code: def _autograd_grad(
      outputs,
      inputs,
      grad_outputs=None,
      retain_graph=False,
      create_graph=True,
  ):
      if grad_outputs is None:
          diff_outputs = tuple(
              out for out in outputs if out.requires_grad
          )
      else:
          result = tuple(
              (out, go)
              for out, go in zip(outputs, grad_outputs)
              if out.requires_grad
          )
          if len(result) == 0:
              diff_outputs, grad_outputs = (), ()
          else:
              diff_outputs, grad_outputs = zip(*result)
      if len(diff_outputs) == 0:
          return tuple(torch.zeros_like(inp) for inp in inputs)
      grad_inputs = torch.autograd.grad(
          diff_outputs,
          inputs,
          grad_outputs,
          retain_graph=retain_graph,
          create_graph=create_graph,
          allow_unused=True,
      )
      grad_inputs = tuple(
          torch.zeros_like(inp) if gi is None else gi
          for gi, inp in zip(grad_inputs, inputs)
      )
      return grad_inputs
  
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 10 for calling a global function with example usage: _autograd_grad(outputs, inputs, grad_outputs, retain_graph, create_graph) and returns: grad_inputs, tuple