code: def main(_):
      # Create the dataset.
      with open(DATASET_PATH.value) as file:
          train_dataset = dataset.load_ascii_dataset(
              corpus=file.read(),
              batch_size=BATCH_SIZE,
              sequence_length=SEQUENCE_LENGTH,
          )
  
      # Initialise the model parameters.
      rng = jax.random.PRNGKey(SEED)
      data = next(train_dataset)
      state = init(rng, data)
  
      # Training loop (note we don't include any explicit eval in this example).
      prev_time = time.time()
      for step in range(MAX_STEPS):
          state, metrics = update(state, data)
          data = next(train_dataset)
          # We use JAX runahead to mask data preprocessing and JAX dispatch overheads.
          # Using values from state/metrics too often will block the runahead and can
          # cause these overheads to become more prominent.
          if step % LOG_EVERY == 0:
              steps_per_sec = LOG_EVERY / (time.time() - prev_time)
              prev_time = time.time()
              metrics |= {"steps_per_sec": steps_per_sec}
              logging.info({k: float(v) for k, v in metrics.items()})
  
imports: from collections.abc import MutableMapping
  import time
  from typing import Any, NamedTuple, Union
  from absl import app
  from absl import flags
  from absl import logging
  import haiku
  from examples.transformer import dataset
  from examples.transformer import model
  import jax
  import jax.numpy
  import numpy
  import optax
label_desc: Use image node_id 6 for calling a global function with example usage: main(_) without return types