code: def train(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Type[base_model.BaseModel],
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> Tuple[train_utils.TrainState, Dict[str, Any], Dict[str, 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 TrainState.
  
      Args:
        rng: Jax rng key.
        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,
        model_state, rng, and the optimizer), train_summary and eval_summary which
        are dict of metrics (from the last evaluation and train metric logging
        respectively). These outputs are used for regression testing.
      """
      lead_host = jax.process_index() == 0
      # Build the loss_fn, metrics, and flax_model.
      model = model_cls(config, dataset.meta_data)
  
      # Initialize model.
      (
          train_state,
          chrono,
          lr_fn,
          start_step,
          num_trainable_params,
          gflops,
      ) = init_state(rng, config, model, dataset, workdir)
  
      # Replicate the optimzier, state, and rng.
      train_state = jax_utils.replicate(train_state)
  
      # Calculate the total number of training steps.
      total_steps, steps_per_epoch = train_utils.get_num_training_steps(
          config, dataset.meta_data
      )
  
      train_step_pmapped = jax.pmap(
          functools.partial(
              train_step,
              flax_model=model.flax_model,
              loss_fn=model.loss_function,
              lr_fn=lr_fn,
              metrics_fn=model.get_metrics_fn("train"),
              config=config,
              debug=config.debug_train,
          ),
          axis_name="batch",
          # We can donate both buffers of train_state and train_batch.
          donate_argnums=(0, 1),
      )
      eval_step_pmapped = jax.pmap(
          functools.partial(
              eval_step,
              flax_model=model.flax_model,
              metrics_fn=model.get_metrics_fn("validation"),
              all_gather=config.get("global_metrics", False),
              debug=config.debug_eval,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1,),
      )
      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."
          )
      checkpoint_steps = (
          config.get("checkpoint_steps") or log_eval_steps
      )
      log_summary_steps = (
          config.get("log_summary_steps") or log_eval_steps
      )
  
      def evaluate(
          train_state: train_utils.TrainState,
          step: int,
          valid_iter: Iterator[Batch],
          num_ex: int,
          val_name: str,
      ) -> Dict[str, Any]:
          eval_summary = {}
          # Ceil rounding such that we include the last incomplete batch.
          total_eval_steps = int(np.ceil(num_ex / config.batch_size))
          steps_per_eval = (
              config.get("steps_per_eval") or total_eval_steps
          )
          eval_metrics = []
          for _ in range(steps_per_eval):
              eval_batch = next(valid_iter)
              if dataset.meta_data[
                  "target_is_onehot"
              ]:  # Which includes multi-hot.
                  # Ignore the entries with all zero label for evaluation.
                  eval_batch["batch_mask"] *= eval_batch["label"].max(
                      axis=-1
                  )
              e_metrics, e_output, e_batch = eval_step_pmapped(
                  train_state, eval_batch
              )
              eval_metrics.append(
                  train_utils.unreplicate_and_get(e_metrics)
              )
              if compute_global_metrics:
                  # Unreplicate outputs of eval_step_pmapped that are coming from
                  # `lax.all_gather`, fetch to the host and add to the Evaluator:
                  e_batch_mask = train_utils.unreplicate_and_get(
                      e_batch["batch_mask"]
                  ).astype(bool)
                  # Classification: 'label', regression: 'target'
                  t_key = "label" if "label" in e_batch else "targets"
                  global_metrics_evaluator.add_batch_of_examples(
                      target=train_utils.unreplicate_and_get(
                          e_batch[t_key]
                      )[e_batch_mask],
                      output=train_utils.unreplicate_and_get(e_output)[
                          e_batch_mask
                      ],
                  )
                  del e_batch, e_output, e_batch_mask
          eval_global_metrics_summary = None
          if compute_global_metrics:
              eval_global_metrics_summary = (
                  global_metrics_evaluator.compute_metrics(
                      clear_annotations=True
                  )
              )
          eval_summary.update(
              train_utils.log_eval_summary(
                  step=step,
                  eval_metrics=eval_metrics,
                  extra_eval_summary=eval_global_metrics_summary,
                  writer=writer,
                  prefix=val_name,
              )
          )
          del eval_metrics, eval_global_metrics_summary
          writer.flush()
          return eval_summary
  
      # If `global_metrics` are set in the config and we are the lead host
      compute_global_metrics = False
      if config.get("global_metrics", False) and lead_host:
          compute_global_metrics = True
      if compute_global_metrics:
          global_metrics_evaluator = sv_metric.TruvariGlobalEvaluator(
              config.global_metrics
          )
  
      train_metrics, extra_training_logs = [], []
      train_summary, eval_summary = None, None
  
      chrono.inform(
          start_step, total_steps, config.batch_size, steps_per_epoch
      )
      logging.info("Starting training loop at step %d.", start_step + 1)
      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)
  
      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)
              train_state, t_metrics, t_logs = 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 (e.g. segmentation) 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)
              # Additional training logs: learning rate:
              t_logs = jax.tree_util.tree_map(
                  jax_utils.unreplicate, t_logs
              )
              extra_training_logs.append(t_logs)
  
          # Quick indication that training is happening.
          logging.log_first_n(
              logging.INFO, "Finished training step %d.", 5, step
          )
          for h in hooks:
              h(step)
  
          ############### LOG TRAIN SUMMARY ###############
          if (
              (step % log_summary_steps == 1)
              or (step == total_steps)
              or (lead_host and chrono.warmup)
          ):
              chrono.pause(wait_for=(train_metrics))
              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
                  ),
                  extra_training_logs=jax.tree_util.tree_map(
                      jax.device_get, extra_training_logs
                  ),
                  writer=writer,
              )
              # Reset metric accumulation for next evaluation cycle.
              train_metrics, extra_training_logs = [], []
              chrono.resume()
          ################### EVALUATION #######################
          if (step % log_eval_steps == 1) or (step == total_steps):
              chrono.pause(wait_for=(train_state.params))
              with report_progress.timed("eval"):
                  # Sync model state across replicas.
                  train_state = (
                      train_utils.sync_model_state_across_replicas(
                          train_state
                      )
                  )
                  eval_summary = evaluate(
                      train_state,
                      step,
                      dataset.valid_iter,
                      dataset.meta_data["num_eval_examples"],
                      dataset.meta_data["eval_name"],
                  )
              if dataset.test_iter:
                  with report_progress.timed("test"):
                      eval_summary = evaluate(
                          train_state,
                          step,
                          dataset.test_iter,
                          dataset.meta_data["num_test_examples"],
                          dataset.meta_data["test_name"],
                      )
              chrono.resume()
          ##################### CHECKPOINTING ############################
          if (
              (step % 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"):
                  # Sync model state across replicas.
                  train_state = (
                      train_utils.sync_model_state_across_replicas(
                          train_state
                      )
                  )
                  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
                      train_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 and eval summary after last step for regression testing.
      return train_state, train_summary, eval_summary
  
imports: import functools
  from typing import Any, Callable, Dict, Iterator, Tuple, Optional, Type
  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.optimizers import clip_grads
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.svvit import metrics
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 5 for calling a global function with example usage: train() and returns: train_state,  train_summary,  eval_summary, eval_summary