code: def test_basic_multi_input(self) -> None:
      net = BasicModel_MultiLayer_MultiInput()
  
      inputs = (
          torch.tensor([[10.0, 20.0, 10.0]]),
          torch.tensor([[1.0, 2.0, 1.0]]),
      )
      add_args = (torch.tensor([[1.0, 2.0, 3.0]]), 1.0)
      baselines = (torch.randn(30, 3), torch.randn(30, 3))
      expected = torch.tensor([[171.6841, 0.0]])
      self._assert_attributions(
          net,
          net.model.linear2,
          inputs,
          baselines,
          0,
          expected,
          add_args=add_args,
      )
  
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 7 for calling the Test obj's underlying member method code with example usage: obj.test_basic_multi_input() without return types