code: def evaluate(
      *,
      p_eval_step,
      params,
      eval_ds: tf.data.Dataset,
      num_eval_steps: int,
  ):
      """Evaluate the params an return a dictionary with the metrics."""
      logging.info("Gathering evaluation metrics.")
      eval_metrics = []
      eval_iter = iter(eval_ds)  # pytype: disable=wrong-arg-types
      for _, eval_batch in zip(range(num_eval_steps), eval_iter):
          eval_batch = jax.tree_util.tree_map(
              lambda x: x._numpy(), eval_batch
          )  # pylint: disable=protected-access
          eval_batch = common_utils.shard(eval_batch)
          metrics = p_eval_step(params, eval_batch)
          eval_metrics.append(metrics)
      eval_metrics = common_utils.get_metrics(eval_metrics)
      eval_metrics_sums = jax.tree_util.tree_map(jnp.sum, eval_metrics)
      eval_denominator = eval_metrics_sums.pop("denominator")
      eval_summary = jax.tree_util.tree_map(
          lambda x: x
          / eval_denominator,  # pylint: disable=cell-var-from-loop
          eval_metrics_sums,
      )
      return eval_summary
  
imports: import collections
  import functools
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  from flax.training import dynamic_scale
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import bleu
  import decode
  import input_pipeline
  import models
label_desc: Use image node_id 13 for calling a global function with example usage: evaluate() and returns: eval_summary