code: def value_and_grad(*args, **kwargs):
      """Computes the gradient of the specified function together with the value."""
      if "value_and_grad" in backend():
          return backend()["value_and_grad"](*args, **kwargs)
      grad_fn = grad(*args, **kwargs)
      fn = args[0]
      has_aux = False
      if has_aux in kwargs:
          has_aux = kwargs["has_aux"]
      if not has_aux:
  
          def val_and_grad(*fn_args, **fn_kwargs):
              return fn(*fn_args, **fn_kwargs), grad_fn(
                  *fn_args, **fn_kwargs
              )
  
          return val_and_grad
  
      def val_and_grad_aux(*fn_args, **fn_kwargs):
          g, aux = grad_fn(*fn_args, **fn_kwargs)
          res, _ = fn(*fn_args, **fn_kwargs)
          return (res, aux), g
  
      return val_and_grad_aux
  
imports: import contextlib
  import enum
  import gin
  from trax.fastmath.jax import JAX_BACKEND
  from trax.fastmath.numpy import NUMPY_BACKEND
  from trax.fastmath.tf import TF_BACKEND
label_desc: Use image node_id 30 for calling a global function with example usage: value_and_grad() and returns: val_and_grad_aux, backend, val_and_grad, res,  aux,  g, fn,  grad_fn