code: def test_unflatten_param_list_dict(self):
      class Mod(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.param_list = torch.nn.ParameterList()
              self.param_dict = torch.nn.ParameterDict()
              for i in range(2):
                  self.param_list.append(
                      torch.nn.Parameter(torch.randn((2, 3)))
                  )
                  self.param_dict[f"key_{i}"] = torch.nn.Parameter(
                      torch.randn((2, 3))
                  )
  
          def forward(self, x):
              for i in range(2):
                  x = x + self.param_list[i]
                  x = x + self.param_dict[f"key_{i}"]
              return x
  
      export_module = torch.export.export(Mod(), (torch.randn((2, 3)),))
      unflattened = unflatten(export_module)
  
      self.compare_outputs(
          export_module, unflattened, (torch.randn((2, 3)),)
      )
  
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 7 for calling the TestUnflatten obj's underlying member method code with example usage: obj.test_unflatten_param_list_dict() and returns: x