code: def _input_x_gradient_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)
      input_x_grad = InputXGradient(model.forward)
      if nt_type == "vanilla":
          attributions = input_x_grad.attribute(input, target)
          output = model(input)[:, target]
          output.backward()
          expected = input.grad * input
          assertTensorAlmostEqual(
              self, attributions, expected, 0.00001, "max"
          )
      else:
          nt = NoiseTunnel(input_x_grad)
          attributions = nt.attribute(
              input,
              nt_type=nt_type,
              nt_samples=10,
              stdevs=1.0,
              target=target,
          )
  
      self.assertEqual(attributions.shape, input.shape)
  
imports: from typing import Any, cast
  import torch
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.input_x_gradient import InputXGradient
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from tests.attr.test_saliency import _get_basic_config, _get_multiargs_basic_config
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.classification_models import SoftmaxModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 12 for calling the Test obj's underlying member method code with example usage: obj._input_x_gradient_classification_assert(nt_type) without return types