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",
          "targets",
          "inputs_position",
          "targets_position",
          "inputs_segmentation",
          "targets_segmentation",
      ]
      (
          inputs,
          targets,
          inputs_positions,
          targets_positions,
          inputs_segmentation,
          targets_segmentation,
      ) = (batch.get(k, None) for k in train_keys)
  
      weights = jnp.where(targets > 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.Transformer(config).apply(
              {"params": params},
              inputs,
              targets,
              inputs_positions=inputs_positions,
              targets_positions=targets_positions,
              inputs_segmentation=inputs_segmentation,
              targets_segmentation=targets_segmentation,
              rngs={"dropout": dropout_rng},
          )
  
          loss, weight_sum = compute_weighted_cross_entropy(
              logits, targets, weights, label_smoothing
          )
          mean_loss = loss / weight_sum
          return mean_loss, logits
  
      step = state.step
  
      if state.dynamic_scale:
          # dynamic scale takes care of averaging gradients across replicas
          grad_fn = state.dynamic_scale.value_and_grad(
              loss_fn, has_aux=True, axis_name="batch"
          )
          dynamic_scale, is_fin, (_, logits), grads = grad_fn(
              state.params
          )
          state = state.replace(dynamic_scale=dynamic_scale)
      else:
          grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
          (_, logits), grads = grad_fn(state.params)
          grads = jax.lax.pmean(grads, axis_name="batch")
  
      new_state = state.apply_gradients(grads=grads)
      metrics = compute_metrics(logits, targets, weights)
      metrics["learning_rate"] = learning_rate_fn(step)
  
      if state.dynamic_scale:
          # if is_fin == False the gradients contain Inf/NaNs and optimizer state and
          # params should be restored (= skip this step).
          select_fn = functools.partial(jnp.where, is_fin)
          new_state = new_state.replace(
              opt_state=jax.tree_util.tree_map(
                  select_fn, new_state.opt_state, state.opt_state
              ),
              params=jax.tree_util.tree_map(
                  select_fn, new_state.params, state.params
              ),
          )
          metrics["loss_scale"] = (
              dynamic_scale.scale * metrics["denominator"]
          )
  
      return new_state, metrics
  
imports: import collections
  import functools
  import os
  from absl import logging
  from clu import metric_writers
  from clu import periodic_actions
  from flax import jax_utils
  from flax import linen
  from flax.training import checkpoints
  from flax.training import common_utils
  from flax.training import dynamic_scale
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import bleu
  import decode
  import input_pipeline
  import models
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