code: def test_sensitivity_max_multi_dim_batching(self) -> None:
      model = BasicModel_MultiLayer()
  
      input = torch.arange(1.0, 16.0).view(5, 3)
  
      additional_forward_args = (torch.ones(5, 3).float(), False)
      targets: List = [0, 0, 0, 0, 0]
  
      sa = Saliency(model)
  
      sensitivity1 = self.sensitivity_max_assert(
          sa.attribute,
          input,
          torch.zeros(5),
          n_perturb_samples=1,
          max_examples_per_batch=None,
          perturb_func=_perturb_func,
          target=targets,
          additional_forward_args=additional_forward_args,
      )
      sensitivity2 = self.sensitivity_max_assert(
          sa.attribute,
          input,
          torch.zeros(5),
          n_perturb_samples=10,
          max_examples_per_batch=10,
          perturb_func=_perturb_func,
          target=targets,
          additional_forward_args=additional_forward_args,
      )
      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 7 for calling the Test obj's underlying member method code with example usage: obj.test_sensitivity_max_multi_dim_batching() without return types