diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/__init__.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/__init__.py
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--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/__init__.py
@@ -0,0 +1,61 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import glob
+import inspect
+from os.path import basename, dirname, isfile, join
+
+import torch
+from torch._export.db.case import (
+    _EXAMPLE_CASES,
+    _EXAMPLE_CONFLICT_CASES,
+    _EXAMPLE_REWRITE_CASES,
+    SupportLevel,
+    export_case,
+    ExportCase,
+)
+
+
+def _collect_examples():
+    case_names = glob.glob(join(dirname(__file__), "*.py"))
+    case_names = [
+        basename(f)[:-3] for f in case_names if isfile(f) and not f.endswith("__init__.py")
+    ]
+
+    case_fields = {f.name for f in dataclasses.fields(ExportCase)}
+    for case_name in case_names:
+        case = __import__(case_name, globals(), locals(), [], 1)
+        variables = [name for name in dir(case) if name in case_fields]
+        export_case(**{v: getattr(case, v) for v in variables})(case.model)
+
+_collect_examples()
+
+def all_examples():
+    return _EXAMPLE_CASES
+
+
+if len(_EXAMPLE_CONFLICT_CASES) > 0:
+
+    def get_name(case):
+        model = case.model
+        if isinstance(model, torch.nn.Module):
+            model = type(model)
+        return model.__name__
+
+    msg = "Error on conflict export case name.\n"
+    for case_name, cases in _EXAMPLE_CONFLICT_CASES.items():
+        msg += f"Case name {case_name} is associated with multiple cases:\n  "
+        msg += f"[{','.join(map(get_name, cases))}]\n"
+
+    raise RuntimeError(msg)
+
+
+def filter_examples_by_support_level(support_level: SupportLevel):
+    return {
+        key: val
+        for key, val in all_examples().items()
+        if val.support_level == support_level
+    }
+
+
+def get_rewrite_cases(case):
+    return _EXAMPLE_REWRITE_CASES.get(case.name, [])
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diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/assume_constant_result.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/assume_constant_result.py
new file mode 100644
index 0000000000000000000000000000000000000000..931ce7f7a50fc5a175101ac57c424c88cf31b54c
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/assume_constant_result.py
@@ -0,0 +1,20 @@
+# mypy: allow-untyped-defs
+import torch
+import torch._dynamo as torchdynamo
+
+
+class AssumeConstantResult(torch.nn.Module):
+    """
+    Applying `assume_constant_result` decorator to burn make non-tracable code as constant.
+    """
+
+    @torchdynamo.assume_constant_result
+    def get_item(self, y):
+        return y.int().item()
+
+    def forward(self, x, y):
+        return x[: self.get_item(y)]
+
+example_args = (torch.randn(3, 2), torch.tensor(4))
+tags = {"torch.escape-hatch"}
+model = AssumeConstantResult()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/autograd_function.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/autograd_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..0458291e2176932b32b0fc2e44d51d5812dcead3
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/autograd_function.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+import torch
+
+class MyAutogradFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        return x.clone()
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output + 1
+
+class AutogradFunction(torch.nn.Module):
+    """
+    TorchDynamo does not keep track of backward() on autograd functions. We recommend to
+    use `allow_in_graph` to mitigate this problem.
+    """
+
+    def forward(self, x):
+        return MyAutogradFunction.apply(x)
+
+example_args = (torch.randn(3, 2),)
+model = AutogradFunction()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/class_method.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/class_method.py
new file mode 100644
index 0000000000000000000000000000000000000000..f701f54d4f4ea1cb5816292cd60bb4df3d03c5e8
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/class_method.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+import torch
+
+class ClassMethod(torch.nn.Module):
+    """
+    Class methods are inlined during tracing.
