code: def train_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      flax_model: nn.Module,
      loss_fn: LossFn,
      lr_fns: LrFns,
      metrics_fn: MetricFn,
      config: ml_collections.ConfigDict,
      debug: Optional[bool] = False,
  ) -> Tuple[
      train_utils.TrainState,
      Dict[str, Tuple[float, int]],
      Dict[str, Any],
  ]:
      """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, params, 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, a batch, and parameters of the
          model calculates the loss.
        lr_fns: The learning rate fns used for the optimizer in train_state.
        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 and computed metrics and some training logs.
      """
      training_logs = {}
      new_rng, rng = jax.random.split(train_state.rng)
  
      if config.get("mixup") and config.mixup.alpha:
          mixup_rng, rng = jax.random.split(rng, 2)
          mixup_rng = train_utils.bind_rng_to_host_device(
              mixup_rng,
              axis_name="batch",
              bind_to=config.mixup.get("bind_to", "device"),
          )
          batch = dataset_utils.mixup(
              batch,
              config.mixup.alpha,
              config.mixup.get("image_format", "NHWC"),
              rng=mixup_rng,
          )
  
      # Bind the rng to the host/device we are on.
      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}
          logits, new_model_state = flax_model.apply(
              variables,
              batch["inputs"],
              mutable=["batch_stats"],
              train=True,
              rngs={"dropout": dropout_rng},
              debug=debug,
          )
          loss = loss_fn(logits, batch, variables["params"])
          return loss, (new_model_state, logits)
  
      compute_gradient_fn = jax.value_and_grad(
          training_loss_fn, has_aux=True
      )
      (
          train_cost,
          (new_model_state, logits),
      ), grad = compute_gradient_fn(train_state.params)
  
      del train_cost
      # Re-use same axis_name as in the call to `pmap(...train_step...)` below.
      grad = jax.lax.pmean(grad, axis_name="batch")
  
      updates, new_opt_state = train_state.tx.update(
          grad, train_state.opt_state, train_state.params
      )
      new_params = optax.apply_updates(train_state.params, updates)
  
      training_logs["l2_grads"] = jnp.sqrt(
          sum([jnp.vdot(g, g) for g in jax.tree_util.tree_leaves(grad)])
      )
      ps = jax.tree_util.tree_leaves(new_params)
      training_logs["l2_params"] = jnp.sqrt(
          sum([jnp.vdot(p, p) for p in ps])
      )
      us = jax.tree_util.tree_leaves(updates)
      training_logs["l2_updates"] = jnp.sqrt(
          sum([jnp.vdot(u, u) for u in us])
      )
      for name, lr_fn in lr_fns.items():
          lr_name = (
              "learning_rate"
              if name == "all"
              else f"learning_rate_{name}"
          )
          training_logs[lr_name] = lr_fn(train_state.global_step)
  
      metrics = metrics_fn(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,
          )
      )
  
      return new_train_state, metrics, training_logs
  
imports: import collections
  import functools
  from typing import Any, Callable, Dict, Iterator, Tuple, Optional, Type
  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
  from flax.training import checkpoints
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  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
  from tensorflow.io import gfile
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,  training_logs, loss,  new_model_state,  logits