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,
  ):
      """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.
      rng, params_init_rng, dropout_init_rng = jax.random.split(
          rng, num=3
      )
      init_rngs = {
          "params": params_init_rng,
          "dropout": dropout_init_rng,
      }
      input_spec_dict = {}
      for key in config.dataset_configs.modalities:
          input_spec = (
              dataset.meta_data["input_shape"][key],
              dataset.meta_data["input_dtype"][key],
          )
          input_spec_dict[key] = input_spec
  
      (
          params,
          model_state,
          num_trainable_params,
          gflops,
      ) = initialize_model(
          model_def=model.flax_model,
          input_spec_dict=input_spec_dict,
          config=config,
          rngs=init_rngs,
          is_train=True,
      )
  
      # 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 = jax.jit(
          optimizers.get_optimizer(config).create, backend="cpu"
      )(params)
      rng, train_rng = jax.random.split(rng)
      train_state = train_utils.TrainState(
          global_step=0,
          optimizer=optimizer,
          model_state=model_state,
          rng=train_rng,
          accum_train_time=0,
      )
      start_step = train_state.global_step
      if config.checkpoint:
          train_state, start_step = train_utils.restore_checkpoint(
              workdir, train_state
          )
          logging.info("Parameter summary after restoring checkpoint")
          debug_utils.log_param_shapes(
              train_state.optimizer.target
          )  # pytype: disable=attribute-error
  
      if (
          start_step == 0  # Which means "no" checkpoint is restored!
          and config.get("init_from") is not None
      ):
          restored_model_cfg = config.init_from.get("model_config")
          init_checkpoint_path = config.init_from.get("checkpoint_path")
          restored_train_state = (
              pretrain_utils.restore_pretrained_checkpoint(
                  init_checkpoint_path, train_state, assert_exist=True
              )
          )
          # Load params from the init_model.
          train_state = model.init_from_train_state(  # pytype: disable=attribute-error
              train_state, restored_train_state, restored_model_cfg
          )
          logging.info(
              "Parameter summary after adapting pretrained checkpoint."
          )
          debug_utils.log_param_shapes(train_state.optimizer.target)
          del restored_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
      )
      # Get learning rate scheduler.
      learning_rate_fn = lr_schedules.get_learning_rate_fn(config)
  
      train_step_pmapped = jax.pmap(
          functools.partial(
              train_step,
              flax_model=model.flax_model,
              learning_rate_fn=learning_rate_fn,
              loss_fn=model.loss_function,
              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"),
              config=config,
              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_valid_ex: int,
      ) -> Dict[str, Any]:
          eval_summary = {}
          image_summary = {}
          if not isinstance(
              valid_iter, dict
          ):  # Only on validation set.
              valid_iter, num_valid_ex = {"valid": valid_iter}, {
                  "valid": num_valid_ex
              }
  
          for val_name, val_iter in valid_iter.items():
              num_ex = num_valid_ex[val_name]
              # 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 iteration in range(steps_per_eval):
                  eval_batch = next(val_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,
                      predictions,
                      token_mask,
                      targets,
                  ) = eval_step_pmapped(train_state, eval_batch)
                  eval_metrics.append(
                      train_utils.unreplicate_and_get(e_metrics)
                  )
  
                  if iteration == 0:
                      for key in eval_batch[
                          "inputs"
                      ].keys():  # pytype: disable=attribute-error  # jax-ndarray
                          input_size = eval_batch["inputs"][key].shape[
                              2:
                          ]
                          unreplicate = jax_utils.unreplicate
                          image_grid = get_image_grid(
                              unreplicate(targets[key]),
                              unreplicate(predictions[key]),
                              unreplicate(token_mask[key]),
                              config,
                              target_modality=key,
                              input_size=input_size,
                          )
  
                          if image_grid is not None:
                              image_summary[
                                  f"valid/reconstruction_{key}"
                              ] = jax.device_get(image_grid)
  
                  del predictions
                  del token_mask
                  del targets
              eval_summary.update(
                  train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics,
                      writer=writer,
                      key_separator="/",
                      prefix=val_name,
                  )
              )
          del eval_metrics
          if image_summary:
              writer.write_images(step, image_summary)
              writer.flush()
          return eval_summary
  
      train_metrics, extra_training_logs = [], []
      train_summary, eval_summary = None, None
  
      chrono = train_utils.Chrono(
          first_step=start_step,
          total_steps=total_steps,
          steps_per_epoch=steps_per_epoch,
          global_bs=config.batch_size,
          accum_train_time=int(
              jax_utils.unreplicate(train_state.accum_train_time)
          ),
      )
  
      logging.info(
          "Starting training loop at step %d for %d steps",
          start_step,
          total_steps,
      )
  
      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)
  
      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, lr = train_step_pmapped(
                  train_state, train_batch
              )
              train_metrics.append(t_metrics)
              # Additional training logs: learning rate:
              extra_training_logs.append({"learning_rate": lr})
  
          # Quick indication that training is happening.
          logging.log_first_n(
              logging.INFO, "Finished training step %d.", 5, step
          )
          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)
              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(
                      train_utils.unreplicate_and_get,
                      extra_training_logs,
                  ),
                  key_separator="/",
                  writer=writer,
              )
              # Reset metric accumulation for next evaluation cycle.
              train_metrics, extra_training_logs = [], []
  
          ################### EVALUATION #######################
          if (step % log_eval_steps == 1) or (step == total_steps):
              # Sync model state across replicas.
              with report_progress.timed("eval"):
                  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"],
                  )
  
          ##################### CHECKPOINTING ############################
          if (
              (step % checkpoint_steps == 1) 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:
                      train_state.replace(  # pytype: disable=attribute-error
                          accum_train_time=chrono.accum_train_time
                      )
                      train_utils.save_checkpoint(workdir, 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, Mapping, Optional, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  import flax
  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
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.av_mae import base_model
  from scenic.projects.av_mae import train_utils
  from scenic.train_lib_deprecated import lr_schedules
  from scenic.train_lib_deprecated import optimizers
  from scenic.train_lib_deprecated import pretrain_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 8 for calling a global function with example usage: train() and returns: train_state,  train_summary,  eval_summary, eval_summary