+    """
+
+    @classmethod
+    def method(cls, x):
+        return x + 1
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.linear = torch.nn.Linear(4, 2)
+
+    def forward(self, x):
+        x = self.linear(x)
+        return self.method(x) * self.__class__.method(x) * type(self).method(x)
+
+example_args = (torch.randn(3, 4),)
+model = ClassMethod()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_branch_class_method.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_branch_class_method.py
new file mode 100644
index 0000000000000000000000000000000000000000..22600cc504348d1d261b0ea2b9ed2d57af76b0a3
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_branch_class_method.py
@@ -0,0 +1,44 @@
+# mypy: allow-untyped-defs
+import torch
+
+from functorch.experimental.control_flow import cond
+
+class MySubModule(torch.nn.Module):
+    def foo(self, x):
+        return x.cos()
+
+    def forward(self, x):
+        return self.foo(x)
+
+class CondBranchClassMethod(torch.nn.Module):
+    """
+    The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:
+      - both branches must take the same args, which must also match the branch args passed to cond.
+      - both branches must return a single tensor
+      - returned tensor must have the same tensor metadata, e.g. shape and dtype
+      - branch function can be free function, nested function, lambda, class methods
+      - branch function can not have closure variables
+      - no inplace mutations on inputs or global variables
+
+
+    This example demonstrates using class method in cond().
+
+    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.subm = MySubModule()
+
+    def bar(self, x):
+        return x.sin()
+
+    def forward(self, x):
+        return cond(x.shape[0] <= 2, self.subm.forward, self.bar, [x])
+
+example_args = (torch.randn(3),)
+tags = {
+    "torch.cond",
+    "torch.dynamic-shape",
+}
+model = CondBranchClassMethod()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_closed_over_variable.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_closed_over_variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..183180ab4fc825385170fea2bec6af184374a67e
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_closed_over_variable.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+import torch
+
+from functorch.experimental.control_flow import cond
+
+class CondClosedOverVariable(torch.nn.Module):
+    """
+    torch.cond() supports branches closed over arbitrary variables.
+    """
+
+    def forward(self, pred, x):
+        def true_fn(val):
+            return x * 2
+
+        def false_fn(val):
+            return x - 2
+
+        return cond(pred, true_fn, false_fn, [x + 1])
+
+example_args = (torch.tensor(True), torch.randn(3, 2))
+tags = {"torch.cond", "python.closure"}
+model = CondClosedOverVariable()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_operands.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_operands.py
new file mode 100644
index 0000000000000000000000000000000000000000..60a75d24639cdac991298e99acf96b8eadbff442
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_operands.py
@@ -0,0 +1,35 @@
+# mypy: allow-untyped-defs
+import torch
+
+from torch.export import Dim
+
+x = torch.randn(3, 2)
+y = torch.randn(2)
+dim0_x = Dim("dim0_x")
+
+class CondOperands(torch.nn.Module):
+    """
+    The operands passed to cond() must be:
+    - a list of tensors
+    - match arguments of `true_fn` and `false_fn`
+
+    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
+    """
+
+    def forward(self, x, y):
+        def true_fn(x, y):
+            return x + y
+
+        def false_fn(x, y):
+            return x - y
+
+        return torch.cond(x.shape[0] > 2, true_fn, false_fn, [x, y])
+
+example_args = (x, y)
+tags = {
+    "torch.cond",
+    "torch.dynamic-shape",
+}
+extra_inputs = (torch.randn(2, 2), torch.randn(2))
+dynamic_shapes = {"x": {0: dim0_x}, "y": None}
+model = CondOperands()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_predicate.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_predicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..68bb8850bba909a0c6546c8f12a1a3fa1bdc70d1
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/cond_predicate.py
@@ -0,0 +1,25 @@
+# mypy: allow-untyped-defs
+import torch
+
+from functorch.experimental.control_flow import cond
+
+class CondPredicate(torch.nn.Module):
+    """
+    The conditional statement (aka predicate) passed to cond() must be one of the following:
+      - torch.Tensor with a single element
+      - boolean expression
+
+    NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.
+    """
+
+    def forward(self, x):
+        pred = x.dim() > 2 and x.shape[2] > 10
+
+        return cond(pred, lambda x: x.cos(), lambda y: y.sin(), [x])
+
+example_args = (torch.randn(6, 4, 3),)
+tags = {
+    "torch.cond",
+    "torch.dynamic-shape",
+}
+model = CondPredicate()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_size_example.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_size_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbed4984ac4d14c0b175874c44e9c7009bfe8ddc
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_size_example.py
@@ -0,0 +1,25 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class ConstrainAsSizeExample(torch.nn.Module):
+    """
+    If the value is not known at tracing time, you can provide hint so that we
+    can trace further. Please look at torch._check and torch._check_is_size APIs.
