code: def process_many_batches(
      process_one_batch: Callable[
          [_TrainingState, _TrainingData],
          jax_types.TrainingStepOutput[_TrainingState],
      ],
      num_batches: int,
      postprocess_aux: Optional[
          Callable[
              [jax_types.TrainingMetrics], jax_types.TrainingMetrics
          ]
      ] = None,
  ) -> Callable[
      [_TrainingState, _TrainingData],
      jax_types.TrainingStepOutput[_TrainingState],
  ]:
      """The version of 'process_multiple_batches' with stronger typing."""
  
      def _process_one_batch(
          state: _TrainingState, data: _TrainingData
      ) -> Tuple[_TrainingState, jax_types.TrainingMetrics]:
          result = process_one_batch(state, data)
          return result.state, result.metrics
  
      func = process_multiple_batches(
          _process_one_batch, num_batches, postprocess_aux
      )
  
      def _process_many_batches(
          state: _TrainingState, data: _TrainingData
      ) -> jax_types.TrainingStepOutput[_TrainingState]:
          state, aux = func(state, data)
          return jax_types.TrainingStepOutput(state, aux)
  
      return _process_many_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 24 for calling a global function with example usage: process_many_batches(process_one_batch, num_batches, postprocess_aux) and returns: _process_many_batches, result,  result, jax_types