code: def undo_gradient_requirements(
      inputs: Tuple[Tensor, ...], grad_required: List[bool]
  ) -> None:
      """
      Iterates through list of tensors, zeros each gradient, and sets required
      grad to false if the corresponding index in grad_required is False.
      This method is used to undo the effects of prepare_gradient_inputs, making
      grads not required for any input tensor that did not initially require
      gradients.
      """
  
      assert isinstance(
          inputs, tuple
      ), "Inputs should be wrapped in a tuple prior to preparing for gradients."
      assert len(inputs) == len(
          grad_required
      ), "Input tuple length should match gradient mask."
      for index, input in enumerate(inputs):
          assert isinstance(
              input, torch.Tensor
          ), "Given input is not a torch.Tensor"
          if not grad_required[index]:
              input.requires_grad_(False)
  
imports: import threading
  import typing
  import warnings
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from captum._utils.common import _reduce_list, _run_forward, _sort_key_list, _verify_select_neuron
  from captum._utils.sample_gradient import SampleGradientWrapper
  from captum._utils.typing import Literal, ModuleOrModuleList, TargetType, TensorOrTupleOfTensorsGeneric
  from torch import device, Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling a global function with example usage: undo_gradient_requirements(inputs, grad_required) without return types