code: def test_multi_layer_multi_gradient_activation(self) -> None:
      net = BasicModel_MultiLayer(multi_input_module=True)
      inp = torch.tensor([[3.0, 4.0, 0.0]])
      module_list: List[Module] = [net.multi_relu, net.linear0]
      self._layer_activation_test_assert(
          net,
          module_list,
          inp,
          [
              ([[0.0, 8.0, 8.0, 8.0]], [[0.0, 8.0, 8.0, 8.0]]),
              [[9.0, 12.0, 0.0]],
          ],
      )
  
imports: import unittest
  from typing import Any, List, Tuple, Union
  import torch
  from captum._utils.typing import ModuleOrModuleList
  from captum.attr._core.layer.layer_activation import LayerActivation
  from captum.attr._core.layer.layer_gradient_x_activation import LayerGradientXActivation
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicEmbeddingModel, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 8 for calling the Test obj's underlying member method code with example usage: obj.test_multi_layer_multi_gradient_activation() without return types