code: def _saliency_classification_assert(
      self, nt_type: str = "vanilla"
  ) -> None:
      num_in = 5
      input = torch.tensor(
          [[0.0, 1.0, 2.0, 3.0, 4.0]], requires_grad=True
      )
      target = torch.tensor(5)
      # 10-class classification model
      model = SoftmaxModel(num_in, 20, 10)
      saliency = Saliency(model)
  
      if nt_type == "vanilla":
          attributions = saliency.attribute(input, target)
  
          output = model(input)[:, target]
          output.backward()
          expected = torch.abs(cast(Tensor, input.grad))
          assertTensorAlmostEqual(self, attributions, expected)
      else:
          nt = NoiseTunnel(saliency)
          attributions = nt.attribute(
              input,
              nt_type=nt_type,
              nt_samples=10,
              stdevs=0.0002,
              target=target,
          )
      self.assertEqual(input.shape, attributions.shape)
  
imports: from typing import Any, cast, Tuple, Union
  import torch
  from captum._utils.gradient import compute_gradients
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._core.saliency import Saliency
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel5_MultiArgs
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 17 for calling the Test obj's underlying member method code with example usage: obj._saliency_classification_assert(nt_type) without return types