code: def _local_perturb_func(
      inputs: TensorOrTupleOfTensorsGeneric,
  ) -> Tuple[
      Union[Tensor, Tuple[Tensor, ...]],
      Union[Tensor, Tuple[Tensor, ...]],
  ]:
      input2 = None
      if isinstance(inputs, tuple):
          input1 = inputs[0]
          input2 = inputs[1]
      else:
          input1 = cast(Tensor, inputs)
  
      perturb1 = 0.0009 * torch.ones_like(input1)
      if input2 is None:
          return perturb1, input1 - perturb1
  
      perturb2 = 0.0121 * torch.ones_like(input2)
      return (perturb1, perturb2), (
          input1 - perturb1,
          input2 - perturb2,
      )
  
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 Attribution, DeepLift, FeatureAblation, IntegratedGradients, Saliency
  from captum.metrics import infidelity, infidelity_perturb_func_decorator
  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
  from torch.nn import Module
label_desc: Use image node_id 4 for calling a global function with example usage: _local_perturb_func(inputs) and returns: perturb1,  perturb2,  unknown,  unknown, perturb1,  unknown