code: def eval_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      representation_fn: Callable[[Batch], jnp.ndarray],
      linear_probe: nn.Module,
      metrics_fn: classification_trainer.MetricFn,
  ) -> Metric:
      """Runs a single step of training.
  
      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.
        representation_fn: A function that given a batch, returns representations
          that are passed to the linear_probe_fn as input.
        linear_probe: The linear probe module.
        metrics_fn: A metrics function, that given logits and batch of data,
          calculates the metrics as well as the loss.
  
      Returns:
        Calculated metrics.
      """
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
      logits = linear_probe.apply(variables, representation_fn(batch))
      metrics = metrics_fn(logits, batch)
      return metrics
  
imports: import functools
  import logging
  from typing import Any, Callable, Dict, List, Mapping, Optional, Text, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import model_utils
  from scenic.train_lib import classification_trainer
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  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