code: def eval_step(
      train_state: train_utils.TrainState,
      batch: constants.Batch,
      *,
      flax_model: nn.Module,
      metrics_fn: constants.MetricFn,
      model_config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[Dict[str, Tuple[float, int]], Dict[str, float]]:
      """Runs a single step of evaluation, TODO(ziniu): Add beam search decoding.
  
      Given the state of the training and a batch of data, computes
      the loss and updates the parameters of the model.
  
      Note that in this code, the buffers of the first (train_state) and second
      (batch) arguments are donated to the computation.
  
      Args:
        train_state: 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. The buffer of this argument can be donated to
          the computation.
        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.
        model_config: Config for model architecture.
        debug: Whether the debug mode is enabled during training. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Updated state of training, loss, computed metrics, and learning rate for
        logging.
      """
      variables = {
          "params": train_state.params,
          **train_state.model_state,
      }
      output_dict = flax_model.apply(
          variables,
          **batch,
          train=False,
          fuse_retrieval=model_config.fuse_retrieval,
          in_batch_neg=model_config.in_batch_neg,
          debug=debug,
      )
      metrics = metrics_fn(output_dict["predicted_logits"], batch)
  
      retr_loss, (retr_acc, _, _) = losses.contrastive_loss(
          query_emb=output_dict["base_query"],
          key_emb=output_dict["retr_keys"],
          temperature=model_config.get("temperature"),
      )
      logs = {"eval/retr_loss": retr_loss, "eval/retr_acc": retr_acc}
  
      return metrics, logs
  
imports: import functools
  from typing import Any, 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
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import losses
  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 3 for calling a global function with example usage: eval_step(train_state, batch) and returns: metrics,  logs