code: def test_classification(self) -> None:
      def custom_baseline_fn(inputs):
          num_in = inputs.shape[1]
          return torch.arange(0.0, num_in * 4.0).reshape(4, num_in)
  
      num_in = 40
      n_samples = 10
  
      # 10-class classification model
      model = SoftmaxModel(num_in, 20, 10)
      model.eval()
  
      inputs = torch.arange(0.0, num_in * 2.0).reshape(2, num_in)
      baselines = custom_baseline_fn
      expected = torch.zeros(2, 20)
  
      self._assert_attributions(
          model,
          model.relu1,
          inputs,
          baselines,
          1,
          expected,
          n_samples=n_samples,
      )
  
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 6 for calling the Test obj's underlying member method code with example usage: obj.test_classification() and returns: torch