code: def _maybe_unwrap_functional_tensor(
      maybe_tensor, *, reapply_views: bool
  ):
      if not isinstance(maybe_tensor, torch.Tensor):
          return maybe_tensor
      if isinstance(maybe_tensor, FunctionalTensor):
          maybe_tensor = maybe_tensor.elem
  
      if not torch._is_functional_tensor(maybe_tensor):
          # If it's not a functional tensor, just return it.
          # This can happen if we functionalize a fn that returns a global,
          # which was never wrapped properly.
          return maybe_tensor
      # Sync any pending updates on the output tensor
      torch._sync(maybe_tensor)
      return _unwrap_functional_tensor(maybe_tensor, reapply_views)
  
imports: from typing import Callable, Union, Tuple, List, Any, Optional
  import torch
  from functools import partial, wraps
  import contextlib
  from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map, tree_map_only, tree_map_, treespec_pprint
  from torch.utils import _pytree
  from torch.fx.experimental import const_fold
  from torch.fx.experimental.proxy_tensor import make_fx
  import torch.autograd.forward_ad
  from torch._subclasses.functional_tensor import FunctionalTensor
  from .vmap import doesnt_support_saved_tensors_hooks, get_chunk_sizes
  from .apis import vmap
  from torch._C._functorch import _wrap_for_grad, _unwrap_for_grad, _grad_increment_nesting, _grad_decrement_nesting, _jvp_increment_nesting, _jvp_decrement_nesting, _wrap_functional_tensor, _unwrap_functional_tensor, _func_decrement_nesting, _func_increment_nesting, _assert_wrapped_functional, _propagate_functional_input_mutation, set_inplace_requires_grad_allowed, get_inplace_requires_grad_allowed
  from torch._functorch.utils import exposed_in, argnums_t
label_desc: Use image node_id 38 for calling a global function with example usage: _maybe_unwrap_functional_tensor(maybe_tensor) and returns: _unwrap_functional_tensor, maybe_tensor, maybe_tensor