+    torch._check_is_size is used for values that NEED to be used for constructing
+    tensor.
+    """
+
+    def forward(self, x):
+        a = x.item()
+        torch._check_is_size(a)
+        torch._check(a <= 5)
+        return torch.zeros((a, 5))
+
+
+example_args = (torch.tensor(4),)
+tags = {
+    "torch.dynamic-value",
+    "torch.escape-hatch",
+}
+model = ConstrainAsSizeExample()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_value_example.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_value_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8bfc3d6e36578ee50f1ca2eee80fed9aae14cfd
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/constrain_as_value_example.py
@@ -0,0 +1,28 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class ConstrainAsValueExample(torch.nn.Module):
+    """
+    If the value is not known at tracing time, you can provide hint so that we
+    can trace further. Please look at torch._check and torch._check_is_size APIs.
+    torch._check is used for values that don't need to be used for constructing
+    tensor.
+    """
+
+    def forward(self, x, y):
+        a = x.item()
+        torch._check(a >= 0)
+        torch._check(a <= 5)
+
+        if a < 6:
+            return y.sin()
+        return y.cos()
+
+
+example_args = (torch.tensor(4), torch.randn(5, 5))
+tags = {
+    "torch.dynamic-value",
+    "torch.escape-hatch",
+}
+model = ConstrainAsValueExample()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/decorator.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d24cc681a6b62adf40bfd9a2e5283afb3515131
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/decorator.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+import functools
+
+import torch
+
+def test_decorator(func):
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        return func(*args, **kwargs) + 1
+
+    return wrapper
+
+class Decorator(torch.nn.Module):
+    """
+    Decorators calls are inlined into the exported function during tracing.
+    """
+
+    @test_decorator
+    def forward(self, x, y):
+        return x + y
+
+example_args = (torch.randn(3, 2), torch.randn(3, 2))
+model = Decorator()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dictionary.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dictionary.py
new file mode 100644
index 0000000000000000000000000000000000000000..49e688bc0ac1f09567e3b877aaca29a1d02b4121
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dictionary.py
@@ -0,0 +1,17 @@
+# mypy: allow-untyped-defs
+import torch
+
+class Dictionary(torch.nn.Module):
+    """
+    Dictionary structures are inlined and flattened along tracing.
+    """
+
+    def forward(self, x, y):
+        elements = {}
+        elements["x2"] = x * x
+        y = y * elements["x2"]
+        return {"y": y}
+
+example_args = (torch.randn(3, 2), torch.tensor(4))
+tags = {"python.data-structure"}
+model = Dictionary()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_assert.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_assert.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc822e5553e1ab8bd350a26966c22f1a9a1698cf
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_assert.py
@@ -0,0 +1,18 @@
+# mypy: allow-untyped-defs
+import torch
+
+class DynamicShapeAssert(torch.nn.Module):
+    """
+    A basic usage of python assertion.
+    """
+
+    def forward(self, x):
+        # assertion with error message
+        assert x.shape[0] > 2, f"{x.shape[0]} is greater than 2"
+        # assertion without error message
+        assert x.shape[0] > 1
+        return x
+
+example_args = (torch.randn(3, 2),)
+tags = {"python.assert"}
+model = DynamicShapeAssert()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py
new file mode 100644
index 0000000000000000000000000000000000000000..21824ef3a0f66eb25f4d8e8c1ba92f53fdd4c275
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_if_guard.py
@@ -0,0 +1,19 @@
+# mypy: allow-untyped-defs
+import torch
+
+class DynamicShapeIfGuard(torch.nn.Module):
+    """
+    `if` statement with backed dynamic shape predicate will be specialized into
+    one particular branch and generate a guard. However, export will fail if the
+    the dimension is marked as dynamic shape from higher level API.
