code: def _value_and_grad_impl(
      f,
      grad_fn,
      *args,
      output_gradients,
      auto_unpack_single_arg,
      expand_tf_modules_as_trainable_vars=False,
      has_aux=False,
      **kwargs,
  ):
      """Helper which generalizes gradient / Jacobian."""
      if not args and not kwargs:
          raise ValueError(
              "Gradient is not defined unless at least one of `arg` or "
              "`kwarg` is specified."
          )
      # The following is for backwards compatibility. In the one arg case with no
      # kwargs we can't tell which protocol to use if not for
      # `auto_unpack_single_arg`.  When `True` and when the sole arg is a tuple
      # or list then we unpack it as if it was the args, i.e., preserve the old
      # behavior.
      do_unpack = (
          auto_unpack_single_arg
          and len(args) == 1
          and not (kwargs)
          and isinstance(args[0], (tuple, list))
      )
      if do_unpack:
          args = args[0]
      args, kwargs = _prepare_args(args, kwargs)
      if expand_tf_modules_as_trainable_vars:
          expand_args, expand_kwargs = tf.nest.map_structure(
              lambda x: x.trainable_variables
              if tensor_util.is_module(x)
              else x,
              [args, kwargs],
          )
      else:
          expand_args, expand_kwargs = args, kwargs
  
      if not has_aux:
          real_f = f
          f = lambda *args, **kwargs: (
              real_f(*args, **kwargs)  # pylint: disable=g-long-lambda
              if _has_args(real_f)
              else real_f(),
              (),
          )
  
      y, dydx, aux = grad_fn(
          lambda: f(*args, **kwargs) if _has_args(f) else f(),
          tf.nest.flatten([expand_args, expand_kwargs]),
          output_gradients,
      )
      dydx_args, dydx_kwargs = tf.nest.pack_sequence_as(
          [expand_args, expand_kwargs], dydx
      )
      if len(args) == 1 and not do_unpack:
          dydx_args = dydx_args[0]
  
      if has_aux:
          res = ((y, aux),)
      else:
          res = (y,)
  
      if args:
          res += (dydx_args,)
      if kwargs:
          res += (dydx_kwargs,)
  
      return res
  
file_path: probability/tensorflow_probability/python/math/gradient.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 8 for calling a global function with example usage: _value_and_grad_impl(f, grad_fn) and returns: res