code: def test_sensitivity_max_multi_dim(self) -> None:
      model = BasicModel_MultiLayer()
  
      input = torch.arange(1.0, 13.0).view(4, 3)
  
      additional_forward_args = (None, True)
      targets: List = [(0, 1, 1), (0, 1, 1), (1, 1, 1), (0, 1, 1)]
  
      ig = IntegratedGradients(model)
      self.sensitivity_max_assert(
          ig.attribute,
          input,
          torch.tensor([0.006, 0.01, 0.001, 0.008]),
          n_perturb_samples=1,
          max_examples_per_batch=4,
          perturb_func=_perturb_func,
          target=targets,
          additional_forward_args=additional_forward_args,
      )
  
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 6 for calling the Test obj's underlying member method code with example usage: obj.test_sensitivity_max_multi_dim() without return types