code: def train_step(
      train_state: utils.OptaxTrainState,
      batch: Any,
      *,
      flax_model: nn.Module,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[utils.OptaxTrainState, Dict[str, Tuple[float, int]]]:
      """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.
        config: Configuration 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)
  
      # Bind the rng to the host/device we are on for dropout.
      dropout_rng = train_utils.bind_rng_to_host_device(
          rng, axis_name="batch", bind_to="device"
      )
  
      def training_loss_fn(params):
          variables = {"params": params, **train_state.model_state}
          encoded_video, encoded_text = flax_model.apply(
              variables,
              batch["inputs"].get("rgb"),
              batch["text_indices"],
              train=True,
              rngs={"dropout": dropout_rng},
              debug=debug,
          )
          encoded_verbs = None
          if config.get("verb_phrase_loss_weight"):
              # We pass None for video inputs so video encoder doesn't duplicate calc.
              _, encoded_verbs = flax_model.apply(
                  variables,
                  None,
                  batch["verb_indices"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  debug=debug,
              )
          return flax_model.loss_function(
              encoded_video,
              encoded_text,
              batch,
              config,
              encoded_verbs=encoded_verbs,
          )
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=False
      )
      step = train_state.global_step
      train_loss, grad = compute_gradient_fn(train_state.weights)
      new_train_state = train_state
      metrics = {"loss": (train_loss, 1)}
      grad = jax.lax.pmean(grad, axis_name="batch")
      if config.get("max_grad_norm", None):
          grad = jax_optimizers.clip_grads(grad, config.max_grad_norm)
      if train_state.tx is not None:
          updates, new_opt_state = train_state.tx.update(
              grad, train_state.opt_state, train_state.weights
          )
          new_weights = optax.apply_updates(
              train_state.weights, updates
          )
          new_train_state = (
              train_state.replace(  # pytype: disable=attribute-error
                  global_step=step + 1,
                  opt_state=new_opt_state,
                  weights=new_weights,
                  rng=new_rng,
              )
          )
      return new_train_state, metrics
  
imports: import functools
  from typing import Any, 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
  import jax.example_libraries.optimizers
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.projects.verbs_in_action import utils
  from scenic.train_lib import train_utils
label_desc: Use image node_id 2 for calling a global function with example usage: train_step(train_state, batch) and returns: new_train_state,  metrics, flax_model