code: def test_multiple_tensors_compare_with_expected(self) -> None:
      net = BasicModel_MultiLayer(multi_input_module=True)
      inp = torch.tensor([[0.0, 100.0, 0.0]])
      self._assert_compare_with_expected(
          net,
          net.multi_relu,
          inp,
          (
              [[90.0, 100.0, 100.0, 100.0]],
              [[90.0, 100.0, 100.0, 100.0]],
          ),
      )
  
imports: from typing import Any, cast, List, Tuple, Union
  import torch
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.layer.layer_activation import LayerActivation
  from captum.attr._core.layer.layer_conductance import LayerConductance
  from captum.attr._core.layer.layer_integrated_gradients import LayerIntegratedGradients
  from captum.attr._models.base import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicEmbeddingModel, BasicModel_MultiLayer, BasicModel_MultiLayer_TrueMultiInput
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 6 for calling the Test obj's underlying member method code with example usage: obj.test_multiple_tensors_compare_with_expected() without return types