+    """
+
+    def forward(self, x):
+        if x.shape[0] == 3:
+            return x.cos()
+
+        return x.sin()
+
+example_args = (torch.randn(3, 2, 2),)
+tags = {"torch.dynamic-shape", "python.control-flow"}
+model = DynamicShapeIfGuard()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_map.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8066aed556b9ee588b9744d17ba16c35d8fed6c
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_map.py
@@ -0,0 +1,19 @@
+# mypy: allow-untyped-defs
+import torch
+
+from functorch.experimental.control_flow import map
+
+class DynamicShapeMap(torch.nn.Module):
+    """
+    functorch map() maps a function over the first tensor dimension.
+    """
+
+    def forward(self, xs, y):
+        def body(x, y):
+            return x + y
+
+        return map(body, xs, y)
+
+example_args = (torch.randn(3, 2), torch.randn(2))
+tags = {"torch.dynamic-shape", "torch.map"}
+model = DynamicShapeMap()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_round.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..decbf036553cb76544a19e531e5aee98d792ae0b
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_round.py
@@ -0,0 +1,21 @@
+# mypy: allow-untyped-defs
+import torch
+
+from torch._export.db.case import SupportLevel
+from torch.export import Dim
+
+class DynamicShapeRound(torch.nn.Module):
+    """
+    Calling round on dynamic shapes is not supported.
+    """
+
+    def forward(self, x):
+        return x[: round(x.shape[0] / 2)]
+
+x = torch.randn(3, 2)
+dim0_x = Dim("dim0_x")
+example_args = (x,)
+tags = {"torch.dynamic-shape", "python.builtin"}
+support_level = SupportLevel.NOT_SUPPORTED_YET
+dynamic_shapes = {"x": {0: dim0_x}}
+model = DynamicShapeRound()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..360fe15f6f98d9d735366bfa53371d79e0b00209
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_slicing.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+import torch
+
+class DynamicShapeSlicing(torch.nn.Module):
+    """
+    Slices with dynamic shape arguments should be captured into the graph
+    rather than being baked in.
+    """
+
+    def forward(self, x):
+        return x[: x.shape[0] - 2, x.shape[1] - 1 :: 2]
+
+example_args = (torch.randn(3, 2),)
+tags = {"torch.dynamic-shape"}
+model = DynamicShapeSlicing()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_view.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_view.py
new file mode 100644
index 0000000000000000000000000000000000000000..c45d4aeebb0282a0f56c58a587b4bfe1655f50e3
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/dynamic_shape_view.py
@@ -0,0 +1,17 @@
+# mypy: allow-untyped-defs
+import torch
+
+class DynamicShapeView(torch.nn.Module):
+    """
+    Dynamic shapes should be propagated to view arguments instead of being
+    baked into the exported graph.
+    """
+
+    def forward(self, x):
+        new_x_shape = x.size()[:-1] + (2, 5)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1)
+
+example_args = (torch.randn(10, 10),)
+tags = {"torch.dynamic-shape"}
+model = DynamicShapeView()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/fn_with_kwargs.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/fn_with_kwargs.py
new file mode 100644
index 0000000000000000000000000000000000000000..46b2637b398c21bf9399d0a3fa2a964354beea3e
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/fn_with_kwargs.py
@@ -0,0 +1,30 @@
+# mypy: allow-untyped-defs
+import torch
+
+class FnWithKwargs(torch.nn.Module):
+    """
+    Keyword arguments are not supported at the moment.
+    """
+
+    def forward(self, pos0, tuple0, *myargs, mykw0, **mykwargs):
+        out = pos0
+        for arg in tuple0:
+            out = out * arg
+        for arg in myargs:
+            out = out * arg
+        out = out * mykw0
+        out = out * mykwargs["input0"] * mykwargs["input1"]
+        return out
+
+example_args = (
+    torch.randn(4),
+    (torch.randn(4), torch.randn(4)),
+    *[torch.randn(4), torch.randn(4)]
+)
+example_kwargs = {
+    "mykw0": torch.randn(4),
+    "input0": torch.randn(4),
+    "input1": torch.randn(4),
+}
+tags = {"python.data-structure"}
+model = FnWithKwargs()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/list_contains.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/list_contains.py
new file mode 100644
index 0000000000000000000000000000000000000000..35a140f4ee2e5d6f42c3509984333db896f1c081
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/list_contains.py
@@ -0,0 +1,17 @@
+# mypy: allow-untyped-defs
+import torch
+
+class ListContains(torch.nn.Module):
+    """
+    List containment relation can be checked on a dynamic shape or constants.
