code: def train(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Any,
      dataset_dict: Dict[str, 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_dict: A dict of datasets that each 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.
      datasets_metadata = {
          name: ds.meta_data for name, ds in dataset_dict.items()
      }
  
      # Build the loss_and_metrics_fn, metrics, and flax_model.
      model = model_cls(config, datasets_metadata)
  
      # Initialize model.
      rng, init_rng = jax.random.split(rng)
      input_spec = {}
      for ds_name, meta_data in datasets_metadata.items():
          input_spec_key = (
              ("dataset", ds_name),
              ("task", meta_data["task"]),
              ("modality", meta_data["modality"]),
          )
  
          if meta_data["modality"] == polyvit_base_model.Modality.AUDIO:
              input_shape = meta_data["input_shape"]["spectrogram"]
          else:
              input_shape = meta_data["input_shape"]
  
          if meta_data["task"] in [
              polyvit_base_model.Task.LABEL,
              polyvit_base_model.Task.MULTILABEL,
              polyvit_base_model.Task.MULTIHEADLABEL,
          ]:
              input_spec[input_spec_key] = [
                  (
                      input_shape,
                      meta_data.get("input_dtype", jnp.float32),
                  )
              ]
          else:
              raise ValueError(
                  f'Input specs for the task "{meta_data["task"]}" is not defined.'
              )
  
      (
          params,
          model_state,
          num_trainable_params,
          gflops,
      ) = train_utils.initialize_multitask_model(
          model_def=model.flax_model,
          input_spec=input_spec,
          config=config,
          rngs=init_rng,
      )
  
      # Multi-task training strategy of 'Weighted Task Sampling':
      all_datasets = []
      all_datasets_num_train_examples = []
      for name, metadata in datasets_metadata.items():
          all_datasets.append(name)
          all_datasets_num_train_examples.append(
              metadata.get("num_train_examples", 0)
          )
      ds_indices_per_step = []
      for index, ds_name in enumerate(all_datasets):
          n_steps = config.batch_sampling_strategy_steps.get(ds_name)
          ds_indices_per_step.append(jnp.full((n_steps,), index))
      ds_indices_per_step = jnp.concatenate(ds_indices_per_step)
      ds_indices_per_step = jax.random.permutation(
          jax.random.PRNGKey(0), ds_indices_per_step
      )
      # Calculate the total number of training steps.
      (
          total_steps,
          steps_per_epoch,
      ) = polyvit_train_utils.get_num_training_steps(
          config, datasets_metadata
      )
  
      def get_dataset_at_step(step):
          return all_datasets[
              ds_indices_per_step[step]
          ]  # pytype: disable=unsupported-operands  # jax-types
  
      # Create LR schedules and optimizer.
      schedule_fns = scenic_optax.make_schedule(config.get("schedule"))
  
      def update_schedule_fn(sfn):
          (re, name, (fn, base_lr)) = sfn
          updated_lr = []
          for step in range(1, total_steps + 1):
              dataset = get_dataset_at_step(step)
              updated_lr.append(
                  fn(step)
                  * config.get("lr_coefs", {dataset: 1.0})[dataset]
              )
          updated_lr = jnp.array(updated_lr)
          return (re, name, (lambda step: updated_lr[step], base_lr))
  
      schedule_fns = [update_schedule_fn(sfn) for sfn in schedule_fns]
  
      tx, _ = scenic_optax.make(config.optimizer, schedule_fns, params)
      opt_state = tx.init(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:
              if config.init_from.get("init_from_vit", False):
                  checkpoint_format = config.init_from.get(
                      "checkpoint_format", "scenic"
                  )
                  if checkpoint_format == "scenic":
                      restored_train_state = (
                          pretrain_utils.restore_pretrained_checkpoint(
                              init_checkpoint_path,
                              None,
                              assert_exist=True,
                          )
                      )
                  elif checkpoint_format == "big_vision":
                      restored_train_state = polyvit_train_utils.restore_pretrained_big_vision_checkpoint(
                          init_checkpoint_path
                      )
                      # Config dict in big_vision is not the same format as scenic.
                      # Therefore, make sure config match the config of the loaded model!
                      restored_model_cfg = copy.deepcopy(config)
                      # The following is needed when the restored and target models used a
                      # different classifier. As big_vision uses a different config dict, we
                      # have to specify this manually.
  
