code: def grad_and_value(
      func: Callable, argnums: argnums_t = 0, has_aux: bool = False
  ) -> Callable:
      """
      Returns a function to compute a tuple of the gradient and primal, or
      forward, computation.
  
      Args:
          func (Callable): A Python function that takes one or more arguments.
              Must return a single-element Tensor. If specified ``has_aux``
              equals ``True``, function can return a tuple of single-element
              Tensor and other auxiliary objects: ``(output, aux)``.
          argnums (int or Tuple[int]): Specifies arguments to compute gradients
              with respect to. ``argnums`` can be single integer or tuple of
              integers. Default: 0.
          has_aux (bool): Flag indicating that ``func`` returns a tensor and
              other auxiliary objects: ``(output, aux)``. Default: False.
  
      Returns:
          Function to compute a tuple of gradients with respect to its inputs
          and the forward computation. By default, the output of the function is
          a tuple of the gradient tensor(s) with respect to the first argument
          and the primal computation. If specified ``has_aux`` equals
          ``True``, tuple of gradients and tuple of the forward computation with
          output auxiliary objects is returned. If ``argnums`` is a tuple of
          integers, a tuple of a tuple of the output gradients with respect to
          each ``argnums`` value and the forward computation is returned.
  
      See :func:`grad` for examples
      """
  
      @doesnt_support_saved_tensors_hooks
      @wraps(func)
      def wrapper(*args, **kwargs):
          level = _grad_increment_nesting()
          try:
              output, aux, grad_input = None, None, None
              # See NOTE [grad and vjp interaction with no_grad]
              with torch.enable_grad():
                  args = _wrap_all_tensors(args, level)
                  kwargs = _wrap_all_tensors(kwargs, level)
                  diff_args = _slice_argnums(
                      args, argnums, as_tuple=False
                  )
                  tree_map_(
                      partial(_create_differentiable, level=level),
                      diff_args,
                  )
  
                  output = func(*args, **kwargs)
                  if has_aux:
                      if not (
                          isinstance(output, tuple) and len(output) == 2
                      ):
                          raise RuntimeError(
                              "grad_and_value(f)(*args): output of function f should be a tuple: (output, aux) "
                              "if has_aux is True"
                          )
                      output, aux = output
  
                  if not isinstance(output, torch.Tensor):
                      raise RuntimeError(
                          "grad_and_value(f)(*args): Expected f(*args) "
                          f"to return a Tensor, got {type(output)}"
                      )
                  if output.dim() != 0:
                      raise RuntimeError(
                          "grad_and_value(f)(*args): Expected f(*args) "
                          "to return a scalar Tensor, got tensor with "
                          f"{output.dim()} dims. Maybe you wanted to "
                          "use the vjp or jacrev APIs instead?"
                      )
  
                  flat_diff_args, spec = tree_flatten(diff_args)
  
                  # NB: need create_graph so that backward pass isn't run in no_grad mode
                  flat_outputs = _as_tuple(output)
                  flat_grad_input = _autograd_grad(
                      flat_outputs, flat_diff_args, create_graph=True
                  )
                  grad_input = tree_unflatten(flat_grad_input, spec)
  
                  grad_input = _undo_create_differentiable(
                      grad_input, level
                  )
                  output = _undo_create_differentiable(output, level)
                  if aux is not None:
                      aux = _undo_create_differentiable(aux, level)
  
              if has_aux:
                  return grad_input, (output, aux)
              return grad_input, output
          finally:
              _grad_decrement_nesting()
  
      return wrapper
  
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 34 for calling a global function with example usage: grad_and_value(func, argnums, has_aux) and returns: wrapper, grad_input,  output, grad_input,  output,  aux