code: def test_basic_multi_tensor_output(self) -> None:
      model = BasicModel_MultiLayer(multi_input_module=True)
      model.eval()
  
      inputs = torch.tensor([[0.0, 100.0, 0.0]])
      expected = (
          [[90.0, 100.0, 100.0, 100.0]],
          [[90.0, 100.0, 100.0, 100.0]],
      )
      self._assert_attributions(
          model,
          model.multi_relu,
          inputs,
          torch.zeros_like(inputs),
          0,
          expected,
          n_samples=5,
      )
  
imports: from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.typing import TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.gradient_shap import GradientShap
  from captum.attr._core.layer.layer_gradient_shap import LayerGradientShap
  from tests.attr.test_gradient_shap import _assert_attribution_delta
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 3 for calling the Test obj's underlying member method code with example usage: obj.test_basic_multi_tensor_output() without return types