code: def test_batched_input_smoothgrad_wo_mutliplying_by_inputs(
      self,
  ) -> None:
      model = BasicModel_MultiLayer()
      inputs = torch.tensor(
          [[1.5, 2.0, 1.3], [0.5, 0.1, 2.3], [1.5, 2.0, 1.3]],
          requires_grad=True,
      )
      ig_wo_mutliplying_by_inputs = IntegratedGradients(
          model, multiply_by_inputs=False
      )
      nt_wo_mutliplying_by_inputs = NoiseTunnel(
          ig_wo_mutliplying_by_inputs
      )
  
      ig = IntegratedGradients(model)
      nt = NoiseTunnel(ig)
      n_samples = 5
      target = 0
      type = "smoothgrad"
      attributions_wo_mutliplying_by_inputs = (
          nt_wo_mutliplying_by_inputs.attribute(
              inputs,
              nt_type=type,
              nt_samples=n_samples,
              stdevs=0.0,
              target=target,
              n_steps=500,
          )
      )
      attributions = nt.attribute(
          inputs,
          nt_type=type,
          nt_samples=n_samples,
          stdevs=0.0,
          target=target,
          n_steps=500,
      )
      assertTensorAlmostEqual(
          self,
          attributions_wo_mutliplying_by_inputs * inputs,
          attributions,
      )
  
imports: import unittest
  from typing import Any, cast, Tuple, Union
  import torch
  from captum._utils.common import _zeros
  from captum._utils.typing import BaselineType, Tensor, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.common import _tensorize_baseline
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel2, BasicModel3, BasicModel4_MultiArgs, BasicModel5_MultiArgs, BasicModel6_MultiTensor, BasicModel_MultiLayer
  from torch.nn import Module
label_desc: Use image node_id 25 for calling the Test obj's underlying member method code with example usage: obj.test_batched_input_smoothgrad_wo_mutliplying_by_inputs() without return types