code: def train_step(
      state,
      batch,
      config,
      learning_rate_fn,
      label_smoothing=0.0,
      dropout_rng=None,
  ):
      """Perform a single training step."""
      # X_position and X_segmentation are needed only when using "packed examples"
      # where multiple sequences are packed into the same example with this
      # metadata.
      # if such features are not present they are ignored and the example is treated
      # like a normal, unpacked sequence example.
      train_keys = ["inputs", "inputs_position", "inputs_segmentation"]
      (inputs, inputs_positions, inputs_segmentation) = (
          batch.get(k, None) for k in train_keys
      )
  
      weights = jnp.where(inputs > 0, 1, 0).astype(jnp.float32)
  
      dropout_rng = jax.random.fold_in(dropout_rng, state.step)
  
      def loss_fn(params):
          """loss function used for training."""
          logits = models.TransformerLM(config).apply(
              {"params": params},
              inputs,
              inputs_positions=inputs_positions,
              inputs_segmentation=inputs_segmentation,
              rngs={"dropout": dropout_rng},
          )
  
          loss, weight_sum = compute_weighted_cross_entropy(
              logits, inputs, weights, label_smoothing
          )
          mean_loss = loss / weight_sum
          return mean_loss, logits
  
      step = state.step
      lr = learning_rate_fn(step)
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (_, logits), grads = grad_fn(state.params)
      new_state = state.apply_gradients(grads=grads)
      metrics = compute_metrics(logits, inputs, weights)
      metrics["learning_rate"] = lr
  
      return new_state, metrics
  
imports: import collections
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  import jax
  from jax import random
  import jax.numpy
  from jax.sharding import PartitionSpec, Mesh, NamedSharding
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
  import temperature_sampler
  import utils
label_desc: Use image node_id 6 for calling a global function with example usage: train_step(state, batch, config, learning_rate_fn, label_smoothing, dropout_rng) and returns: new_state,  metrics, mean_loss,  logits