code: def train_step(
      dataset: str,  # pylint: disable=unused-argument
      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,  # pylint: disable=unused-argument
  ) -> Tuple[
      train_utils.TrainState, float, Dict[str, Tuple[float, int]], float
  ]:
      """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:
        dataset: dataset name
        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 give the global_step
          generates the learning rate.
        loss_fn: A loss function that given logits and batch of data, calculates the
          training loss.
        metrics_fn: A metrics function that given logits and batch of data,
          calculates the metrics as well as the loss.
        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, loss, 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"
      )
  
      token_loss_coef = config.get("token_loss_coef")
      corrupt_coef = (
          config.dataset_configs.get("corrupt_coef")
          if config.dataset_configs.corrupt
          else 0.0
      )
      return_as_dict = config.dataset_configs.return_as_dict
      modalities = config.dataset_configs.modalities
  
      def training_loss_fn(params):
          variables = {"params": params, **train_state.model_state}
          assert return_as_dict
          encoder_inputs = batch["encoder_inputs"]
  
          # encode video
          if "features" in modalities:
              enc_video, _ = flax_model.apply(
                  variables,
                  {"features": encoder_inputs["features"]},
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  method=flax_model.encode,
              )
          # encode speech
          if "text" in modalities and "text" in encoder_inputs:
              enc_text, _ = flax_model.apply(
                  variables,
                  {"text": encoder_inputs["text"]},
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  method=flax_model.encode,
              )
  
          # concat video and text encodings
          if (
              "features" in modalities
              and "text" in modalities
              and ("text" in encoder_inputs)
          ):
              encoded = jnp.concatenate(
                  [enc_video["encoded"], enc_text["encoded"]], -2
              )
              mask = jnp.concatenate(
                  [enc_video["mask"], enc_text["mask"]], -1
              )
              encoded = {"encoded": encoded, "mask": mask}
          elif "features" in modalities:
              encoded = enc_video
          elif "text" in modalities and "text" in encoder_inputs:
              encoded = enc_text
  
          loss = 0.0
          aux = {}
  
          if token_loss_coef:
              token_encoded = encoded
              token_decoder_inputs = {
                  "encoder_input_tokens": token_encoded["mask"],
                  "decoder_input_tokens": batch["text_indices"][
                      ..., :-1
                  ],
                  "decoder_target_tokens": batch["text_indices"][
                      ..., 1:
                  ],
              }
              token_logits, new_model_state = flax_model.apply(
                  variables,
                  token_encoded["encoded"],
                  token_decoder_inputs,
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  method=flax_model.decode,
              )
  
              loss_token = loss_fn(
                  token_logits,  # pytype: disable=wrong-arg-types  # jax-ndarray
                  {"decoder_inputs": token_decoder_inputs},
              )
              aux["token_logits"] = token_logits
              aux["token_loss"] = loss_token
              loss += loss_token * token_loss_coef
  
          if corrupt_coef:
              corrupt_encoder_inputs = {
                  "text": batch["text_indices_corrupt_inputs"]
              }
  
              # encode text
              out_text, _ = flax_model.apply(
                  variables,
                  corrupt_encoder_inputs,
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  method=flax_model.encode,
              )
              if "features" in modalities:
                  corrupt_encoded = jnp.concatenate(
                      [
                          encoded["encoded"][
                              ..., : batch["features"].shape[1], :
                          ],
                          out_text["encoded"],
                      ],
                      1,
                  )
                  corrupt_mask = jnp.concatenate(
                      [
                          encoded["mask"][
                              ..., : batch["features"].shape[1]
                          ],
                          out_text["mask"],
                      ],
                      1,
                  )
                  corrupt_encoded = {
                      "encoded": corrupt_encoded,
                      "mask": corrupt_mask,
                  }
              else:
                  corrupt_encoded = out_text
  
              corrupt_decoder_inputs = {
                  "encoder_input_tokens": corrupt_encoded["mask"],
                  "decoder_input_tokens": batch[
                      "text_indices_corrupt_outputs"
                  ][..., :-1],
                  "decoder_target_tokens": batch[
                      "text_indices_corrupt_outputs"
                  ][..., 1:],
              }
  
              # decode
              corrupt_logits, new_model_state = flax_model.apply(
                  variables,
                  corrupt_encoded["encoded"],
                  corrupt_decoder_inputs,
                  mutable=["batch_stats"],
                  train=True,
                  rngs={"dropout": dropout_rng},
                  method=flax_model.decode,
              )
              loss_corrupt = loss_fn(
                  corrupt_logits,  # pytype: disable=wrong-arg-types  # jax-ndarray
                  {"decoder_inputs": corrupt_decoder_inputs},
              )
              aux["corrupt_logits"] = corrupt_logits
              aux["corrupt_loss"] = loss_corrupt
              loss += corrupt_coef * loss_corrupt
  
          aux["state"] = new_model_state
  
          return loss, aux
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
      step = train_state.global_step
      lr = learning_rate_fn(step)
      (train_loss, aux), grad = compute_gradient_fn(
          train_state.optimizer.target
      )
      new_model_state = aux["state"]
  
      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)
  
      new_optimizer = train_state.optimizer.apply_gradient(
          grad, learning_rate=lr
      )
  
      metrics = {}
      if token_loss_coef:
          token_logits = aux["token_logits"]
          x = metrics_fn(
              token_logits,
              {
                  "decoder_inputs": {
                      "decoder_input_tokens": batch["text_indices"][
                          ..., :-1
                      ],
                      "decoder_target_tokens": batch["text_indices"][
                          ..., 1:
                      ],
                  },
                  "batch_mask": batch["batch_mask"],
              },
          )
          metrics.update({"token_accuracy": x["token_accuracy"]})
      if corrupt_coef:
          corrupt_logits = aux["corrupt_logits"]
          x = metrics_fn(
              corrupt_logits,
              {
                  "decoder_inputs": {
                      "decoder_input_tokens": batch[
                          "text_indices_corrupt_outputs"
                      ][..., :-1],
                      "decoder_target_tokens": batch[
                          "text_indices_corrupt_outputs"
                      ][..., 1:],
                  },
                  "batch_mask": batch["batch_mask"],
              },
          )
          metrics.update(
              {"corrupt_token_accuracy": x["token_accuracy"]}
          )
  
      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, train_loss, metrics, lr
  
imports: import copy
  import dataclasses
  import functools
  import json
  import os
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from dmvr import tokenizers
  from flax import jax_utils
  from flax.core import unfreeze
  import flax.linen
  import jax
  import jax.example_libraries.optimizers
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.t5 import model
  from scenic.projects.t5 import tokenizer
  from scenic.projects.vid2seq import load_utils
  from scenic.projects.vid2seq import models
  from scenic.projects.vid2seq import train_utils
  from scenic.projects.vid2seq import dvc_eval
  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(dataset, train_state, batch) and returns: new_train_state,  train_loss,  metrics,  lr, loss,  aux