code: def _grad_cam_test_assert(
      self,
      model: Module,
      target_layer: Module,
      test_input: Union[Tensor, Tuple[Tensor, ...]],
      expected_activation: Union[
          TensorLikeList,
          Tuple[TensorLikeList, ...],
          Tensor,
          Tuple[Tensor, ...],
      ],
      additional_input: Any = None,
      attribute_to_layer_input: bool = False,
      relu_attributions: bool = False,
      attr_dim_summation: bool = True,
  ):
      layer_gc = LayerGradCam(model, target_layer)
      self.assertFalse(layer_gc.multiplies_by_inputs)
      attributions = layer_gc.attribute(
          test_input,
          target=0,
          additional_forward_args=additional_input,
          attribute_to_layer_input=attribute_to_layer_input,
          relu_attributions=relu_attributions,
          attr_dim_summation=attr_dim_summation,
      )
      assertTensorTuplesAlmostEqual(
          self, attributions, expected_activation, delta=0.01
      )
  
imports: import unittest
  from typing import Any, Tuple, Union
  import torch
  from captum._utils.typing import TensorLikeList
  from captum.attr._core.layer.grad_cam import LayerGradCam
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 10 for calling the Test obj's underlying member method code with example usage: obj._grad_cam_test_assert(model, target_layer, test_input, expected_activation, additional_input, attribute_to_layer_input, relu_attributions, attr_dim_summation) without return types