code: def train_step(
      train_state: train_utils.TrainState,
      batch: constants.Batch,
      *,
      flax_model: nn.Module,
      loss_fn: constants.LossFn,
      metrics_fn: constants.MetricFn,
      model_config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[
      train_utils.TrainState,
      Dict[str, Tuple[float, int]],
      Dict[str, 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:
        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.
        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.
        model_config: Config for model architecture.
        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"
      )
  
      def training_loss_fn(params):
          variables = {"params": params, **train_state.model_state}
          output_dict, new_model_state = flax_model.apply(
              variables,
              **batch,
              mutable=["batch_stats"],
              train=True,
              fuse_retrieval=model_config.fuse_retrieval,
              in_batch_neg=model_config.in_batch_neg,
              rngs={"dropout": dropout_rng},
              debug=debug,
          )
          if debug:
              logging.info(
                  "Shape of token_logits in train step is: %s",
                  output_dict["predicted_logits"].shape,
              )
          r = model_config.retrieval_ratio
          output_dict[
              "supervised_retrieval"
          ] = model_config.supervised_retrieval
          loss_dict = loss_fn(output_dict, batch)
          train_loss = (
              loss_dict["gen_loss"] * (1 - r)
              + loss_dict["retr_loss"] * r
          )
          return train_loss, (new_model_state, output_dict, loss_dict)
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
  
      (
          train_loss,
          (new_model_state, output_dict, loss_dict),
      ), grad = compute_gradient_fn(train_state.params)
      grad = jax.lax.pmean(grad, axis_name="batch")
      grad_norm = jax_optimizers.l2_norm(grad).astype(float)
      updates, new_opt_state = train_state.tx.update(
          grad, train_state.opt_state, train_state.params
      )
      update_norm = jax_optimizers.l2_norm(updates).astype(float)
      new_params = optax.apply_updates(train_state.params, updates)
      param_norm = jax_optimizers.l2_norm(train_state.params).astype(
          float
      )
  
      metrics = metrics_fn(output_dict["predicted_logits"], batch)
  
      new_train_state = (
          train_state.replace(  # pytype: disable=attribute-error
              global_step=train_state.global_step + 1,
              opt_state=new_opt_state,
              params=new_params,
              model_state=new_model_state,
              rng=new_rng,
          )
      )
  
      logs = {
          "train/train_loss": train_loss,
          "train/gen_loss": loss_dict["gen_loss"],
          "train/retr_loss": loss_dict["retr_loss"],
          "train/retr_acc": loss_dict["retr_acc"],
          "train/s0": loss_dict["s0"],
          "train/s1": loss_dict["s1"],
          "grad_norm": grad_norm,
          "update_norm": update_norm,
          "param_norm": param_norm,
          "bias": train_state.params["att_transform"]["bias"][0],
          "scale": train_state.params["att_transform"]["scale"][0],
      }
  
      if "retr_scores" in output_dict:
          logs["train/a0"] = output_dict["retr_scores"][0][0]
          logs["train/a1"] = output_dict["retr_scores"][0][1]
  
      return new_train_state, metrics, logs
  
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.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.xm import xm_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.knowledge_visual_language import trainer_utils
  from scenic.projects.knowledge_visual_language.models import constants
  from scenic.projects.knowledge_visual_language.models import losses
  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
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,  logs, train_loss,  new_model_state,  output_dict,  loss_dict