code: def train_step(
      state: train_state.TrainState,
      batch: Array,
      lstm_rng: PRNGKey,
      eos_id: int,
  ) -> Tuple[train_state.TrainState, Dict[str, jax.Array]]:
      """Trains one step."""
      labels = batch["answer"][:, 1:]
      lstm_key = jax.random.fold_in(lstm_rng, state.step)
  
      def loss_fn(params):
          logits, _ = state.apply_fn(
              {"params": params},
              batch["query"],
              batch["answer"],
              rngs={"lstm": lstm_key},
          )
          loss = cross_entropy_loss(
              logits, labels, get_sequence_lengths(labels, eos_id)
          )
          return loss, logits
  
      grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
      (_, logits), grads = grad_fn(state.params)
      state = state.apply_gradients(grads=grads)
      metrics = compute_metrics(logits, labels, eos_id)
  
      return state, metrics
  
imports: import functools
  from typing import Any, Dict, Tuple
  from absl import app
  from absl import flags
  from absl import logging
  from clu import metric_writers
  from flax import linen
  from flax.training import train_state
  import jax
  import jax.numpy
  import optax
  import models
  from input_pipeline import CharacterTable
  from input_pipeline import get_sequence_lengths
  from input_pipeline import mask_sequences
label_desc: Use image node_id 6 for calling a global function with example usage: train_step(state, batch, lstm_rng, eos_id) and returns: state,  metrics, loss,  logits