code: def train(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Any,
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ):
      """Main training loop.
  
      Given the model class and dataset, it prepares the items needed to run the
      training, including the TrainState.
  
      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:
        train_sate that has the state of training (including current
          global_step, rng, and the optimizer), train_summary and eval_summary which
          are a dict of metrics.
      """
      lead_host = jax.process_index() == 0
      # The pool is used to perform async evaluation on the CPU.
      pool = futures.ThreadPoolExecutor(max_workers=2)
  
      # Build the loss_and_metrics_fn, metrics, and flax_model.
      model = model_cls(config, dataset.meta_data)
  
      # Initialize model.
      rng, init_rng = jax.random.split(rng)
  
      input_spec = [
          (
              dataset.meta_data["input_shape"],
              dataset.meta_data.get("input_dtype", jnp.float32),
          ),
          (dataset.meta_data["query_shape"], jnp.int32),
      ]
  
      (
          params,
          model_state,
          num_trainable_params,
          gflops,
      ) = train_utils.initialize_model(
          model_def=model.flax_model,
          input_spec=input_spec,
          config=config,
          rngs=init_rng,
      )
  
      if config.prior_prob:
          params = flax.core.unfreeze(params)
          bias_init = utils.init_classification_bias(
              params["class_head"]["logit_shift"]["bias"],
              config.prior_prob,
          )
          params["class_head"]["logit_shift"]["bias"] = bias_init
          params = flax.core.freeze(params)
  
      # Create optimizer & LR schedules.
      # We jit this, such that the arrays that are created are created on the same
      # device as the input is, in this case the CPU. Else they'd be on device[0].
      schedule = scenic_optax.make_schedule(config.get("schedule"))
      tx, sched_fns = scenic_optax.make(
          config.optimizer, schedule, params
      )
      opt_state = jax.jit(tx.init, backend="cpu")(params)
      sched_fns = [jax.jit(lr_fn, backend="cpu") for lr_fn in sched_fns]
  
      rng, train_rng = jax.random.split(rng)
  
      # Create chrono class to track and store training statistics and metadata:
      chrono = train_utils.Chrono()
  
      train_state = train_utils.TrainState(
          global_step=0,
          params=params,
          tx=tx,
          opt_state=opt_state,
          model_state=model_state,
          rng=train_rng,
          metadata={"chrono": chrono.save()},
      )
      start_step = train_state.global_step
  
      # Decide how to initialize training. Four options will be tried in this order:
      # 1. Continue training run from an existing checkpoint in the workdir.
      # 2. Resume training from a previous checkpoint, i.e. load both params and
      #    optimizer state (e.g. a cooldown job).
      # 3. Initialize the model parameters from a checkpoint, but not the optimizer
      #    state (e.g. a fine-tuning job).
      # 4. Train from scratch.
  
      if config.checkpoint:
          train_state, start_step = train_utils.restore_checkpoint(
              workdir, train_state
          )
      chrono.load(train_state.metadata["chrono"])
  
      if start_step != 0:
          # Option 1:
          logging.info(
              "Continuing from checkpoint in workdir: step=%s, workdir=%s",
              start_step,
              workdir,
          )
      else:
          # Option 2: Resume from previous training job:
          if config.get("resume_from") is not None:
              logging.info(
                  "Loading params and optimizer: %s", config.init_from
              )
              checkpoint_path = config.resume_from.get(
                  "checkpoint_path"
              )
              train_state = checkpoints.restore_checkpoint(
                  checkpoint_path, target=train_state
              )
  
          # Option 3: Load only the parameters, e.g. for fine-tuning:
          elif config.get("init_from") is not None:
              logging.info("Loading params: %s", config.init_from)
              init_config = (
                  config.init_from.copy_and_resolve_references()
              )
              # Delegate the actual loading to the model. `module.bind()` is needed to
              # initialize submodules, which have their own `load` functions.
              params = model.flax_model.bind({}).load(
                  train_state.params.unfreeze(), init_config
              )
              train_state = train_state.replace(
                  params=flax.core.freeze(params)
              )
  
