constexpr auto name = const_name("object"); // See discussion under PR #4601.
+
+    // This overload is purely to guard against accidents.
+    template <typename T,
+              detail::enable_if_t<!is_same_ignoring_cvref<T, PyObject *>::value, int> = 0>
+    static handle cast(T &&, return_value_policy, handle /*parent*/) {
+        static_assert(is_same_ignoring_cvref<T, PyObject *>::value,
+                      "Invalid C++ type T for to-Python conversion (type_caster<PyObject>).");
+        return nullptr; // Unreachable.
+    }
+
+    static handle cast(PyObject *src, return_value_policy policy, handle /*parent*/) {
+        if (src == nullptr) {
+            throw error_already_set();
+        }
+        if (PyErr_Occurred()) {
+            raise_from(PyExc_SystemError, "src != nullptr but PyErr_Occurred()");
+            throw error_already_set();
+        }
+        if (policy == return_value_policy::take_ownership) {
+            return src;
+        }
+        if (policy == return_value_policy::reference
+            || policy == return_value_policy::automatic_reference) {
+            return handle(src).inc_ref();
+        }
+        pybind11_fail("type_caster<PyObject>::cast(): unsupported return_value_policy: "
+                      + std::to_string(static_cast<int>(policy)));
+    }
+
+    bool load(handle src, bool) {
+        value = reinterpret_borrow<object>(src);
+        return true;
+    }
+
+    template <typename T>
+    using cast_op_type = PyObject *;
+
+    explicit operator PyObject *() { return value.ptr(); }
+
+private:
+    object value;
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/pybind11/typing.h b/lib/python3.11/site-packages/torch/include/pybind11/typing.h
new file mode 100644
index 0000000000000000000000000000000000000000..84aaf9f702d780b1415b806f4efc7ebfd3d2675d
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/pybind11/typing.h
@@ -0,0 +1,242 @@
+/*
+    pybind11/typing.h: Convenience wrapper classes for basic Python types
+    with more explicit annotations.
+
+    Copyright (c) 2023 Dustin Spicuzza <dustin@virtualroadside.com>
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "detail/common.h"
+#include "cast.h"
+#include "pytypes.h"
+
+#include <algorithm>
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+PYBIND11_NAMESPACE_BEGIN(typing)
+
+/*
+    The following types can be used to direct pybind11-generated docstrings
+    to have have more explicit types (e.g., `list[str]` instead of `list`).
+    Just use these in place of existing types.
+
+    There is no additional enforcement of types at runtime.
+*/
+
+template <typename... Types>
+class Tuple : public tuple {
+    using tuple::tuple;
+};
+
+template <typename K, typename V>
+class Dict : public dict {
+    using dict::dict;
+};
+
+template <typename T>
+class List : public list {
+    using list::list;
+};
+
+template <typename T>
+class Set : public set {
+    using set::set;
+};
+
+template <typename T>
+class Iterable : public iterable {
+    using iterable::iterable;
+};
+
+template <typename T>
+class Iterator : public iterator {
+    using iterator::iterator;
+};
+
+template <typename Signature>
+class Callable;
+
+template <typename Return, typename... Args>
+class Callable<Return(Args...)> : public function {
+    using function::function;
+};
+
+template <typename T>
+class Type : public type {
+    using type::type;
+};
+
+template <typename... Types>
+class Union : public object {
+    PYBIND11_OBJECT_DEFAULT(Union, object, PyObject_Type)
+    using object::object;
+};
+
+template <typename T>
+class Optional : public object {
+    PYBIND11_OBJECT_DEFAULT(Optional, object, PyObject_Type)
+    using object::object;
+};
+
+template <typename T>
+class TypeGuard : public bool_ {
+    using bool_::bool_;
+};
+
+template <typename T>
+class TypeIs : public bool_ {
+    using bool_::bool_;
+};
+
+class NoReturn : public none {
+    using none::none;
+};
+
+class Never : public none {
+    using none::none;
+};
+
+#if defined(__cpp_nontype_template_args) && __cpp_nontype_template_args >= 201911L
+#    define PYBIND11_TYPING_H_HAS_STRING_LITERAL
+template <size_t N>
+struct StringLiteral {
+    constexpr StringLiteral(const char (&str)[N]) { std::copy_n(str, N, name); }
+    char name[N];
+};
+
+template <StringLiteral... StrLits>
+class Literal : public object {
+    PYBIND11_OBJECT_DEFAULT(Literal, object, PyObject_Type)
+};
+
+// Example syntax for creating a TypeVar.
