code: def _maybe_wrap_functional_tensor(
      maybe_tensor, level, *, _python_functionalize: bool = False
  ):
      if not isinstance(maybe_tensor, torch.Tensor):
          return maybe_tensor
      wrapped = _wrap_functional_tensor(maybe_tensor, level)
      _assert_wrapped_functional(maybe_tensor, wrapped)
      if _python_functionalize:
          out = FunctionalTensor(wrapped)
          torch._mirror_autograd_meta_to(maybe_tensor, out)
          return out
      return wrapped
  
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 36 for calling a global function with example usage: _maybe_wrap_functional_tensor(maybe_tensor, level) and returns: wrapped, maybe_tensor, out