code: def unwrap_batched(args, level):
      flat_args, spec = tree_flatten(args)
      if len(flat_args) == 0:
          return args, ()
      result = [
          torch._C._functorch._unwrap_batched(arg, level)
          if isinstance(arg, torch.Tensor)
          else (arg, None)
          for arg in flat_args
      ]
      output, bdims = zip(*result)
      return tree_unflatten(output, spec), tree_unflatten(bdims, spec)
  
imports: import torch
  import functools
  import threading
  from torch import Tensor
  from typing import Any, Callable, Optional, Tuple, Union, List
  from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map_, _broadcast_to_and_flatten, TreeSpec
  from functools import partial
  import os
  import itertools
  from torch._C._functorch import _add_batch_dim, _remove_batch_dim, _vmap_decrement_nesting, _vmap_increment_nesting, is_batchedtensor
label_desc: Use image node_id 23 for calling a global function with example usage: unwrap_batched(args, level) and returns: tree_unflatten,  tree_unflatten, args,  