                  train_state = model.init_from_vit_train_state(
                      train_state,
                      restored_train_state,
                      restored_model_cfg,
                  )
              elif config.init_from.get("init_from_polyvit", False):
                  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_polyvit_train_state(  # pytype: disable=attribute-error
                      train_state,
                      restored_train_state,
                      tokenizer_to_use=config.init_from.get(
                          "tokenizer_to_use"
                      ),
                      tokenizer_to_init=config.init_from.get(
                          "tokenizer_to_init"
                      ),
                      resolution=config.init_from.get("resolution"),
                  )
              elif config.init_from.get("init_from_mbt", False):
                  restored_train_state = (
                      pretrain_utils.restore_pretrained_checkpoint(
                          init_checkpoint_path, None, assert_exist=True
                      )
                  )
                  # Load params from the init_model.
                  train_state = model.init_from_mbt_train_state(  # pytype: disable=attribute-error
                      train_state,
                      restored_train_state,
                      tokenizer_to_init=config.init_from.get(
                          "tokenizer_to_init", "tokenizer_spec"
                      ),
                      resolution=config.init_from.get("resolution"),
                  )
              elif config.init_from.get("init_from_vivit", False):
                  restored_train_state = (
                      pretrain_utils.restore_pretrained_checkpoint(
                          init_checkpoint_path, None, assert_exist=True
                      )
                  )
                  # Load params from the init_model.
                  train_state = model.init_from_vivit_train_state(  # pytype: disable=attribute-error
                      train_state,
                      restored_train_state,
                      tokenizer_to_init=config.init_from.get(
                          "tokenizer_to_init", "tokenizer3d"
                      ),
                      resolution=config.init_from.get("resolution"),
                  )
              else:
                  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.
  
      train_step_pmapped = jax.pmap(
          functools.partial(
              train_step,
              flax_model=model.flax_model,
              loss_fn=model.loss_function,
              lr_fns={
                  name: lr_fn for _, name, (lr_fn, _) in schedule_fns
              },
              metrics_fn=model.get_metrics_fn("train"),
              config=config,
              debug=config.debug_train,
          ),
          axis_name="batch",
          static_broadcasted_argnums=(
              0,
              1,
              2,
          ),  # Task, dataset and modality args.
          # We can donate both buffers of train_state and train_batch.
          donate_argnums=(3, 4),
      )
      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",
          static_broadcasted_argnums=(
              0,
              1,
              2,
          ),  # Task, dataset and modality args.
          # We can donate the eval_batch's buffer.
          donate_argnums=(4,),
      )
      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
          )
  
      log_eval_steps = config.get("log_eval_steps")
      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, dataset: str
      ) -> Dict[str, Any]:
          ds = dataset_dict[dataset]
          valid_iter = ds.valid_iter
          task = ds.meta_data["task"]
          modality = ds.meta_data["modality"]
          num_valid_ex = ds.meta_data["num_eval_examples"]
          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]
              is_one_hot = ds.meta_data["target_is_onehot"]
              # Ceil rounding such that we include the last incomplete batch.
              if config.get("batch_sizes") is not None:
                  batch_size = config.batch_sizes.get(dataset)
              else:
                  batch_size = config.batch_size
              total_eval_steps = int(np.ceil(num_ex / batch_size))
              steps_per_eval = (
                  config.get("steps_per_eval") or total_eval_steps
              )
              eval_metrics = []
              additional_summary = None
              if modality == polyvit_base_model.Modality.AUDIO:
                  eval_logits = []
                  eval_labels = []
                  n_classes = ds.meta_data["num_classes"]
              for _ in range(steps_per_eval):
                  eval_batch = next(val_iter)
                  if is_one_hot:  # 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, logits = eval_step_pmapped(
                      task, dataset, modality, train_state, eval_batch
                  )
                  if modality == polyvit_base_model.Modality.AUDIO:
                      eval_logits.append(
                          jax.device_get(
                              logits.reshape(  # pytype: disable=attribute-error
                                  [-1, n_classes]
                              )
                          )
                      )
                      eval_labels.append(
                          jax.device_get(
                              eval_batch[
                                  "label"
                              ].reshape(  # pytype: disable=attribute-error
                                  [-1, n_classes]
                              )
                          )
                      )
                  eval_metrics.append(
                      train_utils.unreplicate_and_get(e_metrics)
                  )
              if modality == polyvit_base_model.Modality.AUDIO:
                  # Note that this is the Mean AP computed from the examples processed
                  # by a single host.
                  additional_summary = (
                      evaluation_lib.compute_mean_average_precision(
                          np.concatenate(eval_logits, axis=0),
                          np.concatenate(eval_labels, axis=0),
                          return_per_class_ap=n_classes < 10,
                      )
                  )
              eval_summary.update(
                  train_utils.log_eval_summary(
                      step=step,
                      eval_metrics=eval_metrics,
                      extra_eval_summary=additional_summary,
                      writer=writer,
                      prefix=f"{task}/{dataset}/{val_name}",
                  )
              )
          del eval_metrics
          writer.flush()
          return eval_summary
  
