code: def _assert_softmax_classification(
      self,
      type: str = "vanilla",
      approximation_method: str = "gausslegendre",
  ) -> None:
      num_in = 40
      input = torch.arange(
          0.0, num_in * 1.0, requires_grad=True
      ).unsqueeze(0)
      target = torch.tensor(5)
      # 10-class classification model
      model = SoftmaxModel(num_in, 20, 10)
      self._validate_completness(
          model, input, target, type, approximation_method
      )
  
imports: import unittest
  import torch
  from captum._utils.typing import BaselineType, Tensor
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.classification_models import SigmoidModel, SoftmaxModel
  from torch.nn import Module
label_desc: Use image node_id 14 for calling the Test obj's underlying member method code with example usage: obj._assert_softmax_classification(type, approximation_method) without return types