code: def _assert_multi_variable(
      self,
      type: str,
      approximation_method: str = "gausslegendre",
      multiply_by_inputs: bool = True,
  ) -> None:
      model = BasicModel2()
  
      input1 = torch.tensor([3.0])
      input2 = torch.tensor([1.0], requires_grad=True)
  
      baseline1 = torch.tensor([0.0])
      baseline2 = torch.tensor([0.0])
  
      attributions1 = self._compute_attribution_and_evaluate(
          model,
          (input1, input2),
          (baseline1, baseline2),
          type=type,
          approximation_method=approximation_method,
          multiply_by_inputs=multiply_by_inputs,
      )
      if type == "vanilla":
          assertTensorAlmostEqual(
              self,
              attributions1[0],
              [1.5] if multiply_by_inputs else [0.5],
              delta=0.05,
              mode="max",
          )
          assertTensorAlmostEqual(
              self,
              attributions1[1],
              [-0.5] if multiply_by_inputs else [-0.5],
              delta=0.05,
              mode="max",
          )
      model = BasicModel3()
      attributions2 = self._compute_attribution_and_evaluate(
          model,
          (input1, input2),
          (baseline1, baseline2),
          type=type,
          approximation_method=approximation_method,
          multiply_by_inputs=multiply_by_inputs,
      )
      if type == "vanilla":
          assertTensorAlmostEqual(
              self,
              attributions2[0],
              [1.5] if multiply_by_inputs else [0.5],
              delta=0.05,
              mode="max",
          )
          assertTensorAlmostEqual(
              self,
              attributions2[1],
              [-0.5] if multiply_by_inputs else [-0.5],
              delta=0.05,
              mode="max",
          )
          # Verifies implementation invariance
          self.assertEqual(
              sum(attribution for attribution in attributions1),
              sum(attribution for attribution in attributions2),
          )
  
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 33 for calling the Test obj's underlying member method code with example usage: obj._assert_multi_variable(type, approximation_method, multiply_by_inputs) without return types