          # Option 4: Train from scratch.
          else:
              logging.info("Training from scratch.")
  
      # Replicate the optimzier, state, and rng.
      train_state = jax_utils.replicate(train_state)
      del params  # do not keep a copy of the initial model
  
      # Calculate the total number of training steps.
      total_steps, steps_per_epoch = train_utils.get_num_training_steps(
          config, dataset.meta_data
      )
  
      train_step = get_train_step(
          flax_model=model.flax_model,
          loss_and_metrics_fn=model.loss_function,
          config=config,
          debug=config.debug_train,
      )
  
      train_step_pmapped = jax.pmap(
          train_step, axis_name="batch", donate_argnums=(0,)
      )
  
      ############### EVALUATION CODE #################
  
      eval_step = get_eval_step(
          flax_model=model.flax_model,
          loss_and_metrics_fn=model.loss_function,
          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
  
      metrics_normalizer_fn = functools.partial(
          utils.normalize_metrics_summary,
          object_detection_loss_keys=model.loss_terms_weights.keys(),
      )
  
      def evaluate(train_state, step, total_steps):
          """Runs evaluation code."""
          # For final evaluation, always run over the entire validation set.
          num_eval_steps = (
              total_eval_steps
              if step == total_steps
              and not config.get("light_eval", False)
              else steps_per_eval
          )
          eval_metrics = []
  
          for eval_step in range(num_eval_steps):
              logging.info("Running eval step %d", eval_step)
              eval_batch = next(dataset.valid_iter)
  
              with jax.profiler.TraceAnnotation(
                  "eval_step", step_num=step, _r=1
              ):
                  eval_metrics.append(
                      train_utils.unreplicate_and_get(
                          eval_step_pmapped(train_state, eval_batch)
                      )
                  )
  
          ############### LOG EVAL SUMMARY ###############
          def log_fn(step, eval_metrics, writer, metrics_normalizer_fn):
              return train_utils.log_eval_summary(
                  step=step,
                  eval_metrics=eval_metrics,
                  writer=writer,
                  metrics_normalizer_fn=metrics_normalizer_fn,
              )
  
          # Note that we return a Future on a summary instead of the summary itself!
          return pool.submit(
              log_fn,
              step=step,
              eval_metrics=eval_metrics,
              writer=writer,
              metrics_normalizer_fn=metrics_normalizer_fn,
          )
  
      ###################################################
  
      log_eval_steps = config.get("log_eval_steps") or steps_per_epoch
      if not log_eval_steps:
          raise ValueError(
              "'log_eval_steps' should be specified in the config."
          )
      log_summary_steps = config.get("log_summary_steps", 100)
      checkpoint_steps = (
          config.get("checkpoint_steps") or log_eval_steps
      )
  
      train_metrics, extra_training_logs, cpu_training_logs = [], [], []
      train_summary, eval_summary = None, None
      chrono.inform(
          start_step, total_steps, config.batch_size, steps_per_epoch
      )
  
      logging.info(
          "Start training from step %d to %d.",
          start_step + 1,
          total_steps + 1,
      )
      report_progress = periodic_actions.ReportProgress(
          num_train_steps=total_steps,
          writer=writer,
          every_secs=None,
          every_steps=log_summary_steps,
      )
  
      def write_note(note):
          if lead_host:
              platform.work_unit().set_notes(note)
  
