code: def eval_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: MetricFn,
      return_logits_and_labels: bool = False,
      debug: Optional[bool] = False,
  ) -> Union[
      Tuple[Dict[str, Tuple[float, int]], jnp.ndarray, jnp.ndarray],
      Dict[str, Tuple[float, int]],
  ]:
      """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.
        return_logits_and_labels: Whether to return logits and labels or not.
        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 optionally logits].
      """
      variables = {
          "params": train_state.optimizer.target,
          **train_state.model_state,
      }
      logits = flax_model.apply(
          variables,
          batch["inputs"],
          train=False,
          mutable=False,
          debug=debug,
      )
  
      metrics = metrics_fn(logits, batch)
      if return_logits_and_labels:
          logits = jax.lax.all_gather(logits, "batch")
          labels = jax.lax.all_gather(batch["label"], "batch")
          return metrics, logits, labels
      return metrics
  
imports: import copy
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple, 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
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.mbt import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.projects.vivit 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 3 for calling a global function with example usage: eval_step(train_state, batch) and returns: metrics, metrics,  logits,  labels