code: def test_sensitivity_additional_forward_args_multi_args(self) -> None:
      model = BasicModel4_MultiArgs()
  
      input1 = torch.tensor([[1.5, 2.0, 3.3]])
      input2 = torch.tensor([[3.0, 3.5, 2.2]])
  
      args = torch.tensor([[1.0, 3.0, 4.0]])
      ig = DeepLift(model)
  
      sensitivity1 = self.sensitivity_max_assert(
          ig.attribute,
          (input1, input2),
          torch.zeros(1),
          additional_forward_args=args,
          n_perturb_samples=1,
          max_examples_per_batch=1,
          perturb_func=_perturb_func,
      )
  
      sensitivity2 = self.sensitivity_max_assert(
          ig.attribute,
          (input1, input2),
          torch.zeros(1),
          additional_forward_args=args,
          n_perturb_samples=4,
          max_examples_per_batch=2,
          perturb_func=_perturb_func,
      )
      assertTensorAlmostEqual(self, sensitivity1, sensitivity2, 0.0)
  
imports: import typing
  from typing import Any, Callable, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr import DeepLift, GradientShap, GuidedBackprop, IntegratedGradients, Saliency
  from captum.metrics import sensitivity_max
  from captum.metrics._core.sensitivity import default_perturb_func
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel2, BasicModel4_MultiArgs, BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer
  from torch import Tensor
label_desc: Use image node_id 8 for calling the Test obj's underlying member method code with example usage: obj.test_sensitivity_additional_forward_args_multi_args() without return types