code: def test_basic_relu_multi_input(self) -> None:
      model = BasicModel2()
  
      input1 = torch.tensor([[3.0]])
      input2 = torch.tensor([[1.0]], requires_grad=True)
  
      baseline1 = torch.tensor([[0.0]])
      baseline2 = torch.tensor([[0.0]])
      inputs = (input1, input2)
      baselines = (baseline1, baseline2)
  
      gs = GradientShap(model)
      n_samples = 20000
      attributions, delta = cast(
          Tuple[Tuple[Tensor, ...], Tensor],
          gs.attribute(
              inputs,
              baselines=baselines,
              n_samples=n_samples,
              return_convergence_delta=True,
          ),
      )
      _assert_attribution_delta(
          self,
          inputs,
          attributions,
          n_samples,
          delta,
          delta_thresh=0.008,
      )
  
      ig = IntegratedGradients(model)
      attributions_ig = ig.attribute(inputs, baselines=baselines)
      self._assert_shap_ig_comparision(attributions, attributions_ig)
  
file_path: captum/tests/attr/test_gradient_shap.py
imports: from typing import cast, Tuple, Union
  import numpy
  import torch
  from captum._utils.typing import Tensor
  from captum.attr._core.gradient_shap import GradientShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from numpy import ndarray
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicLinearModel, BasicModel2
  from tests.helpers.classification_models import SoftmaxModel
label_desc: Use image node_id 5 for calling the Test obj's underlying member method code with example usage: obj.test_basic_relu_multi_input() without return types