code: def value_and_grad(fun, argnums=0, has_aux=False, holomorphic=False):
      r"""Creates a function which evaluates both ``fun`` and the grad of ``fun``.
  
      NOTE: You only need this in a very specific case that you want to take a
      gradient **inside** a :func:`transform`\ ed function and the function you are
      differentiating uses :func:`set_state`. For example:
  
      >>> class MyModule(hk.Module):
      ...   def __call__(self, x):
      ...     hk.set_state("last", jnp.sum(x))
      ...     return x ** 2
  
      >>> def f(x):
      ...   m = MyModule()
      ...   y, g = hk.value_and_grad(m)(x)
      ...   return y, g
  
      >>> f = hk.transform_with_state(f)
      >>> x = jnp.array(2.)
      >>> _ = jax.jit(f.init)(None, x)
  
      Args:
        fun: Function to be differentiated. Its arguments at positions specified by
          ``argnums`` should be arrays, scalars, or standard Python containers. It
          should return a scalar (which includes arrays with shape ``()`` but not
          arrays with shape ``(1,)`` etc.)
        argnums: Optional, integer or tuple of integers. Specifies which positional
          argument(s) to differentiate with respect to (default 0).
        has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the
         first element is considered the output of the mathematical function to be
         differentiated and the second element is auxiliary data. Default False.
        holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be
          holomorphic. Default False.
  
      Returns:
        A function with the same arguments as ``fun`` that evaluates both ``fun``
        and the gradient of ``fun`` and returns them as a pair (a two-element
        tuple). If ``argnums`` is an integer then the gradient has the same shape
        and type as the positional argument indicated by that integer. If argnums is
        a tuple of integers, the gradient is a tuple of values with the same shapes
        and types as the corresponding arguments.
      """
      if not base.inside_transform():
          raise ValueError(
              "hk.grad() should not be used outside of hk.transform(). "
              "Use jax.grad() instead."
          )
  
      @functools.wraps(fun)
      def stateful_fun(*args, **kwargs):
          state_in = kwargs.pop("hk_state")
          with temporary_internal_state(
              state_in
          ), base.push_jax_trace_level():
              out = fun(*args, **kwargs)
              out, aux = out if has_aux else (out, None)
              state_out = difference(state_in, internal_state())
              return out, (aux, state_out)
  
      grad_fun = jax.value_and_grad(
          stateful_fun,
          argnums=argnums,
          has_aux=True,
          holomorphic=holomorphic,
      )
  
      @functools.wraps(grad_fun)
      def wrapper(*args, **kwargs):
          kwargs["hk_state"] = internal_state()
          (value, (aux, hk_state)), grads = grad_fun(*args, **kwargs)
          update_internal_state(hk_state)
          if has_aux:
              return (value, aux), grads
          else:
              return value, grads
  
      return wrapper
  
imports: import collections
  import collections.abc
  from collections.abc import Mapping, MutableMapping
  import functools
  import inspect
  from typing import Any, Callable, Optional, TypeVar
  from haiku._src import base
  import jax
  import jax.numpy
label_desc: Use image node_id 8 for calling a global function with example usage: value_and_grad(fun, argnums, has_aux, holomorphic) and returns: wrapper, out,  aux,  state_out, value,  aux,  grads, value,  grads