code: def evaluate(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Any,
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> Dict[str, Any]:
      """Eval only loop.
  
      Given the model class and dataset, it prepares the items needed to run the
      evaluation without training.
  
      Args:
        rng: JAX PRNGKey.
        config: Configurations of the experiment.
        model_cls: Model class; A model has a flax_module, a loss_fn, and a
          metrics_fn associated with it.
        dataset: The dataset that has train_iter, eval_iter, meta_data, and
          optionally, test_iter.
        workdir: Directory for checkpointing.
        writer: CLU metrics writer instance.
  
      Returns:
        Eval summary.
      """
      lead_host = jax.process_index() == 0
  
      # The pool is used to perform misc operations such as logging in async way.
      pool = futures.ThreadPoolExecutor(max_workers=2)
  
      input_spec = [
          (
              dataset.meta_data["input_shape"],
              dataset.meta_data.get("input_dtype", jnp.float32),
          )
      ]
      (
          model,
          _,
          train_state,
          num_trainable_params,
          gflops,
          _,
      ) = get_model_and_tx_and_train_state(
          rng,
          dataset,
          config,
          model_cls,
          workdir,
          input_spec=input_spec,
      )
  
      step0_log = {"num_trainable_params": num_trainable_params}
      if gflops:
          step0_log["gflops"] = gflops
      writer.write_scalars(1, step0_log)
  
      report_progress = periodic_actions.ReportProgress(
          writer=writer,
          every_secs=None,
          every_steps=config.get("report_progress_step", 10),
      )
      hooks = []
      if lead_host:
          hooks.append(report_progress)
      if config.get("xprof", True) and lead_host:
          hooks.append(
              periodic_actions.Profile(
                  num_profile_steps=5, logdir=workdir
              )
          )
  
      eval_step = ddetr_eval.get_eval_step(
          flax_model=model.flax_model,
          loss_and_metrics_fn=model.loss_and_metrics_function,
          logits_to_probs_fn=model.logits_to_probs,
          debug=config.debug_eval,
      )
      eval_step_pmapped = jax.pmap(
          eval_step, axis_name="batch", donate_argnums=(1,)
      )
  
      # Ceil rounding such that we include the last incomplete batch.
      eval_batch_size = config.get("eval_batch_size", config.batch_size)
      total_eval_steps = int(
          np.ceil(
              dataset.meta_data["num_eval_examples"] / eval_batch_size
          )
      )
      steps_per_eval = config.get("steps_per_eval") or total_eval_steps
      logging.info("steps_per_eval: %s", steps_per_eval)
  
      metrics_normalizer_fn = functools.partial(
          detr_train_utils.normalize_metrics_summary,
          object_detection_loss_keys=model.loss_terms_weights.keys(),
      )
  
      global_metrics_evaluator = (
          None  # Only run eval on the lead_host node.
      )
      if lead_host:
          global_metrics_evaluator = (
              coco_eval.DeformableDetrGlobalEvaluator(
                  config.dataset_name
              )
          )
  
      # Sync model state across replicas (in case of having model state, e.g.
      # batch statistic when using batch norm).
      start_time = time.time()
      with report_progress.timed("eval"):
          train_state = train_utils.sync_model_state_across_replicas(
              train_state
          )
          (
              last_eval_step,
              last_eval_metrics,
          ), last_eval_future = ddetr_eval.run_eval(
              global_metrics_evaluator=global_metrics_evaluator,
              dataset=dataset,
              train_state=train_state,
              eval_step_pmapped=eval_step_pmapped,
              pool=pool,
              step=0,
              steps_per_eval=steps_per_eval,
          )
      duration = time.time() - start_time
      logging.info("Done with async evaluation: %.4f sec.", duration)
      writer.flush()
  
      # Wait until computations are done before exiting.
      pool.shutdown()
  
      assert last_eval_future is not None
      eval_summary = train_utils.log_eval_summary(
          step=last_eval_step,
          eval_metrics=last_eval_metrics,
          extra_eval_summary=last_eval_future.result(),
          writer=writer,
          metrics_normalizer_fn=metrics_normalizer_fn,
      )
      train_utils.barrier()
  
      logging.info("Eval Summary: %s.", eval_summary)
      return eval_summary
  
imports: from collections.abc import Mapping
  from concurrent import futures
  import functools
  import time
  from typing import Any, Callable, Dict, Sequence, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  from flax import jax_utils
  from flax.training.checkpoints import restore_checkpoint
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.baselines.deformable_detr import coco_eval
  from scenic.projects.baselines.deformable_detr import evaluate
  from scenic.projects.baselines.deformable_detr.model import DeformableDETRModel
  from scenic.projects.baselines.detr import train_utils
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.train_utils import PyTree
  from scenic.train_lib.train_utils import TrainState
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate() and returns: eval_summary