+// typedef typing::TypeVar<"T"> TypeVarT;
+template <StringLiteral>
+class TypeVar : public object {
+    PYBIND11_OBJECT_DEFAULT(TypeVar, object, PyObject_Type)
+    using object::object;
+};
+#endif
+
+PYBIND11_NAMESPACE_END(typing)
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+template <typename... Types>
+struct handle_type_name<typing::Tuple<Types...>> {
+    static constexpr auto name = const_name("tuple[")
+                                 + ::pybind11::detail::concat(make_caster<Types>::name...)
+                                 + const_name("]");
+};
+
+template <>
+struct handle_type_name<typing::Tuple<>> {
+    // PEP 484 specifies this syntax for an empty tuple
+    static constexpr auto name = const_name("tuple[()]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Tuple<T, ellipsis>> {
+    // PEP 484 specifies this syntax for a variable-length tuple
+    static constexpr auto name
+        = const_name("tuple[") + make_caster<T>::name + const_name(", ...]");
+};
+
+template <typename K, typename V>
+struct handle_type_name<typing::Dict<K, V>> {
+    static constexpr auto name = const_name("dict[") + make_caster<K>::name + const_name(", ")
+                                 + make_caster<V>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::List<T>> {
+    static constexpr auto name = const_name("list[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Set<T>> {
+    static constexpr auto name = const_name("set[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Iterable<T>> {
+    static constexpr auto name = const_name("Iterable[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Iterator<T>> {
+    static constexpr auto name = const_name("Iterator[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename Return, typename... Args>
+struct handle_type_name<typing::Callable<Return(Args...)>> {
+    using retval_type = conditional_t<std::is_same<Return, void>::value, void_type, Return>;
+    static constexpr auto name
+        = const_name("Callable[[") + ::pybind11::detail::concat(make_caster<Args>::name...)
+          + const_name("], ") + make_caster<retval_type>::name + const_name("]");
+};
+
+template <typename Return>
+struct handle_type_name<typing::Callable<Return(ellipsis)>> {
+    // PEP 484 specifies this syntax for defining only return types of callables
+    using retval_type = conditional_t<std::is_same<Return, void>::value, void_type, Return>;
+    static constexpr auto name
+        = const_name("Callable[..., ") + make_caster<retval_type>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Type<T>> {
+    static constexpr auto name = const_name("type[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename... Types>
+struct handle_type_name<typing::Union<Types...>> {
+    static constexpr auto name = const_name("Union[")
+                                 + ::pybind11::detail::concat(make_caster<Types>::name...)
+                                 + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::Optional<T>> {
+    static constexpr auto name = const_name("Optional[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::TypeGuard<T>> {
+    static constexpr auto name = const_name("TypeGuard[") + make_caster<T>::name + const_name("]");
+};
+
+template <typename T>
+struct handle_type_name<typing::TypeIs<T>> {
+    static constexpr auto name = const_name("TypeIs[") + make_caster<T>::name + const_name("]");
+};
+
+template <>
+struct handle_type_name<typing::NoReturn> {
+    static constexpr auto name = const_name("NoReturn");
+};
+
+template <>
+struct handle_type_name<typing::Never> {
+    static constexpr auto name = const_name("Never");
+};
+
+#if defined(PYBIND11_TYPING_H_HAS_STRING_LITERAL)
+template <typing::StringLiteral... Literals>
+struct handle_type_name<typing::Literal<Literals...>> {
+    static constexpr auto name = const_name("Literal[")
+                                 + pybind11::detail::concat(const_name(Literals.name)...)
