code: def _multiple_layer_activation_test_assert(
      self,
      model: Module,
      target_layers: List[Module],
      test_input: Union[Tensor, Tuple[Tensor, ...]],
      expected_activation: Union[
          List, Tuple[List[float], ...], Tuple[List[List[float]], ...]
      ],
      additional_input: Any = None,
      attribute_to_layer_input: bool = False,
  ):
      layer_act = LayerActivation(model, target_layers)
      self.assertTrue(layer_act.multiplies_by_inputs)
      attributions = layer_act.attribute(
          test_input,
          additional_forward_args=additional_input,
          attribute_to_layer_input=attribute_to_layer_input,
      )
      for i in range(len(target_layers)):
          assertTensorTuplesAlmostEqual(
              self, attributions[i], expected_activation[i], delta=0.01
          )
  
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  import torch.nn
  from captum.attr._core.layer.layer_activation import LayerActivation
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, Conv1dSeqModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 16 for calling the Test obj's underlying member method code with example usage: obj._multiple_layer_activation_test_assert(model, target_layers, test_input, expected_activation, additional_input, attribute_to_layer_input) without return types