code: def test_flatten_unflattener(
      self, param_shapes: List[Tuple[int, ...]]
  ):
      # unflatten and flatten should be inverses of each other. check this holds.
      _unflatten_params = _unflatten_params_factory(param_shapes)
      params = tuple(torch.randn(shape) for shape in param_shapes)
      assertTensorTuplesAlmostEqual(
          self,
          params,
          _unflatten_params(_flatten_params(params)),
          delta=1e-4,
          mode="max",
      )
  
file_path: captum/tests/influence/_core/test_naive_influence.py
imports: import tempfile
  from typing import Callable, List, Tuple, Union
  import torch
  import torch.nn
  from captum._utils.common import _parse_version
  from captum.influence._core.influence_function import NaiveInfluenceFunction
  from captum.influence._utils.common import _custom_functional_call, _flatten_params, _functional_call, _unflatten_params_factory
  from parameterized import parameterized
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.influence._utils.common import _format_batch_into_tuple, build_test_name_func, DataInfluenceConstructor, ExplicitDataset, get_random_model_and_data, Linear, UnpackDataset, USE_GPU_LIST
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling the TestNaiveInfluence obj's underlying member method code with example usage: obj.test_flatten_unflattener(param_shapes) without return types