+                                 + const_name("]");
+};
+template <typing::StringLiteral StrLit>
+struct handle_type_name<typing::TypeVar<StrLit>> {
+    static constexpr auto name = const_name(StrLit.name);
+};
+#endif
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/channel/basic/factory.h b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/basic/factory.h
new file mode 100644
index 0000000000000000000000000000000000000000..b9c55316d32facf8fcff501d9308aee90b4033a5
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/basic/factory.h
@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+
+#include <tensorpipe/channel/context.h>
+
+namespace tensorpipe {
+namespace channel {
+namespace basic {
+
+std::shared_ptr<Context> create();
+
+} // namespace basic
+} // namespace channel
+} // namespace tensorpipe
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/channel/context.h b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/context.h
new file mode 100644
index 0000000000000000000000000000000000000000..cbcef807f6b4c345252504bdbef518405d4584d4
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/context.h
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <tensorpipe/common/buffer.h>
+#include <tensorpipe/transport/context.h>
+
+namespace tensorpipe {
+namespace channel {
+
+enum class Endpoint : bool { kConnect, kListen };
+
+class Channel;
+
+// Abstract base class for channel context classes.
+//
+// Instances of these classes are expected to be registered with a
+// context. All registered instances are assumed to be eligible
+// channels for all pairs.
+//
+class Context {
+ public:
+  // Return whether the context is able to operate correctly.
+  //
+  // Some channel types may be unable to perform as intended under some
+  // circumstances (e.g., specialized hardware unavailable, lack of
+  // permissions). They can report it through this method in order for
+  // the core context to avoid registering them in the first place.
+  //
+  virtual bool isViable() const = 0;
+
+  // Return the number of control connections needed to create an instance of
+  // this channel.
+  //
+  // Most channels require only one, but some require more (cuda_basic), and
+  // some might require none.
+  //
+  virtual size_t numConnectionsNeeded() const = 0;
+
+  // Return a map from supported devices to strings describing the device from
+  // the channel's perspective.
+  //
+  // Two processes with a channel context of the same type can leverage this
+  // channel to make two devices communicate if one side's device descriptor is
+  // "accepted" by the other one, using the canCommunicateWithRemote method
+  // below. That method must be symmetric, and unless overridden defaults to
+  // string comparison.
+  //
+  virtual const std::unordered_map<Device, std::string>& deviceDescriptors()
+      const = 0;
+
+  // Compare local and remote device descriptors for compatibility.
+  //
+  // Determine whether a channel can be opened between a local device and
+  // a remote one that has the given device descriptor. This function
+  // needs to be symmetric: if we called this method on the remote
+  // context with the local descriptor we should get the same answer.
+  // Unless overridden it defaults to string comparison.
+  //
+  virtual bool canCommunicateWithRemote(
+      const std::string& localDeviceDescriptor,
+      const std::string& remoteDeviceDescriptor) const = 0;
+
+  // Return newly created channel using the specified connections.
+  //
+  // It is up to the channel to either use these connections for further
+  // initialization, or use them directly. Either way, the returned
+  // channel should be immediately usable. If the channel isn't fully
+  // initialized yet, take care to queue these operations to execute
+  // as soon as initialization has completed.
+  //
+  virtual std::shared_ptr<Channel> createChannel(
+      std::vector<std::shared_ptr<transport::Connection>>,
+      Endpoint) = 0;
+
+  // Tell the context what its identifier is.
+  //
+  // This is only supposed to be called from the high-level context. It will
+  // only used for logging and debugging purposes.
+  virtual void setId(std::string id) = 0;
+
+  // Put the channel context in a terminal state, in turn closing all of its
+  // channels, and release its resources. This may be done asynchronously, in
+  // background.
+  virtual void close() = 0;
+
+  // Wait for all resources to be released and all background activity to stop.
