code: def eval_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[
      Dict[str, Tuple[float, int]],
      jnp.ndarray,
      jnp.ndarray,
      jnp.ndarray,
  ]:
      """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.
        config: Configurations of the experiment.
        debug: Whether the debug mode is enabled during evaluation. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Calculated metrics and logits.
      """
      variables = {
          "params": train_state.optimizer.target,  # pytype: disable=attribute-error
          **train_state.model_state,
      }
  
      # Compute the targets for feature regression.
      compute_feature_targets(batch, config)
  
      # We need an rng for masking at test time.
      # Note that we are using the same rng for the whole validation set (ie each
      # batch will have the same token mask).
      _, rng = jax.random.split(train_state.rng)
      dropout_rng = train_utils.bind_rng_to_host_device(
          rng, axis_name="batch", bind_to="device"
      )
  
      logits, token_mask = flax_model.apply(
          variables,
          batch["inputs"],
          train=True,
          mutable=False,
          debug=debug,
          rngs={"dropout": dropout_rng},
      )
      metrics = metrics_fn(logits, token_mask, batch)
  
      return metrics, logits, token_mask, batch["targets"]
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Mapping, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  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
  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.av_mae import base_model
  from scenic.projects.av_mae import train_utils
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 5 for calling a global function with example usage: eval_step(train_state, batch) and returns: metrics,  logits,  token_mask,  batch