code: def process_multiple_batches(
      process_one_batch: Callable[
          [_TrainingState, _TrainingData],
          Tuple[_TrainingState, _TrainingAux],
      ],
      num_batches: int,
      postprocess_aux: Optional[
          Callable[[_TrainingAux], _TrainingAux]
      ] = None,
  ) -> Callable[
      [_TrainingState, _TrainingData],
      Tuple[_TrainingState, _TrainingAux],
  ]:
      """Makes 'process_one_batch' process multiple batches at once.
  
      Args:
        process_one_batch: a function that takes 'state' and 'data', and returns
          'new_state' and 'aux' (for example 'metrics').
        num_batches: how many batches to process at once
        postprocess_aux: how to merge the extra information, defaults to taking the
          mean.
  
      Returns:
        A function with the same interface as 'process_one_batch' which processes
        multiple batches at once.
      """
      assert num_batches >= 1
      if num_batches == 1:
          if not postprocess_aux:
              return process_one_batch
  
          def _process_one_batch(state, data):
              state, aux = process_one_batch(state, data)
              return state, postprocess_aux(aux)
  
          return _process_one_batch
  
      if postprocess_aux is None:
          postprocess_aux = lambda x: jax.tree_map(jnp.mean, x)
  
      def _process_multiple_batches(state, data):
          data = jax.tree_map(
              lambda a: jnp.reshape(a, (num_batches, -1, *a.shape[1:])),
              data,
          )
  
          state, aux = jax.lax.scan(
              process_one_batch, state, data, length=num_batches
          )
          return state, postprocess_aux(aux)
  
      return _process_multiple_batches
  
imports: import functools
  import itertools
  import queue
  import threading
  from typing import Callable, Iterable, Iterator, NamedTuple, Optional, Sequence, Tuple, TypeVar
  from absl import logging
  from acme import core
  from acme import types
  from acme.jax import types
  import haiku
  import jax
  import jax.numpy
  import numpy
  import reverb
  import tree
label_desc: Use image node_id 23 for calling a global function with example usage: process_multiple_batches(process_one_batch, num_batches, postprocess_aux) and returns: _process_multiple_batches, _process_one_batch, state,  postprocess_aux, process_one_batch, state,  postprocess_aux