code: def _assert_multi_argument(
      self, type: str, approximation_method: str = "gausslegendre"
  ) -> None:
      model = BasicModel4_MultiArgs()
      self._compute_attribution_and_evaluate(
          model,
          (
              torch.tensor([[1.5, 2.0, 34.3]], requires_grad=True),
              torch.tensor([[3.0, 3.5, 23.2]], requires_grad=True),
          ),
          baselines=(0.0, torch.zeros((1, 3))),
          additional_forward_args=torch.arange(1.0, 4.0).reshape(1, 3),
          type=type,
          approximation_method=approximation_method,
      )
      # uses batching with an integer variable and nd-tensors as
      # additional forward arguments
      self._compute_attribution_and_evaluate(
          model,
          (
              torch.tensor(
                  [[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]],
                  requires_grad=True,
              ),
              torch.tensor(
                  [[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]],
                  requires_grad=True,
              ),
          ),
          baselines=(torch.zeros((2, 3)), 0.0),
          additional_forward_args=(
              torch.arange(1.0, 7.0).reshape(2, 3),
              1,
          ),
          type=type,
          approximation_method=approximation_method,
      )
      # uses batching with an integer variable and python list
      # as additional forward arguments
      model = BasicModel5_MultiArgs()
      self._compute_attribution_and_evaluate(
          model,
          (
              torch.tensor(
                  [[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]],
                  requires_grad=True,
              ),
              torch.tensor(
                  [[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]],
                  requires_grad=True,
              ),
          ),
          baselines=(0.0, 0.00001),
          additional_forward_args=([2, 3], 1),
          type=type,
          approximation_method=approximation_method,
      )
      # similar to previous case plus baseline consists of a tensor and
      # a single example
      self._compute_attribution_and_evaluate(
          model,
          (
              torch.tensor(
                  [[1.5, 2.0, 34.3], [3.4, 1.2, 2.0]],
                  requires_grad=True,
              ),
              torch.tensor(
                  [[3.0, 3.5, 23.2], [2.3, 1.2, 0.3]],
                  requires_grad=True,
              ),
          ),
          baselines=(torch.zeros((1, 3)), 0.00001),
          additional_forward_args=([2, 3], 1),
          type=type,
          approximation_method=approximation_method,
      )
  
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 35 for calling the Test obj's underlying member method code with example usage: obj._assert_multi_argument(type, approximation_method) without return types