code: def eval_step(
      task: str,
      dataset: str,
      modality: str,
      train_state: train_utils.TrainState,
      batch: Batch,
      flax_model: nn.Module,
      metrics_fn: MetricFn,
      debug: Optional[bool] = False,
  ) -> Tuple[Dict[str, Tuple[float, int]], 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:
        task: Task for which we are running the eval_step.
        dataset: The name of the dataset used for the task.
        modality: The modality for the train step.
        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.
  
      Returns:
        Calculated metrics and logits.
      """
  
      if modality == polyvit_base_model.Modality.AUDIO:
          batch["inputs"] = batch["inputs"]["spectrogram"]
  
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
      logits = flax_model.apply(
          variables,
          x=batch["inputs"],
          targets=batch["label"],
          task=task,
          dataset=dataset,
          modality=modality,
          train=False,
          mutable=False,
          debug=debug,
      )
      metrics = metrics_fn(logits, batch, dataset)
  
      return metrics, logits
  
imports: import collections
  import copy
  import functools
  from typing import Any, Callable, Dict, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries import optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.mbt import trainer
  from scenic.projects.polyvit import polyvit_base_model
  from scenic.projects.polyvit import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.train_lib import optax
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.transfer import fewshot_utils
label_desc: Use image node_id 2 for calling a global function with example usage: eval_step(task, dataset, modality, train_state, batch, flax_model, metrics_fn, debug) and returns: metrics,  logits