code: def evaluate(
      *,
      jit_eval_step,
      state: TrainState,
      eval_ds: tf.data.Dataset,
      num_eval_steps: int,
  ):
      """Evaluate the target 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
          metrics = jit_eval_step(
              state.params, eval_batch, state.graphdef
          )
          eval_metrics.append(metrics)
      eval_metrics = common_utils.stack_forest(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 dataclasses
  import os
  import input_pipeline
  import jax
  import jax.numpy
  import models
  import numpy
  import optax
  import temperature_sampler
  import tensorflow
  import utils
  from absl import logging
  from clu import metric_writers, periodic_actions
  from configs import default
  from jax import random
  from jax.sharding import Mesh, NamedSharding
  from jax.sharding import PartitionSpec
  from utils import HasCache, TrainState
  from flax import linen
  from flax.experimental import nnx
  from flax.training import checkpoints, common_utils
label_desc: Use image node_id 12 for calling a global function with example usage: evaluate() and returns: eval_summary