+    """
+
+    def forward(self, x):
+        assert x.size(-1) in [6, 2]
+        assert x.size(0) not in [4, 5, 6]
+        assert "monkey" not in ["cow", "pig"]
+        return x + x
+
+example_args = (torch.randn(3, 2),)
+tags = {"torch.dynamic-shape", "python.data-structure", "python.assert"}
+model = ListContains()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/nested_function.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/nested_function.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4076ac14dada40b4d78812666a9ec6b5e67045b
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/nested_function.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+import torch
+
+class NestedFunction(torch.nn.Module):
+    """
+    Nested functions are traced through. Side effects on global captures
+    are not supported though.
+    """
+
+    def forward(self, a, b):
+        x = a + b
+        z = a - b
+
+        def closure(y):
+            nonlocal x
+            x += 1
+            return x * y + z
+
+        return closure(x)
+
+example_args = (torch.randn(3, 2), torch.randn(2))
+tags = {"python.closure"}
+model = NestedFunction()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/null_context_manager.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/null_context_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..80d09f68097edbe676077be183711dabe5cbc664
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/null_context_manager.py
@@ -0,0 +1,21 @@
+# mypy: allow-untyped-defs
+import contextlib
+
+import torch
+
+class NullContextManager(torch.nn.Module):
+    """
+    Null context manager in Python will be traced out.
+    """
+
+    def forward(self, x):
+        """
+        Null context manager in Python will be traced out.
+        """
+        ctx = contextlib.nullcontext()
+        with ctx:
+            return x.sin() + x.cos()
+
+example_args = (torch.randn(3, 2),)
+tags = {"python.context-manager"}
+model = NullContextManager()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/optional_input.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/optional_input.py
new file mode 100644
index 0000000000000000000000000000000000000000..9693aa476f0eb31ec912c840ed3a4426ac832de8
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/optional_input.py
@@ -0,0 +1,20 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._export.db.case import SupportLevel
+
+
+class OptionalInput(torch.nn.Module):
+    """
+    Tracing through optional input is not supported yet
+    """
+
+    def forward(self, x, y=torch.randn(2, 3)):
+        if y is not None:
+            return x + y
+        return x
+
+
+example_args = (torch.randn(2, 3),)
+tags = {"python.object-model"}
+support_level = SupportLevel.NOT_SUPPORTED_YET
+model = OptionalInput()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/pytree_flatten.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/pytree_flatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..804e73c5a6d58ad5b5be179bf67a5d5bc38c2e2b
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/pytree_flatten.py
@@ -0,0 +1,16 @@
+# mypy: allow-untyped-defs
+import torch
+
+from torch.utils import _pytree as pytree
+
+class PytreeFlatten(torch.nn.Module):
+    """
+    Pytree from PyTorch can be captured by TorchDynamo.
+    """
+
+    def forward(self, x):
+        y, _spec = pytree.tree_flatten(x)
+        return y[0] + 1
+
+example_args = ({1: torch.randn(3, 2), 2: torch.randn(3, 2)},),
+model = PytreeFlatten()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/scalar_output.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/scalar_output.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d3b4645330c47c3625736b695d635f4ab58c70
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/scalar_output.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+import torch
+
+from torch.export import Dim
+
+x = torch.randn(3, 2)
+dim1_x = Dim("dim1_x")
+
+class ScalarOutput(torch.nn.Module):
+    """
+    Returning scalar values from the graph is supported, in addition to Tensor
+    outputs. Symbolic shapes are captured and rank is specialized.
