code: def eval_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      model: models.EncoderWithT5DecoderModel,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Any:
      """Runs a single step of evaluation.
  
      Note: The buffer of the provided batch is donated to the computation.
  
      Args:
        train_state: TrainState, the state of training including the current
        batch: A single batch of data. a metrics function, that given logits and
          batch of data, calculates the metrics as well as the loss.
        model: An EncoderWithT5DecoderModel. global_step, model_state, rng, and
          optimizer. The buffer of this argument can be donated to the computation.
        metrics_fn: A metrics function, that given logits and batch of data,
          calculates the metrics as well as the loss.
        config: Configuration 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 predicted tokens.
      """
      variables = {
          "params": train_state.optimizer.target,
          **train_state.model_state,
      }
      if config.dataset_configs.return_as_dict:
          encoder_inputs = batch["encoder_inputs"]
      else:
          raise NotImplementedError
  
      decoding_method = config.decoding.get(
          "decoding_method", "beamsearch"
      )
      if decoding_method == "beamsearch":
          decode_fn = models.beam_search
      elif decoding_method == "temperature_sample":
          decode_fn = models.temperature_sample
      else:
          raise ValueError("Unrecognized decoding method.")
      batch[
          "decoder_inputs"
      ] = {  # pytype: disable=container-type-mismatch  # jax-ndarray
          "decoder_input_tokens": batch["text_indices"][..., :-1],
          "decoder_target_tokens": batch["text_indices"][..., 1:],
      }
  
      decoded, _ = model.predict_batch_with_aux(
          variables,
          {
              "encoder_inputs": encoder_inputs,
              "decoder_inputs": batch["decoder_inputs"],
          },
          decode_fn,
          num_decodes=config.decoding.get("num_decodes"),
          alpha=config.decoding.get("alpha"),
          decoding_method=decoding_method,
          temperature=config.decoding.get("temperature"),
          eos_id=1,
          vocabulary_size=32128,
      )
  
      if debug:
          logging.info(
              "Shape of decoded in eval step is: %s", decoded.shape
          )
  
      return metrics_fn(decoded, batch), decoded
  
imports: import copy
  import dataclasses
  import functools
  import json
  import os
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from dmvr import tokenizers
  from flax import jax_utils
  from flax.core import unfreeze
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.t5 import model
  from scenic.projects.t5 import tokenizer
  from scenic.projects.vid2seq import load_utils
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import train_utils
  from scenic.projects.vid2seq import dvc_eval
  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, model, metrics_fn, config, debug) and returns: metrics_fn,  decoded