code: def _autograd_grad(
      outputs,
      inputs,
      grad_outputs=None,
      retain_graph=False,
      create_graph=True,
  ):
      inputs, inputs_spec = tree_flatten(inputs)
      diff_inputs = tuple(inp for inp in inputs if inp.requires_grad)
      if grad_outputs is None:
          diff_outputs = tuple(
              out for out in outputs if out.requires_grad
          )
      else:
          diff_grad_outputs = [
              (out, go)
              for out, go in zip(outputs, grad_outputs)
              if out.requires_grad
          ]
          if len(diff_grad_outputs) == 0:
              diff_outputs, grad_outputs = (), ()
          else:
              diff_outputs, grad_outputs = zip(*diff_grad_outputs)
      grad_inputs = torch.autograd.grad(
          diff_outputs,
          diff_inputs,
          grad_outputs,
          retain_graph=retain_graph,
          create_graph=create_graph,
          allow_unused=True,
      )
      result = []
      grad_inputs_iter = iter(grad_inputs)
      for inp in inputs:
          if inp.requires_grad:
              grad_input = next(grad_inputs_iter)
              if grad_input is None:
                  result.append(torch.zeros_like(inp))
              else:
                  result.append(grad_input)
          else:
              result.append(torch.zeros_like(inp))
      return tree_unflatten(result, inputs_spec)
  
imports: from collections import defaultdict
  from torch import Tensor
  import torch.autograd
  from torch._decomp import core_aten_decompositions, decomposition_table
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from torch.utils import _pytree
  from torch.testing import make_tensor
  from torch.testing._internal.common_cuda import tf32_off
  from torch.testing._internal.common_utils import unMarkDynamoStrictTest
  from torch.testing._internal.common_utils import is_iterable_of_tensors, IS_WINDOWS, TestCase, skipIfCrossRef, suppress_warnings, TEST_WITH_ASAN, TEST_WITH_SLOW, run_tests, skipIfTorchDynamo
  from torch.testing._internal.common_modules import module_db, modules
  from torch.testing._internal.common_device_type import onlyNativeDeviceTypes, ops, instantiate_device_type_tests, onlyCPU, onlyCUDA
  from torch.testing._internal.common_methods_invocations import op_db, skip, skipOps, xfail
  from torch._dispatch.python import enable_python_dispatcher
  from torch._ops import DispatchKey
  import itertools
  import functools
  from functools import partial
  import unittest
label_desc: Use image node_id 3 for calling a global function with example usage: _autograd_grad(outputs, inputs, grad_outputs, retain_graph, create_graph) and returns: tree_unflatten