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@@ -4458,16 +4458,4 @@ tool_server/.venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so filter=
 tool_server/.venv/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so filter=lfs diff=lfs merge=lfs -text
 tool_server/.venv/lib/python3.12/site-packages/torch/lib/libtorch_cuda_linalg.so filter=lfs diff=lfs merge=lfs -text
 tool_server/.venv/lib/python3.12/site-packages/torch/lib/libtorch_python.so filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/__pycache__/__init__.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/__pycache__/_meta_registrations.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/__pycache__/_tensor_docs.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/__pycache__/_torch_docs.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/__pycache__/overrides.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_decomp/__pycache__/decompositions.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/__pycache__/symbolic_convert.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/__pycache__/utils.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/__pycache__/guards.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/__pycache__/output_graph.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/__pycache__/trace_rules.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/variables/__pycache__/builder.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
-tool_server/.venv/lib/python3.12/site-packages/torch/_dynamo/variables/__pycache__/higher_order_ops.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
+tool_server/.venv/lib/python3.12/site-packages/torch/_export/serde/__pycache__/serialize.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/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()

tool_server/.venv/lib/python3.12/site-packages/torch/_export/db/examples/list_unpack.py

@@ -0,0 +1,21 @@
+# mypy: allow-untyped-defs
+
+import torch
+
+class ListUnpack(torch.nn.Module):
+    """
+    Lists are treated as static construct, therefore unpacking should be
+    erased after tracing.
+    """
+
+    def forward(self, args: list[torch.Tensor]):
+        """
+        Lists are treated as static construct, therefore unpacking should be
+        erased after tracing.
+        """
+        x, *y = args
+        return x + y[0]
+
+example_args = ([torch.randn(3, 2), torch.tensor(4), torch.tensor(5)],)
+tags = {"python.control-flow", "python.data-structure"}
+model = ListUnpack()