code: def clone_inputs_retaining_gradness(example_inputs):
      """
      This clone inputs is different from utils clone_input. In case of minifier,
      all the tensors are leaf tensors while creating a new graph. So, we set the
      requires_grad field w/o checking the leafness of the tensor.
      """
      cloned_inputs = clone_inputs(example_inputs)
      for idx in range(len(example_inputs)):
          if isinstance(cloned_inputs[idx], torch.Tensor):
              cloned_inputs[idx].requires_grad_(
                  example_inputs[idx].requires_grad
              )
      return cloned_inputs
  
imports: import copy
  import functools
  import getpass
  import itertools
  import logging
  import os
  import subprocess
  import tempfile
  import textwrap
  from collections import Counter
  from importlib import import_module
  from typing import Callable, Optional, TypeVar
  import torch
  import torch._prims_common
  import torch._subclasses.meta_utils
  from torch._dynamo.testing import rand_strided
  from torch._prims_common import is_float_dtype
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._content_store import ContentStoreReader, ContentStoreWriter
  from .None import config
  from .utils import clone_inputs, get_debug_dir
label_desc: Use image node_id 6 for calling a global function with example usage: clone_inputs_retaining_gradness(example_inputs) and returns: cloned_inputs