code: def train_and_eval(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Any,
      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:
        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
      """
      host_id = jax.process_index()
      lead_host = host_id == 0
      # Build the loss_fn, metrics, and flax_model.
      model = model_cls(config, dataset.meta_data)
      train_state, start_step, chrono, lr_fn = init_state(
          model, dataset, config, workdir, rng, writer
      )
  
      logging.info("Complete initialization. Start Training.")
      train_state = jax_utils.replicate(train_state)
      logging.info("Number of processes is %s", jax.process_count())
  
      # Get the pmapped train and eval steps.
      train_step_pmapped = jax.pmap(
          functools.partial(
              train_step,
              flax_model=model.flax_model,
              loss_fn=model.loss_function,
              metrics_fn=model.get_metrics_fn("train"),
              debug=config.debug_train,
              model_config=config.model,
          ),
          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"),
              debug=config.debug_eval,
              model_config=config.model,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1,),
      )
      train_summary, eval_summary, e_metrics = {}, {}, {}
      # Calculate the total number of training steps.
      total_steps, steps_per_epoch = train_utils.get_num_training_steps(
          config, dataset.meta_data
      )
      log_eval_steps = config.get("log_eval_steps", steps_per_epoch)
      checkpoint_steps = config.get("checkpoint_steps", steps_per_epoch)
      log_summary_steps = config.get(
          "log_summary_steps", log_eval_steps
      )
  
      logging.info("Start training from step %d", start_step + 1)
      report_progress = periodic_actions.ReportProgress(
          num_train_steps=total_steps, writer=writer
      )
      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
              )
          )
  
      chrono.inform(
          int(start_step),
          int(total_steps),
          int(config.batch_size),
          int(steps_per_epoch),
      )
  
      summary_builder = trainer_utils.SummaryBuilder([], [])
  
      def write_note(note):
          if lead_host:
              platform.work_unit().set_notes(note)
  
      for step in range(start_step + 1, total_steps + 1):
          if lead_host:
              logging.info("training for step %d", step)
          ###################### Training ########################
          with jax.profiler.StepTraceAnnotation("train", step_num=step):
              train_batch = next(dataset.train_iter)
              train_state, t_metrics, logs = train_step_pmapped(
                  train_state=train_state, batch=train_batch
              )
          for hook in hooks:
              hook(step)
          logs["learning_rate"] = lr_fn(step).reshape([-1, 1])
          summary_builder.update(
              metrics_update=t_metrics, extra_logs_update=logs
          )
          if lead_host:
              logging.info("finish training for step %d", step)
          ###################### LOG TRAIN SUMMARY ########################
          if (step % log_summary_steps == 10) or (step == total_steps):
              chrono.pause()
              if lead_host:
                  logging.info("log training summary")
                  chrono.tick(step, writer, write_note)
              train_summary = summary_builder.write(writer, step)
              chrono.resume()
          ################### EVALUATION ################################
          should_eval = (step % log_eval_steps == 10) or (
              step == total_steps
          )
          if should_eval:
              logging.info("Start validation!")
              chrono.pause(wait_for=(train_state.params))
              # Sync model state across replicas.
              train_state = (
                  train_utils.sync_model_state_across_replicas(
                      train_state
                  )
              )
              for ds_name in dataset.valid_iter:
                  logging.info("Validate on %s", ds_name)
                  # 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_metrics_all = []
                  for _ in range(steps_per_eval):
                      eval_batch = next(dataset.valid_iter[ds_name])
                      e_metrics, logs = eval_step_pmapped(
                          train_state=train_state, batch=eval_batch
                      )
                      eval_metrics_all.append(
                          train_utils.unreplicate_and_get(e_metrics)
                      )
                  logging.info(e_metrics)
                  logging.info(logs)
                  eval_summary = train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics_all,
                      writer=writer,
                      prefix=ds_name,
                      extra_eval_summary=jax.tree_util.tree_map(
                          train_utils.unreplicate_and_get, logs
                      ),
                  )
              chrono.resume()
  
          ##################### CHECKPOINTING ###########################
          if not config.checkpoint:
              continue
          elif (
              step % checkpoint_steps == 0
              and step > 0
              or (step == total_steps)
          ):
              logging.info("Save 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:
                      logging.info(
                          "checkpointing (training step: %d)", step
                      )
                      # 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()
              logging.info("Checkpoint saved!")
  
      # Wait until computations are done before exiting.
      train_utils.barrier_across_hosts()
      # Return the train and eval summary after last step.
      return train_state, train_summary, eval_summary
  
imports: import functools
  from typing import Any, Dict, Optional, 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
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import losses
  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 4 for calling a global function with example usage: train_and_eval() and returns: train_state,  train_summary,  eval_summary