code: def train(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> Tuple[Any, Any]:
      """Main training loop lives in this function.
  
      Given the model class and dataset, it prepares the items needed to run the
      training, including the utils.TrainState.
  
      Args:
        rng: Jax rng key.
        config: Configurations of the experiment.
        dataset: The dataset that has train_iter and meta_data.
        workdir: Directory for checkpointing.
        writer: CLU metrics writer instance.
  
      Returns:
        train_state that has the state of training.
      """
      lead_host = jax.process_index() == 0
  
      # Build the loss_fn, metrics, and flax_model.
      model = vit.ViTLOCAModel(config, dataset.meta_data)
  
      # Randomly initialize model parameters.
      rng, init_rng = jax.random.split(rng)
      (
          params,
          _,
          num_trainable_params,
          gflops,
      ) = train_utils.initialize_model(
          model_def=model.flax_model,
          input_spec=[
              (
                  dataset.meta_data["input_shape"],
                  dataset.meta_data.get("input_dtype", jnp.float32),
              )
          ],
          config=config,
          rngs=init_rng,
      )
      # Only one model function but two sets of parameters.
      ema_params = copy.deepcopy(params)
  
      # Get learning rate and ema temperature schedulers.
      learning_rate_fn = lr_schedules.get_learning_rate_fn(config)
      momentum_parameter_scheduler = lr_schedules.compound_lr_scheduler(
          config.momentum_rate
      )
  
      # Create optimizer.
      weight_decay_mask = jax.tree_map(lambda x: x.ndim != 1, params)
      tx = optax.inject_hyperparams(optax.adamw)(
          learning_rate=learning_rate_fn,
          weight_decay=config.weight_decay,
          mask=weight_decay_mask,
      )
      opt_state = jax.jit(tx.init, backend="cpu")(params)
  
      # Create chrono class to track and store training statistics and metadata.
      chrono = train_utils.Chrono()
  
      # Create the TrainState to track training state (i.e. params and optimizer).
      train_state = utils.TrainState(
          global_step=0,
          opt_state=opt_state,
          tx=tx,
          params=params,
          ema_params=ema_params,
          rng=rng,
          metadata={"chrono": chrono.save()},
      )
  
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, start_step = utils.restore_checkpoint(
              workdir, train_state
          )
      chrono.load(train_state.metadata["chrono"])
      train_state = train_state.replace(metadata={})
      # Replicate the training state: optimizer, params and rng.
      train_state = jax_utils.replicate(train_state)
      del params, ema_params
      total_steps, steps_per_epoch = train_utils.get_num_training_steps(
          config, dataset.meta_data
      )
  
      # The function that performs one step of loca training.
      loca_train_step_pmapped = jax.pmap(
          functools.partial(
              loca_train_step,
              flax_model=model.flax_model,
              loss_fn=model.loss_function,
              metrics_fn=model.get_metrics_fn(),
              momentum_parameter_scheduler=momentum_parameter_scheduler,
              config=config,
          ),
          axis_name="batch",
          # We can donate both buffers of train_state and train_batch.
          donate_argnums=(0, 1),
      )
  
      train_metrics, train_summary = [], None
      chrono.inform(
          start_step, total_steps, config.batch_size, steps_per_epoch
      )
      report_progress = periodic_actions.ReportProgress(
          num_train_steps=total_steps, writer=writer
      )
  
      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)
      logging.info("Starting training loop at step %d.", start_step + 1)
      for step in range(start_step + 1, total_steps + 1):
          with jax.profiler.StepTraceAnnotation("train", step_num=step):
              train_batch = next(dataset.train_iter)
              train_state, tm = loca_train_step_pmapped(
                  train_state, train_batch
              )
              train_metrics.append(tm)
          for h in hooks:
              h(step)
          ###################### LOG TRAIN SUMMARY ########################
          if (step % config.get("log_summary_steps") == 1) or (
              step == total_steps
          ):
              chrono.pause()
              if lead_host:
                  chrono.tick(step, writer, write_note)
              train_summary = train_utils.log_train_summary(
                  step=step,
                  train_metrics=jax.tree_util.tree_map(
                      train_utils.unreplicate_and_get, train_metrics
                  ),
                  writer=writer,
              )
              chrono.resume()
              train_metrics = []
          ##################### CHECKPOINTING ###################
          if (
              (step % config.get("checkpoint_steps") == 1 and step > 1)
              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.replace(
                          metadata=metadata
                      )  # pytype: disable=attribute-error
                      utils.save_checkpoint(workdir, unrep_train_state)
                      del unrep_train_state
              chrono.resume()  # Un-pause now.
      # Wait until computations are done before exiting.
      train_utils.barrier_across_hosts()
      # Return the train summary after last step.
      return train_state, train_summary
  
imports: import copy
  import functools
  from typing import Any, Callable, Dict, 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
  from jax.example_libraries import optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.loca import utils
  from scenic.projects.loca import vit
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: train() and returns: train_state,  train_summary