+    """
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, x):
+        return x.shape[1] + 1
+
+example_args = (x,)
+tags = {"torch.dynamic-shape"}
+dynamic_shapes = {"x": {1: dim1_x}}
+model = ScalarOutput()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/specialized_attribute.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/specialized_attribute.py
new file mode 100644
index 0000000000000000000000000000000000000000..f17092f9afc681b91a982a8a2479ac1dde4f455d
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/specialized_attribute.py
@@ -0,0 +1,26 @@
+# mypy: allow-untyped-defs
+from enum import Enum
+
+import torch
+
+class Animal(Enum):
+    COW = "moo"
+
+class SpecializedAttribute(torch.nn.Module):
+    """
+    Model attributes are specialized.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.a = "moo"
+        self.b = 4
+
+    def forward(self, x):
+        if self.a == Animal.COW.value:
+            return x * x + self.b
+        else:
+            raise ValueError("bad")
+
+example_args = (torch.randn(3, 2),)
+model = SpecializedAttribute()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_for_loop.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_for_loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa62b86d16d9b6a1c539976a891f58bd732ae30d
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_for_loop.py
@@ -0,0 +1,16 @@
+# mypy: allow-untyped-defs
+import torch
+
+class StaticForLoop(torch.nn.Module):
+    """
+    A for loop with constant number of iterations should be unrolled in the exported graph.
+    """
+
+    def forward(self, x):
+        # constant
+        ret = [i + x for i in range(10)]
+        return ret
+
+example_args = (torch.randn(3, 2),)
+tags = {"python.control-flow"}
+model = StaticForLoop()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_if.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_if.py
new file mode 100644
index 0000000000000000000000000000000000000000..f169380159a45489142ce5ae3523b2e4504c6721
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/static_if.py
@@ -0,0 +1,18 @@
+# mypy: allow-untyped-defs
+import torch
+
+class StaticIf(torch.nn.Module):
+    """
+    `if` statement with static predicate value should be traced through with the
+    taken branch.
+    """
+
+    def forward(self, x):
+        if len(x.shape) == 3:
+            return x + torch.ones(1, 1, 1)
+
+        return x
+
+example_args = (torch.randn(3, 2, 2),)
+tags = {"python.control-flow"}
+model = StaticIf()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/tensor_setattr.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/tensor_setattr.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbc263e7ff2240a3cf8618c56f152e744aa40e8
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/tensor_setattr.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class TensorSetattr(torch.nn.Module):
+    """
+    setattr() call onto tensors is not supported.
+    """
+    def forward(self, x, attr):
+        setattr(x, attr, torch.randn(3, 2))
+        return x + 4
+
+example_args = (torch.randn(3, 2), "attr")
+tags = {"python.builtin"}
+model = TensorSetattr()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/type_reflection_method.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/type_reflection_method.py
new file mode 100644
index 0000000000000000000000000000000000000000..99ad42a153c512d65aaae1bcac2377ee1e124f25
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/type_reflection_method.py
@@ -0,0 +1,22 @@
+# mypy: allow-untyped-defs
+import torch
+
+class A:
+    @classmethod
+    def func(cls, x):
+        return 1 + x
+
+class TypeReflectionMethod(torch.nn.Module):
+    """
+    type() calls on custom objects followed by attribute accesses are not allowed
+    due to its overly dynamic nature.
+    """
+
+    def forward(self, x):
+        a = A()
+        return type(a).func(x)
+
+
+example_args = (torch.randn(3, 4),)
+tags = {"python.builtin"}
+model = TypeReflectionMethod()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/unsupported_operator.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/unsupported_operator.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5a52d80b895b3b2c2d85b878ca4efea511e73ea
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/unsupported_operator.py
@@ -0,0 +1,18 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._export.db.case import SupportLevel
+
+
+class TorchSymMin(torch.nn.Module):
+    """
+    torch.sym_min operator is not supported in export.
