code: def test_unflatten_preserve_signature(self):
      class NestedChild(torch.nn.Module):
          def forward(self, zx, y):
              return {"x": y["key"] + zx[1], "w": y["key"] * zx[1]}
  
      class Child1(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.nested = NestedChild()
  
          def forward(self, x, y):
              z = torch.ones_like(x)
              xw = self.nested((z, x), y={"key": y})
              return xw["w"] + z - xw["x"]
  
      class Child2(torch.nn.Module):
          def __init__(self):
              super().__init__()
  
          def forward(self, x):
              return x - 1
  
      class MyModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.foo = Child1()
              self.bar = Child2()
  
          def forward(self, x, y):
              x = self.foo(x, y)
              x = self.bar(x)
              return x
  
      orig_eager = MyModule()
      inps = torch.rand(2, 3), torch.rand(2, 3)
      export_module = export(
          orig_eager,
          inps,
          {},
          preserve_module_call_signature=("foo.nested",),
      )
      unflattened = unflatten(export_module)
      self.compare_outputs(export_module, unflattened, inps)
      unflattened.foo.nested = NestedChild()
      self.compare_outputs(export_module, unflattened, inps)
  
      # Test tree spec mismatched input
      orig_outs = export_module(*inps)
      new_inps = *inps, torch.rand(2, 3)
      with self.assertRaisesRegex(
          TypeError,
          "There is no flat args adapter sepcified. Are you sure you are calling this with the right arguments?",
      ):
          unflattened(new_inps)
  
      # With flat args adapter
      class KeepTwoFlatArgsAdapter(FlatArgsAdapter):
          def adapt(
              self,
              target_spec: TreeSpec,
              input_spec: TreeSpec,
              input_args: List[Any],
          ) -> List[Any]:
              while len(input_args) > 2:
                  input_args.pop(-1)
              return input_args
  
      unflattened = unflatten(export_module, KeepTwoFlatArgsAdapter())
      new_outs = unflattened(*new_inps)
      self.assertTrue(torch.allclose(orig_outs, new_outs))
  
imports: import dataclasses
  import unittest
  from contextlib import contextmanager
  from dataclasses import dataclass
  from typing import List, Any
  import torch
  import torch._dynamo
  from functorch.experimental.control_flow import cond, map
  from torch import Tensor
  from torch.export import Constraint, Dim, dynamic_dim, export, unflatten, FlatArgsAdapter
  from torch.export._trace import DEFAULT_EXPORT_DYNAMO_CONFIG
  from torch._export import capture_pre_autograd_graph
  from torch._export.utils import get_buffer, get_param, is_buffer, is_param, register_dataclass_as_pytree_node
  from torch.export import Constraint, Dim, export
  from torch.fx.experimental.proxy_tensor import make_fx
  from torch.testing import FileCheck
  from torch.testing._internal.common_utils import run_tests, TestCase
  from torch.utils._pytree import LeafSpec, tree_flatten, tree_unflatten, TreeSpec, treespec_dumps, treespec_loads
label_desc: Use image node_id 6 for calling the TestUnflatten obj's underlying member method code with example usage: obj.test_unflatten_preserve_signature() and returns: dict, unknown, unknown, x, input_args