code: def train_and_evaluate(workdir: str) -> train_state.TrainState:
      """Trains for a fixed number of steps and decode during training."""
  
      # TODO(marcvanzee): Integrate ctable with train_state.
      ctable = CTable("0123456789+= ", FLAGS.max_len_query_digit)
      rng = jax.random.key(0)
      state = get_train_state(rng, ctable)
  
      writer = metric_writers.create_default_writer(workdir)
      for step in range(FLAGS.num_train_steps):
          batch = ctable.get_batch(FLAGS.batch_size)
          state, metrics = train_step(state, batch, rng, ctable.eos_id)
          if step and step % FLAGS.decode_frequency == 0:
              writer.write_scalars(step, metrics)
              batch = ctable.get_batch(5)
              decode_batch(state, batch, rng, ctable)
  
      return state
  
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 10 for calling a global function with example usage: train_and_evaluate(workdir) and returns: state