      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
              )
          )
  
      if start_step == 0:
          step0_log = {"num_trainable_params": num_trainable_params}
          if gflops:
              step0_log["gflops"] = gflops
          writer.write_scalars(1, step0_log)
  
      write_note(f"First step compilations...\n{chrono.note}")
      for step in range(start_step + 1, total_steps + 1):
          with jax.profiler.StepTraceAnnotation("train", step_num=step):
              train_batch = next(dataset.train_iter)
              # Do the train step.
              with jax.profiler.TraceAnnotation(
                  "train_step", step_num=step, _r=1
              ):
                  train_state, t_metrics = train_step_pmapped(
                      train_state, train_batch
                  )
              # This will accumulate metrics in TPU memory up to the point that we log
              # them. This is no problem for small metrics but may be a problem for
              # large metrics. An alternative is to set `log_summary_steps` to a small
              # number, or to use `train_utils.unreplicate_and_get` here instead of
              # right before writing summaries, but that means in each step, we have
              # data transfer between tpu and host, which might slow down the training.
              train_metrics.append(t_metrics)
  
          for h in hooks:
              h(step)
  
          # Additional training logs: time, learning rate, num parameters.
          cpu_training_logs.append(
              {
                  f"learning_rate_{name}": lr_fn(step)
                  for name, lr_fn in zip(
                      config.schedule.keys(), sched_fns
                  )
              }
          )
  
          if (
              (step % log_summary_steps == 0)
              or (step == total_steps)
              or (lead_host and chrono.warmup)
          ):
              ############### LOG TRAIN SUMMARY ###############
              chrono.pause(wait_for=(train_metrics))
              if lead_host:
                  chrono.tick(step, writer, write_note)
              # Write summary:
              train_summary = train_utils.log_train_summary(
                  step=step,
                  train_metrics=jax.tree_util.tree_map(
                      train_utils.unreplicate_and_get, train_metrics
                  ),
                  extra_training_logs=cpu_training_logs
                  + jax.tree_util.tree_map(
                      train_utils.unreplicate_and_get,
                      extra_training_logs,
                  ),
                  writer=writer,
                  metrics_normalizer_fn=metrics_normalizer_fn,
              )
              # Reset metric accumulation for next evaluation cycle.
              train_metrics, extra_training_logs, cpu_training_logs = (
                  [],
                  [],
                  [],
              )
              chrono.resume()
              #################################################
  
          if (step % log_eval_steps == 0) or (step == total_steps):
              chrono.pause(wait_for=(train_state.params))
              start_time = time.time()
              with report_progress.timed("eval"):
                  eval_summary = evaluate(
                      train_state, step, total_steps
                  )
              duration = time.time() - start_time
              try:
                  ex = (
                      eval_summary.exception(1)
                      if eval_summary
                      else None
                  )
                  if ex is not None:
                      logging.error(
                          "Failed evaluation: %.4f sec.", duration
                      )
                      raise ex  # pylint: disable=raising-bad-type
                  logging.info(
                      "Done with evaluation: %.4f sec.", duration
                  )
              except futures.TimeoutError:
                  pass
              writer.flush()
              if step != total_steps:
                  eval_summary = None  # Free up space.
              chrono.resume()
  
          ##################### CHECKPOINTING ############################
          if (
              (step % checkpoint_steps == 0 and step > 0)
              or (step == total_steps)
          ) and config.checkpoint:
              chrono.pause(
                  wait_for=(train_state.params, train_state.opt_state)
              )
              with report_progress.timed("checkpoint"):
                  if lead_host:
                      # Take the first replica.
                      unrep_train_state = jax_utils.unreplicate(
                          train_state
                      )
                      metadata = unrep_train_state.metadata
                      metadata["chrono"] = chrono.save()
                      unrep_train_state = unrep_train_state.replace(
                          metadata=metadata
                      )
                      train_utils.save_checkpoint(
                          workdir, unrep_train_state
                      )
                      del unrep_train_state
              chrono.resume()
  
      # Wait until computations are done before exiting.
      if eval_summary is not None:
          eval_summary = eval_summary.result()
      pool.shutdown()
      train_utils.barrier()
      return train_state, train_summary, eval_summary
  
imports: from concurrent import futures
  import functools
  import time
  from typing import Any
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from clu import platform
  import flax
  from flax import jax_utils
  from flax.training import checkpoints
  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.owl_vit import utils
  from scenic.train_lib import optax
  from scenic.train_lib import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: train() and returns: train_state,  train_summary,  eval_summary, pool, train_utils