code: def train_step(
      train_state: train_utils.TrainState,
      batch: Batch,
      *,
      representation_fn: Callable[[Batch], jnp.ndarray],
      linear_probe: nn.Module,
      label_smoothing: Optional[float] = None,
      max_grad_norm: Optional[float] = None,
  ) -> Tuple[train_utils.TrainState, Dict[str, Tuple[float, int]]]:
      """The training step.
  
      Args:
        train_state: TrainState, the state of training including the current
          global_step, model_state, rng, and optimizer.
        batch: A single batch of data. Must contain the following keys:
          'representations': A [batch_size, representation_size] ndarray of the
          outputs of the base network, which will be fed into the linear probe.
          'label': A [batch_size] ndarray of integer class labels. 'batch_mask': A
          [batch_size] ndarray where 1.0 indicates a valid sample and 0.0 indicates
          an invalid sample.
        representation_fn: A function that given a batch, returns representations
          that are passed to the linear_probe_fn as input.
        linear_probe: The linear probe module.
        label_smoothing: The label smoothing coefficient.
        max_grad_norm: Maximum gradient norm used for gradient clliping.
  
      Returns:
        The updated train state.
      """
  
      def loss_fn(params):
          variables = {"params": params, **train_state.model_state}
          logits, new_model_state = linear_probe.apply(
              variables,
              representation_fn(batch),
              mutable=["batch_stats"],
              train=True,
          )
          loss = scenic_model_utils.weighted_softmax_cross_entropy(
              logits,
              batch["label"],
              batch["batch_mask"],
              label_smoothing=label_smoothing,
          )
          return loss, (new_model_state, logits)
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (train_cost, (new_model_state, logits)), grad = grad_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")
  
      if max_grad_norm is not None:
          grad = clip_grads(grad, max_grad_norm)
  
      tx = train_state.tx
      if tx is None:
          raise ValueError(
              "train_state.tx, the Gradient Transformation, is None"
          )
  
      updates, new_opt_state = tx.update(
          grad, train_state.opt_state, train_state.params
      )
      new_params = optax.apply_updates(train_state.params, updates)
      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,
          )
      )
      metrics = classification_model.classification_metrics_function(
          logits, batch, target_is_onehot=True
      )
      return new_train_state, metrics
  
imports: import functools
  import logging
  from typing import Any, Callable, Dict, List, Mapping, Optional, Text, Tuple, Type, Union
  from absl import logging
  from clu import metric_writers
  import flax
  from flax import jax_utils
  import flax.linen
  import jax
  from jax.example_libraries.optimizers import clip_grads
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import model_utils
  from scenic.train_lib import classification_trainer
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: train_step(train_state, batch) and returns: new_train_state,  metrics, loss,  new_model_state,  logits