+    """
+
+    def forward(self, x):
+        return x.sum() + torch.sym_min(x.size(0), 100)
+
+
+example_args = (torch.randn(3, 2),)
+tags = {"torch.operator"}
+support_level = SupportLevel.NOT_SUPPORTED_YET
+model = TorchSymMin()
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/user_input_mutation.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/user_input_mutation.py
new file mode 100644
index 0000000000000000000000000000000000000000..3156b3a1bf2ec6f6361395de3dacb098ecf20c3a
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/_export/db/examples/user_input_mutation.py
@@ -0,0 +1,17 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+class UserInputMutation(torch.nn.Module):
+    """
+    Directly mutate user input in forward
+    """
+
+    def forward(self, x):
+        x.mul_(2)
+        return x.cos()
+
+
+example_args = (torch.randn(3, 2),)
+tags = {"torch.mutation"}
+model = UserInputMutation()
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diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/__init__.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c01cbd457374c27e40b07daca5ae1644a701767d
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/__init__.py
@@ -0,0 +1,15 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+
+from torch.ao.quantization.fx.convert import convert
+from torch.ao.quantization.fx.fuse import fuse
+
+# omitting files that's unlikely to be used right now, for example
+# the newly added lower_to_fbgemm etc.
+from torch.ao.quantization.fx.prepare import prepare
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..7acea4f84a2a0a82f134b6790e573f8f1cb677f2
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/_equalize.py
@@ -0,0 +1,38 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx._equalize import (
+    _convert_equalization_ref,
+    _InputEqualizationObserver,
+    _WeightEqualizationObserver,
+    calculate_equalization_scale,
+    clear_weight_quant_obs_node,
+    convert_eq_obs,
+    CUSTOM_MODULE_SUPP_LIST,
+    custom_module_supports_equalization,
+    default_equalization_qconfig,
+    EqualizationQConfig,
+    fused_module_supports_equalization,
+    get_equalization_qconfig_dict,
+    get_layer_sqnr_dict,
+    get_op_node_and_weight_eq_obs,
+    input_equalization_observer,
+    is_equalization_observer,
+    maybe_get_next_equalization_scale,
+    maybe_get_next_input_eq_obs,
+    maybe_get_weight_eq_obs_node,
+    nn_module_supports_equalization,
+    node_supports_equalization,
+    remove_node,
+    reshape_scale,
+    scale_input_observer,
+    scale_weight_functional,
+    scale_weight_node,
+    update_obs_for_equalization,
+    weight_equalization_observer,
+)
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/convert.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d6ac350602bb7a97c773a3a09fec0780483379f
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/convert.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.convert import convert
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py
new file mode 100644
index 0000000000000000000000000000000000000000..e29337b3f861e5b54dc9f37d39d12ad975ad1315
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/fusion_patterns.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.fuse_handler import DefaultFuseHandler, FuseHandler
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..a71e980a57ba141bdc5bbe9b283d69582eb8fd82
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/graph_module.py
@@ -0,0 +1,17 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.graph_module import (
+    _is_observed_module,
+    _is_observed_standalone_module,
+    FusedGraphModule,
+    GraphModule,
+    ObservedGraphModule,
+    ObservedStandaloneGraphModule,
+    QuantizedGraphModule,
+)
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b49f7c645d8d1bc3a154d62a1295a90b155f986
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/match_utils.py
@@ -0,0 +1,14 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.match_utils import (
+    _find_matches,
+    _is_match,
+    _MatchResult,
+    MatchAllNode,
+)
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/prepare.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca65dcc04dd0021f0065892ca86e209a1c218473
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/prepare.py
@@ -0,0 +1,9 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.prepare import prepare
diff --git a/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/utils.py b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef35559884b7c430f1d5c72b21f72979108469a5
--- /dev/null
+++ b/data/tefig/envs/olmes-eval/lib/python3.10/site-packages/torch/quantization/fx/utils.py
@@ -0,0 +1,20 @@
+# flake8: noqa: F401
+r"""
+This file is in the process of migration to `torch/ao/quantization`, and
+is kept here for compatibility while the migration process is ongoing.
+If you are adding a new entry/functionality, please, add it to the
+appropriate files under `torch/ao/quantization/fx/`, while adding an import statement
+here.
+"""
+from torch.ao.quantization.fx.utils import (
+    all_node_args_have_no_tensors,
+    assert_and_get_unique_device,
+    create_getattr_from_value,
+    get_custom_module_class_keys,
+    get_linear_prepack_op_for_dtype,
+    get_new_attr_name_with_prefix,
+    get_non_observable_arg_indexes_and_types,
+    get_qconv_prepack_op,
+    graph_module_from_producer_nodes,
+    maybe_get_next_module,
+)