code: def test_basic_sensitivity_max_multiple(self) -> None:
      model = BasicModel2()
      sa = Saliency(model)
  
      input1 = torch.tensor([3.0] * 20)
      input2 = torch.tensor([1.0] * 20)
      self.sensitivity_max_assert(
          sa.attribute,
          (input1, input2),
          torch.zeros(20),
          max_examples_per_batch=21,
      )
      self.sensitivity_max_assert(
          sa.attribute,
          (input1, input2),
          torch.zeros(20),
          max_examples_per_batch=60,
      )
  
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 2 for calling the Test obj's underlying member method code with example usage: obj.test_basic_sensitivity_max_multiple() without return types