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_state 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. 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.
      rng, init_rng = jax.random.split(rng)
      (
          params,
          model_state,
          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,
      )
  
      # Get learning rate scheduler.
      learning_rate_fn = lr_schedules.get_learning_rate_fn(config)
  
      # Create optimizer.
      # 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].
      optimizer = optimizers.get_optimizer(
          config.optimizer_configs,
          learning_rate_fn=learning_rate_fn,
          params=params,
      )
      opt_state = jax.jit(optimizer.init, backend="cpu")(params)
  
      # Create chrono class to track and store training statistics and metadata:
      chrono = train_utils.Chrono()
  
      rng, train_rng = jax.random.split(rng)
      train_state = train_utils.TrainState(
          global_step=0,
          opt_state=opt_state,
          tx=optimizer,
          params=params,
          model_state=model_state,
          rng=train_rng,
          metadata={"chrono": chrono.save()},
      )
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, start_step = train_utils.restore_checkpoint(
              workdir, train_state
          )
      # Replicate the optimzier, state, and rng.
      train_state = jax_utils.replicate(train_state)
      del params  # Do not keep a copy of the initial params.
  
      # 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=learning_rate_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"),
              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
      )
  
      # 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
  
      train_metrics, train_images, extra_training_logs = [], [], []
      train_summary, eval_summary = None, 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
      )
      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)
  
      def write_note(note):
          if lead_host:
              platform.work_unit().set_notes(note)
  
      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,
                  t_images,
              ) = 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)
              t_logs = jax.tree_util.tree_map(
                  jax_utils.unreplicate, t_logs
              )
              # Additional training logs: learning rate:
              extra_training_logs.append(t_logs["learning_rate"])
          for h in hooks:
              h(step)
          chrono.pause()  # Below are once-in-a-while ops -> pause.
          ###################### LOG TRAIN SUMMARY ########################
          if (step % log_summary_steps == 1) or (step == total_steps):
              if lead_host:
                  chrono.tick(
                      step, writer=writer, write_note=write_note
                  )
              train_images.append(t_images)
              # train_metrics is list of a dictionaries of metrics, where the shape of
              # the metrics[key] is [n_local_devices]. However, because metric functions
              # have a psum, we have already summed across the whole sharded batch, and
              # what's returned is n_local_devices copies of the same summed metric.
              # So we do unreplicate and fetch them to host using `unreplicate_and_get`.
              train_summary = adv_train_utils.log_train_summary(
                  step=step,
                  train_metrics=jax.tree_util.tree_map(
                      jax.device_get, train_metrics
                  ),
                  train_images=jax.tree_util.tree_map(
                      jax.device_get, train_images
                  ),
                  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, train_images, extra_training_logs = (
                  [],
                  [],
                  [],
              )
          ################### EVALUATION #######################
          if (step % log_eval_steps == 1) or (step == total_steps):
              with report_progress.timed("eval"):
                  eval_metrics = []
                  # Sync model state across replicas.
                  train_state = (
                      train_utils.sync_model_state_across_replicas(
                          train_state
                      )
                  )
                  for _ in range(steps_per_eval):
                      eval_batch = next(dataset.valid_iter)
                      e_metrics, _ = eval_step_pmapped(
                          train_state, eval_batch
                      )
                      eval_metrics.append(
                          train_utils.unreplicate_and_get(e_metrics)
                      )
                  eval_summary = train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics,
                      writer=writer,
                  )
              writer.flush()
              del eval_metrics
          ##################### CHECKPOINTING ###################
          if (
              (step % checkpoint_steps == 0 and step > 0)
              or (step == total_steps)
          ) and config.checkpoint:
              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 regresesion testing.
      return train_state, train_summary, eval_summary
  
imports: import functools
  from typing import Any, Callable, Dict, Optional, Tuple, Type
  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.adversarialtraining import train_utils
  from scenic.projects.adversarialtraining.attacks import attack_compute
  from scenic.projects.adversarialtraining.attacks import attack_metrics
  from scenic.projects.adversarialtraining.models import models
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
label_desc: Use image node_id 4 for calling a global function with example usage: train() and returns: train_state,  train_summary,  eval_summary