      train_metrics = collections.defaultdict(list)
      extra_training_logs = collections.defaultdict(list)
      train_summary, eval_summary = None, None
  
      chrono = train_utils.Chrono()
      logging.info("Starting training loop at step %d.", start_step)
  
      chrono.inform(
          start_step,
          total_steps,
          polyvit_train_utils.get_average_batch_size(config),
          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.update(gflops)
          writer.write_scalars(1, step0_log)
  
      def get_next_train_batch(step):
          dataset = get_dataset_at_step(step)
          ds = dataset_dict[dataset]
          return (
              next(ds.train_iter),
              ds.meta_data["task"],
              dataset,
              ds.meta_data["modality"],
          )
  
      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,
                  train_task,
                  train_ds,
                  train_modality,
              ) = get_next_train_batch(step)
              train_state, t_metrics, t_logs = train_step_pmapped(
                  train_task,
                  train_ds,
                  train_modality,
                  train_state,
                  train_batch,
              )
              # This will accumulate metrics in accelerator 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
              # accelerator and host, which might slow down the training.
              train_metrics[(train_task, train_ds)].append(t_metrics)
              t_logs = jax.tree_util.tree_map(
                  jax_utils.unreplicate, t_logs
              )
              extra_training_logs[(train_task, train_ds)].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 = {}
              for train_task, train_ds in train_metrics.keys():
                  train_summary.update(
                      train_utils.log_train_summary(
                          step=step,
                          train_metrics=jax.tree_util.tree_map(
                              train_utils.unreplicate_and_get,
                              train_metrics[(train_task, train_ds)],
                          ),
                          extra_training_logs=jax.tree_util.tree_map(
                              jax.device_get,
                              extra_training_logs[
                                  (train_task, train_ds)
                              ],
                          ),
                          writer=writer,
                          prefix=f"{train_task}/{train_ds}/train",
                      )
                  )
              # Reset metric accumulation for next evaluation cycle.
              train_metrics = collections.defaultdict(list)
              extra_training_logs = collections.defaultdict(list)
              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"):
                  train_state = (
                      train_utils.sync_model_state_across_replicas(
                          train_state
                      )
                  )
                  eval_summaries = []
                  for ds_name in dataset_dict.keys():
                      eval_summaries.append(
                          evaluate(train_state, step, ds_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"):
                  train_utils.handle_checkpointing(
                      train_state, chrono, workdir
                  )
              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.
  
      # 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 collections
  import copy
  import functools
  from typing import Any, Callable, 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
  from jax.example_libraries import optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.projects.mbt import trainer
  from scenic.projects.polyvit import polyvit_base_model
  from scenic.projects.polyvit import train_utils
  from scenic.projects.vivit import evaluation_lib
  from scenic.train_lib import optax
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  from scenic.train_lib.transfer import fewshot_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, all_datasets, re,  name,  function,  base_lr, eval_summary, next,  ds,  dataset,  ds