code: def train_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      learning_rate_fn: Callable[[int], float],
      loss_fn: LossFn,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ):
      """Runs a single step of training.
  
      Given the state of the training and a batch of data, computes
      the loss and updates the parameters of the model.
  
      Note that in this code, the buffers of the first (train_state) and second
      (batch) arguments are donated to the computation.
  
      Args:
        train_state: The state of training including the current global_step,
          model_state, rng, and optimizer. The buffer of this argument can be
          donated to the computation.
        batch: A single batch of data. The buffer of this argument can be donated to
          the computation.
        flax_model: A Flax model.
        learning_rate_fn: Learning rate scheduler which given the global_step
          generates the learning rate.
        loss_fn: A loss function that given logits, a batch, and parameters of the
          model calculates the loss.
        metrics_fn: A metrics function that given logits and batch of data,
          calculates the metrics as well as the loss.
        config: Configurations of the experiment.
        debug: Whether the debug mode is enabled during training. `debug=True`
          enables model specific logging/storing some values using
          jax.host_callback.
  
      Returns:
        Updated state of training, computed metrics, and learning rate for logging.
      """
      new_rng, rng = jax.random.split(train_state.rng)
  
      if config.get("mixup") and config.mixup.alpha:
          raise ValueError("mixup is not supported yet!")
  
      # Bind the dropout rng to the host/device we are on.
      dropout_rng = train_utils.bind_rng_to_host_device(
          rng, axis_name="batch", bind_to="device"
      )
  
      # Compute the targets for feature regression.
      compute_feature_targets(batch, config)
  
      def training_loss_fn(params, batch, dropout_rng):
          variables = {"params": params, **train_state.model_state}
          (logits, token_mask), new_model_state = flax_model.apply(
              variables,
              batch["inputs"],
              mutable=["batch_stats"],
              train=True,
              rngs={"dropout": dropout_rng},
              debug=debug,
          )
          loss = loss_fn(logits, token_mask, batch, variables["params"])
          return loss, (new_model_state, logits, token_mask)
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
      (
          loss,
          (new_model_state, logits, token_mask),
      ), grad = compute_gradient_fn(
          train_state.optimizer.target, batch, dropout_rng
      )  # pytype: disable=attribute-error
      metrics = metrics_fn(logits, token_mask, batch)
      metrics["total_loss"] = (loss, 1)
  
      for key, token_mask_ in token_mask.items():
          metrics[f"mask_ratio_{key}"] = (jnp.mean(token_mask_), 1)
  
      step = train_state.global_step
      lr = learning_rate_fn(step)
      # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
      grad = jax.lax.pmean(grad, axis_name="batch")
  
      metrics["max_grad_norm_preclip"] = (compute_max_norm(grad), 1)
      if config.get("max_grad_norm", None) is not None:
          grad = clip_grads(grad, config.max_grad_norm)
          metrics["max_grad_norm_postclip"] = (
              compute_max_norm(grad),
              1,
          )
  
      new_optimizer = train_state.optimizer.apply_gradient(
          grad, learning_rate=lr
      )  # pytype: disable=attribute-error
  
      # Explicit weight decay, if necessary.
      if config.get("explicit_weight_decay", None) is not None:
          new_optimizer = new_optimizer.replace(
              target=optimizers.tree_map_with_names(
                  functools.partial(
                      optimizers.decay_weight_fn,
                      lr=lr,
                      decay=config.explicit_weight_decay,
                  ),
                  new_optimizer.target,
                  match_name_fn=lambda name: "kernel" in name,
              )
          )
      new_train_state = (
          train_state.replace(  # pytype: disable=attribute-error
              global_step=step + 1,
              optimizer=new_optimizer,
              model_state=new_model_state,
              rng=new_rng,
          )
      )
      return new_train_state, metrics, lr
  
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 4 for calling a global function with example usage: train_step(train_state, batch) and returns: new_train_state,  metrics,  lr, loss,  new_model_state,  logits,  token_mask