code: def grad(
      outputs: _TensorOrTensors,
      inputs: _TensorOrTensorsOrGradEdge,
      grad_outputs: Optional[_TensorOrTensors] = None,
      retain_graph: Optional[bool] = None,
      create_graph: bool = False,
      only_inputs: bool = True,
      allow_unused: Optional[bool] = None,
      is_grads_batched: bool = False,
      materialize_grads: bool = False,
  ) -> Tuple[torch.Tensor, ...]:
      r"""Computes and returns the sum of gradients of outputs with respect to
      the inputs.
  
      ``grad_outputs`` should be a sequence of length matching ``output``
      containing the "vector" in vector-Jacobian product, usually the pre-computed
      gradients w.r.t. each of the outputs. If an output doesn't require_grad,
      then the gradient can be ``None``).
  
      .. note::
  
          If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
          in a user-specified CUDA stream context, see
          :ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.
  
      .. note::
  
          ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
          To accumulate gradient for other parts of the graph, please use
          ``torch.autograd.backward``.
  
      Args:
          outputs (sequence of Tensor): outputs of the differentiated function.
          inputs (sequence of Tensor or GradientEdge): Inputs w.r.t. which the gradient will be
              returned (and not accumulated into ``.grad``).
          grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.
              Usually gradients w.r.t. each output. None values can be specified for scalar
              Tensors or ones that don't require grad. If a None value would be acceptable
              for all grad_tensors, then this argument is optional. Default: None.
          retain_graph (bool, optional): If ``False``, the graph used to compute the grad
              will be freed. Note that in nearly all cases setting this option to ``True``
              is not needed and often can be worked around in a much more efficient
              way. Defaults to the value of ``create_graph``.
          create_graph (bool, optional): If ``True``, graph of the derivative will
              be constructed, allowing to compute higher order derivative products.
              Default: ``False``.
          allow_unused (Optional[bool], optional): If ``False``, specifying inputs
              that were not used when computing outputs (and therefore their grad is
              always zero) is an error. Defaults to the value of ``materialize_grads``.
          is_grads_batched (bool, optional): If ``True``, the first dimension of each
              tensor in ``grad_outputs`` will be interpreted as the batch dimension.
              Instead of computing a single vector-Jacobian product, we compute a
              batch of vector-Jacobian products for each "vector" in the batch.
              We use the vmap prototype feature as the backend to vectorize calls
              to the autograd engine so that this computation can be performed in a
              single call. This should lead to performance improvements when compared
              to manually looping and performing backward multiple times. Note that
              due to this feature being experimental, there may be performance
              cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``
              to show any performance warnings and file an issue on github if warnings exist
              for your use case. Defaults to ``False``.
          materialize_grads (bool, optional): If ``True``, set the gradient for unused inputs
              to zero instead of None. This is useful when computing higher-order derivatives.
              If ``materialize_grads`` is ``True`` and ``allow_unused`` is ``False``, an error
              will be raised. Defaults to ``False``.
  
      """
      if materialize_grads and allow_unused is False:
          raise ValueError(
              "Expected allow_unused to be True or not passed when materialize_grads=True, "
              "but got: allow_unused=False."
          )
      if allow_unused is None:
          allow_unused = materialize_grads
      t_outputs = cast(
          Tuple[torch.Tensor, ...],
          (outputs,) if is_tensor_like(outputs) else tuple(outputs),
      )
      if is_tensor_like(inputs) or isinstance(
          inputs, graph.GradientEdge
      ):
          inputs = cast(_TensorOrTensorsOrGradEdge, (inputs,))
      else:
          inputs = tuple(inputs)
      t_inputs = tuple(i for i in inputs if is_tensor_like(i))
      overridable_args = t_outputs + t_inputs
      if has_torch_function(overridable_args):
          return handle_torch_function(
              grad,
              overridable_args,
              t_outputs,
              inputs,
              grad_outputs=grad_outputs,
              retain_graph=retain_graph,
              create_graph=create_graph,
              only_inputs=only_inputs,
              allow_unused=allow_unused,
              is_grads_batched=is_grads_batched,
              materialize_grads=materialize_grads,
          )
  
      if not only_inputs:
          warnings.warn(
              "only_inputs argument is deprecated and is ignored now "
              "(defaults to True). To accumulate gradient for other "
              "parts of the graph, please use torch.autograd.backward."
          )
  
      grad_outputs_ = _tensor_or_tensors_to_tuple(
          grad_outputs, len(t_outputs)
      )
      grad_outputs_ = _make_grads(
          t_outputs, grad_outputs_, is_grads_batched=is_grads_batched
      )
  
      if retain_graph is None:
          retain_graph = create_graph
  
      # The reason we repeat the same comment several times below is because
      # some Python versions print out the first line of multi-line function
      # calls in the traceback and some print out the last line
      if is_grads_batched:
  
          def vjp(gO):
              return _engine_run_backward(
                  t_outputs,
                  gO,
                  retain_graph,
                  create_graph,
                  inputs,
                  allow_unused,
                  accumulate_grad=False,
              )
  
          result = _vmap_internals._vmap(
              vjp, 0, 0, allow_none_pass_through=True
          )(grad_outputs_)
      else:
          result = _engine_run_backward(
              t_outputs,
              grad_outputs_,
              retain_graph,
              create_graph,
              inputs,
              allow_unused,
              accumulate_grad=False,
          )
      if materialize_grads:
          if any(
              result[i] is None and not is_tensor_like(inputs[i])
              for i in range(len(inputs))
          ):
              raise RuntimeError(
                  "materialize_grads cannot be used when the given input is a GradientEdge"
              )
          result = tuple(
              output
              if output is not None
              else torch.zeros_like(input, requires_grad=True)
              for (output, input) in zip(result, inputs)
          )
      return result
  
imports: import warnings
  from typing import Any, Callable, cast, List, Optional, Sequence, Tuple, Union
  import torch
  from torch.types import _size, _TensorOrTensors, _TensorOrTensorsOrGradEdge
  from ..None import _vmap_internals
  from ..overrides import handle_torch_function, has_torch_function, is_tensor_like
  from .None import forward_ad, functional, graph
  from .anomaly_mode import detect_anomaly, set_detect_anomaly
  from .function import Function, NestedIOFunction
  from .grad_mode import _force_original_view_tracking, _unsafe_preserve_version_counter, enable_grad, inference_mode, no_grad, set_grad_enabled, set_multithreading_enabled
  from .gradcheck import gradcheck, gradgradcheck
  from .graph import _engine_run_backward
  from .variable import Variable
  from torch._C._autograd import _add_metadata_json, _disable_profiler, _disable_profiler_legacy, _enable_profiler, _enable_profiler_legacy, _enable_record_function, _get_sequence_nr, _kineto_step, _KinetoEvent, _pop_saved_tensors_default_hooks, _prepare_profiler, _profiler_enabled, _ProfilerResult, _push_saved_tensors_default_hooks, _record_function_with_args_enter, _record_function_with_args_exit, _set_empty_test_observer, _supported_activities, DeviceType, kineto_available, ProfilerEvent, SavedTensor
  from torch._C._profiler import ProfilerActivity, ProfilerConfig, ProfilerState
  from .None import profiler
label_desc: Use image node_id 5 for calling a global function with example usage: grad(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched, materialize_grads) and returns: result, handle_torch_function, _engine_run_backward