code: def eval_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: MetricFn,
      debug: Optional[bool] = False,
      learn_token_score: bool = False,
      add_boxes: bool = False,
  ):
      """Runs a single step of training.
  
      Note that in this code, the buffer of the second argument (batch) is donated
      to the computation.
  
      Assumed API of metrics_fn is:
      ```metrics = metrics_fn(logits, batch)
      where batch is yielded by the batch iterator, and metrics is a dictionary
      mapping metric name to a vector of per example measurements. eval_step will
      aggregate (by summing) all per example measurements and divide by the
      aggregated normalizers. For each given metric we compute:
      1/N sum_{b in batch_iter} metric(b), where  N is the sum of normalizer
      over all batches.
  
      Args:
        train_state: TrainState, the state of training including the current
          global_step, model_state, rng, and optimizer. The buffer of this argument
          can be donated to the computation.
        batch: A single batch of data. a metrics function, that given logits and
          batch of data, calculates the metrics as well as the loss.
        flax_model: A Flax model.
        metrics_fn: A metrics function, that given logits and batch of data,
          calculates the metrics as well as the loss.
        debug: Whether the debug mode is enabled during evaluation. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
        learn_token_score: if enable learning token score. The network will have
          additional outputs of each scores for each token.
        add_boxes: if add boxes in shape batch x time x num_objs x 4
  
      Returns:
        Calculated metrics and logits.
      """
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
      outputs = flax_model.apply(
          variables,
          batch["inputs"],
          boxes=batch["bboxes"] if add_boxes else None,
          rngs={"dropout": train_state.rng},
          train=False,
          mutable=False,
          debug=debug,
      )
      if learn_token_score:
          logits = outputs["logits"]
      else:
          logits = outputs
      metrics = metrics_fn(logits, batch)
      return metrics, logits
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.objectvivit import optimizer_utils
  from scenic.projects.objectvivit import train_utils
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: eval_step(train_state, batch) and returns: metrics,  logits