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.
      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,
      )
  
      # Create optimizer.
      lr_fn = lr_schedules.get_learning_rate_fn(config)
      optimizer_config = optimizers.get_optax_optimizer_config(config)
      # If the config is already an optax-compatible config, better call directly:
      #   optimizers.get_optimizer(config.optimizer_configs, lr_fn)
      tx = optimizers.get_optimizer(
          optimizer_config, lr_fn, params=params
      )
      # 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].
      opt_state = jax.jit(tx.init, backend="cpu")(params)
  
      rng, train_rng = jax.random.split(rng)
  
      # Create chrono class to track and store training statistics and metadata:
      chrono = train_utils.Chrono()
  
      train_state = train_utils.TrainState(
          global_step=0,
          opt_state=opt_state,
          tx=tx,
          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
          )
      chrono.load(train_state.metadata["chrono"])
      train_state = train_state.replace(metadata={})
      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")
          if init_checkpoint_path is not None:
              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
              )
              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
      )
  
      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"),
              debug=config.debug_eval,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1,),
      )
      if "fewshot" in config:
          representation_fn_fewshot = functools.partial(
              representation_fn,
              flax_model=model.flax_model,
              representation_layer=config.fewshot.representation_layer,
          )
          fewshotter = fewshot_utils.FewShotEvaluator(
              representation_fn_fewshot, config.fewshot
          )
  
      if "linear_probe" in config:
          representation_fn_linear_probe = functools.partial(
              representation_fn,
              flax_model=model.flax_model,
              representation_layer=config.linear_probe.representation_layer,
              gather_to_host=False,
          )
          rng, linear_probe_rng = jax.random.split(rng)
          linear_probe = linear_probe_utils.LinearEvaluator(
              representation_fn=representation_fn_linear_probe,
              rng=linear_probe_rng,
              linear_eval_config=config.linear_probe,
          )
  
      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
      )
      max_checkpoint_keep = config.get("max_checkpoint_keep", 3)
      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 = {}
          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.
              eval_batch_size = config.get(
                  "eval_batch_size", config.batch_size
              )
              total_eval_steps = int(np.ceil(num_ex / eval_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(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, _ = eval_step_pmapped(
                      train_state, eval_batch
                  )
                  eval_metrics.append(
                      train_utils.unreplicate_and_get(e_metrics)
                  )
              eval_summary.update(
                  train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics,
                      writer=writer,
                      prefix=val_name,
                  )
              )
          del eval_metrics
          writer.flush()
          return eval_summary
  
      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,
          every_secs=None,
          every_steps=config.get(
              "report_progress_step", log_summary_steps
          ),
      )
  
      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"],
                  )
              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"):
                  train_utils.handle_checkpointing(
                      train_state, chrono, workdir, max_checkpoint_keep
                  )
              chrono.resume()
  
          ##################### FEWSHOT EVALUATION ############################
          if "fewshot" in config:
              # Compute few-shot on-the-fly evaluation.
              if (step % config.fewshot.log_eval_steps == 1) or (
                  step == total_steps
              ):
                  chrono.pause(wait_for=(train_state.params))
                  with report_progress.timed("fewshot"):
                      results = fewshotter.run_all(
                          train_state, config.fewshot.datasets
                      )
                      fewshotter.log_fewshot_summary(
                          writer=writer, step=step, results=results
                      )
                      del results
                      writer.write_scalars(
                          step, {"zz/epoch": step / steps_per_epoch}
                      )
                  writer.flush()
                  chrono.resume()  # Un-pause now.
  
          ##################### LINEAR-PROBE EVALUATION ##########################
          if "linear_probe" in config:
              if (
                  config.linear_probe.log_eval_steps > 0
                  and step % config.linear_probe.log_eval_steps == 1
              ) or (step == total_steps):
                  chrono.pause(wait_for=(train_state.params))
                  with report_progress.timed("linear_probe"):
                      linear_probe.run_all(
                          train_state,
                          config.linear_probe.datasets,
                          writer=writer,
                          repr_step=step,
                      )
                  writer.flush()
                  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.
      assert train_summary is not None
      assert eval_summary is not None
      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
  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
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  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
  from scenic.train_lib.transfer import fewshot_utils
  from scenic.train_lib.transfer import linear_probe_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, eval_summary