code: def eval_only(
      rng: np.ndarray,
      config: ml_collections.ConfigDict,
      *,
      workdir: str,
      writer: Any,
      model_cls,
      dataset_dict,
  ) -> Tuple[train_utils.TrainState, Dict[str, Any], Dict[str, Any]]:
      """Evaluate the model.
  
      Args:
        rng: JAX prng key.
        config: The configuration of the experiment.
        workdir: Where to checkpoint and write the summaries.
        writer: Summary writer object.
        model_cls: The model class used to instantiate the model.
        dataset_dict: The dataset for training and evaluation.
  
      Returns:
        A tuple with:
          * the state that has the state of training (including current
            global_step, model_state, rng, and the optimizer)
          * a dictionary with the train_summary
          * a dictionary with the evaluation summary
      """
  
      datasets_metadata = {
          name: ds.meta_data for name, ds in dataset_dict.items()
      }
      all_datasets = []
      all_datasets_num_train_examples = []
      for name, metadata in datasets_metadata.items():
          all_datasets.append(name)
          all_datasets_num_train_examples.append(
              metadata.get("num_train_examples", 0)
          )
      dataset = dataset_dict[all_datasets[0]]
  
      model = model_cls(config, dataset.meta_data)
      _, eval_step_pmapped = pmapped_steps(model, config)
  
      train_state, start_step = init_state(
          model, dataset, config, workdir, rng
      )  # pytype: disable=wrong-arg-types  # jax-ndarray
      assert start_step == 0
      train_state = jax_utils.replicate(train_state)
      logging.info("Number of processes is %s", jax.process_count())
  
      del rng  # So that we don't mistakenly re-use it.
  
      # Build a tokenizer for the evaluation
      tokenizer = get_tokenizer(config)
  
      hooks = []
      if config.get("xprof", True) and jax.process_index() == 0:
          hooks.append(
              periodic_actions.Profile(
                  num_profile_steps=5, logdir=workdir
              )
          )
  
      # Evaluate every `log_eval_steps`.
      for ds_name in dataset.valid_iter:
          # Compute the number of evaluation steps per dataset.
          num_eval_examples = dataset.meta_data["num_eval_examples"][
              ds_name
          ]
          total_eval_steps = int(
              np.ceil(
                  num_eval_examples / (config.get("eval_batch_size"))
              )
          )
          steps_per_eval = config.get(
              "steps_per_eval", total_eval_steps
          )
  
          eval_summary = eval_and_log_summary(
              train_state=train_state,
              iterator=dataset.valid_iter[ds_name],
              eval_step_fn=eval_step_pmapped,
              eval_steps=steps_per_eval,
              writer=writer,
              train_iteration=0,
              tokenizer=tokenizer,
              dataset_name=ds_name,
              num_bins=config.dataset_configs.num_bins,
              vocabulary_size=config.dataset_configs.vocabulary_size,
              abs_time_token=config.dataset_configs.abs_time_token,
              time_format=config.dataset_configs.time_format,
              tmp_only=config.dataset_configs.tmp_only,
              runlocal=config.runlocal,
              order=config.dataset_configs.order,
              workdir=workdir,
              soda="soda" in config.eval_metrics,
              eval_batch_size=config.eval_batch_size,
              max_events=config.dataset_configs.max_events,
              para="para" in ds_name,
              t=1000000.0,  # 1 FPS
              is_split=config.dataset_configs.split,
          )
  
      # Return the train and eval summary after last step.
      return train_state, {}, eval_summary
  
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 13 for calling a global function with example usage: eval_only(rng, config) and returns: train_state,  dict,  eval_summary