+  virtual void join() = 0;
+
+  virtual ~Context() = default;
+
+ private:
+  std::string name_;
+};
+
+} // namespace channel
+} // namespace tensorpipe
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/channel/error.h b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/error.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d4bbec479f33b122403cc35937e58dbf32ce755
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/channel/error.h
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <string>
+
+#include <tensorpipe/common/error.h>
+
+namespace tensorpipe {
+namespace channel {
+
+class ContextClosedError final : public BaseError {
+ public:
+  ContextClosedError() {}
+
+  std::string what() const override;
+};
+
+class ChannelClosedError final : public BaseError {
+ public:
+  ChannelClosedError() {}
+
+  std::string what() const override;
+};
+
+class ContextNotViableError final : public BaseError {
+ public:
+  ContextNotViableError() {}
+
+  std::string what() const override;
+};
+
+} // namespace channel
+} // namespace tensorpipe
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/config.h b/lib/python3.11/site-packages/torch/include/tensorpipe/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..1274da5b0a3d0066246b8f88e0d0992e98f01021
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/config.h
@@ -0,0 +1,14 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#define TENSORPIPE_HAS_SHM_TRANSPORT 1
+#define TENSORPIPE_HAS_IBV_TRANSPORT 1
+
+#define TENSORPIPE_HAS_CMA_CHANNEL 1
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/config_cuda.h b/lib/python3.11/site-packages/torch/include/tensorpipe/config_cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..03591662ece703a2cf751927edc8036db964de8c
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/config_cuda.h
@@ -0,0 +1,12 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#define TENSORPIPE_HAS_CUDA_IPC_CHANNEL 1
+#define TENSORPIPE_HAS_CUDA_GDR_CHANNEL 1
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe.h b/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae273575457382f556c0be7a9a68fc2c73fde9fb
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe.h
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <tensorpipe/config.h>
+
+// High-level API
+
+#include <tensorpipe/core/context.h>
+#include <tensorpipe/core/error.h>
+#include <tensorpipe/core/listener.h>
+#include <tensorpipe/core/message.h>
+#include <tensorpipe/core/pipe.h>
+
+#include <tensorpipe/common/buffer.h>
+
+#include <tensorpipe/common/cpu_buffer.h>
+
+// Transports
+
+#include <tensorpipe/transport/context.h>
+#include <tensorpipe/transport/error.h>
+
+#include <tensorpipe/transport/uv/error.h>
+#include <tensorpipe/transport/uv/factory.h>
+#include <tensorpipe/transport/uv/utility.h>
+
+#if TENSORPIPE_HAS_SHM_TRANSPORT
+#include <tensorpipe/transport/shm/factory.h>
+#endif // TENSORPIPE_HAS_SHM_TRANSPORT
+
+#if TENSORPIPE_HAS_IBV_TRANSPORT
+#include <tensorpipe/transport/ibv/error.h>
+#include <tensorpipe/transport/ibv/factory.h>
+#include <tensorpipe/transport/ibv/utility.h>
+#endif // TENSORPIPE_HAS_IBV_TRANSPORT
+
+// Channels
+
+#include <tensorpipe/channel/context.h>
+#include <tensorpipe/channel/error.h>
+
+#include <tensorpipe/channel/basic/factory.h>
+#include <tensorpipe/channel/mpt/factory.h>
+#include <tensorpipe/channel/xth/factory.h>
+
+#if TENSORPIPE_HAS_CMA_CHANNEL
+#include <tensorpipe/channel/cma/factory.h>
+#endif // TENSORPIPE_HAS_CMA_CHANNEL
diff --git a/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe_cuda.h b/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe_cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..b841085cd308abf88bc52cd74690ac9e22e938e1
--- /dev/null
+++ b/lib/python3.11/site-packages/torch/include/tensorpipe/tensorpipe_cuda.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <tensorpipe/config_cuda.h>
+
+// High-level API
+
+#include <tensorpipe/common/cuda_buffer.h>
+
+// Channels
+
+#include <tensorpipe/channel/cuda_basic/factory.h>
+#include <tensorpipe/channel/cuda_xth/factory.h>
+
+#if TENSORPIPE_HAS_CUDA_GDR_CHANNEL
+#include <tensorpipe/channel/cuda_gdr/factory.h>
+#endif // TENSORPIPE_HAS_CUDA_GDR_CHANNEL
+
+#if TENSORPIPE_HAS_CUDA_IPC_CHANNEL
+#include <tensorpipe/channel/cuda_ipc/factory.h>
+#endif // TENSORPIPE_HAS_CUDA_IPC_CHANNEL