code: def main(argv):
      if len(argv) > 1:
          raise app.UsageError("Too many command-line arguments.")
  
      # Make sure tf does not allocate gpu memory.
      tf.config.experimental.set_visible_devices([], "GPU")
  
      batch_size = FLAGS.batch_size
      learning_rate = FLAGS.learning_rate
      num_train_steps = FLAGS.num_train_steps
      eval_freq = FLAGS.eval_frequency
      random_seed = FLAGS.random_seed
  
      if not FLAGS.dev:
          raise app.UsageError("Please provide path to dev set.")
      if not FLAGS.train:
          raise app.UsageError("Please provide path to training set.")
      if batch_size % jax.device_count() > 0:
          raise ValueError(
              "Batch size must be divisible by the number of devices"
          )
      device_batch_size = batch_size // jax.device_count()
  
      if jax.process_index() == 0:
          train_summary_writer = tensorboard.SummaryWriter(
              os.path.join(FLAGS.model_dir, FLAGS.experiment + "_train")
          )
          eval_summary_writer = tensorboard.SummaryWriter(
              os.path.join(FLAGS.model_dir, FLAGS.experiment + "_eval")
          )
  
      # create the training and development dataset
      vocabs = input_pipeline.create_vocabs(FLAGS.train)
      config = models.TransformerConfig(
          vocab_size=len(vocabs["forms"]),
          output_vocab_size=len(vocabs["xpos"]),
          max_len=FLAGS.max_length,
      )
  
      attributes_input = [input_pipeline.CoNLLAttributes.FORM]
      attributes_target = [input_pipeline.CoNLLAttributes.XPOS]
      train_ds = input_pipeline.sentence_dataset_dict(
          FLAGS.train,
          vocabs,
          attributes_input,
          attributes_target,
          batch_size=batch_size,
          bucket_size=config.max_len,
      )
      train_iter = iter(train_ds)
  
      eval_ds = input_pipeline.sentence_dataset_dict(
          FLAGS.dev,
          vocabs,
          attributes_input,
          attributes_target,
          batch_size=batch_size,
          bucket_size=config.max_len,
          repeat=1,
      )
  
      model = models.Transformer(config)
  
      rng = random.key(random_seed)
      rng, init_rng = random.split(rng)
  
      # call a jitted initialization function to get the initial parameter tree
      @jax.jit
      def initialize_variables(init_rng):
          init_batch = jnp.ones((config.max_len, 1), jnp.float32)
          init_variables = model.init(
              init_rng, inputs=init_batch, train=False
          )
          return init_variables
  
      init_variables = initialize_variables(init_rng)
  
      learning_rate_fn = create_learning_rate_scheduler(
          base_learning_rate=learning_rate
      )
  
      optimizer = optax.adamw(
          learning_rate_fn, b1=0.9, b2=0.98, eps=1e-9, weight_decay=1e-1
      )
      state = train_state.TrainState.create(
          apply_fn=model.apply,
          params=init_variables["params"],
          tx=optimizer,
      )
  
      # Replicate optimizer.
      state = jax_utils.replicate(state)
  
      p_train_step = jax.pmap(
          functools.partial(
              train_step, model=model, learning_rate_fn=learning_rate_fn
          ),
          axis_name="batch",
          donate_argnums=(0,),
      )  # pytype: disable=wrong-arg-types
  
      def eval_step(params, batch):
          """Calculate evaluation metrics on a batch."""
          inputs, targets = batch["inputs"], batch["targets"]
          weights = jnp.where(targets > 0, 1.0, 0.0)
          logits = model.apply(
              {"params": params}, inputs=inputs, train=False
          )
          return compute_metrics(logits, targets, weights)
  
      p_eval_step = jax.pmap(eval_step, axis_name="batch")
  
      # We init the first set of dropout PRNG keys, but update it afterwards inside
      # the main pmap'd training update for performance.
      dropout_rngs = random.split(rng, jax.local_device_count())
      metrics_all = []
      tick = time.time()
      best_dev_score = 0
      for step, batch in zip(range(num_train_steps), train_iter):
          batch = common_utils.shard(
              jax.tree_util.tree_map(lambda x: x._numpy(), batch)
          )  # pylint: disable=protected-access
  
          state, metrics = p_train_step(
              state, batch, dropout_rng=dropout_rngs
          )
          metrics_all.append(metrics)
          if (step + 1) % eval_freq == 0:
              metrics_all = common_utils.get_metrics(metrics_all)
              lr = metrics_all.pop("learning_rate").mean()
              metrics_sums = jax.tree_util.tree_map(
                  jnp.sum, metrics_all
              )
              denominator = metrics_sums.pop("denominator")
              summary = jax.tree_util.tree_map(
                  lambda x: x / denominator, metrics_sums
              )  # pylint: disable=cell-var-from-loop
              summary["learning_rate"] = lr
              logging.info(
                  "train in step: %d, loss: %.4f", step, summary["loss"]
              )
              if jax.process_index() == 0:
                  tock = time.time()
                  steps_per_sec = eval_freq / (tock - tick)
                  tick = tock
                  train_summary_writer.scalar(
                      "steps per second", steps_per_sec, step
                  )
                  for key, val in summary.items():
                      train_summary_writer.scalar(key, val, step)
                  train_summary_writer.flush()
  
              metrics_all = (
                  []
              )  # reset metric accumulation for next evaluation cycle.
  
              eval_metrics = []
              eval_iter = iter(eval_ds)
  
              for eval_batch in eval_iter:
                  eval_batch = jax.tree_util.tree_map(
                      lambda x: x._numpy(), eval_batch
                  )  # pylint: disable=protected-access
                  # Handle final odd-sized batch by padding instead of dropping it.
                  cur_pred_batch_size = eval_batch["inputs"].shape[0]
                  if cur_pred_batch_size != batch_size:
                      # pad up to batch size
                      eval_batch = jax.tree_util.tree_map(
                          lambda x: pad_examples(x, batch_size),
                          eval_batch,
                      )
                  eval_batch = common_utils.shard(eval_batch)
  
                  metrics = p_eval_step(state.params, eval_batch)
  
                  eval_metrics.append(metrics)
              eval_metrics = common_utils.get_metrics(eval_metrics)
              eval_metrics_sums = jax.tree_util.tree_map(
                  jnp.sum, eval_metrics
              )
              eval_denominator = eval_metrics_sums.pop("denominator")
              eval_summary = jax.tree_util.tree_map(
                  lambda x: x
                  / eval_denominator,  # pylint: disable=cell-var-from-loop
                  eval_metrics_sums,
              )
  
              logging.info(
                  "eval in step: %d, loss: %.4f, accuracy: %.4f",
                  step,
                  eval_summary["loss"],
                  eval_summary["accuracy"],
              )
  
              if best_dev_score < eval_summary["accuracy"]:
                  best_dev_score = eval_summary["accuracy"]
                  # TODO: save model.
              eval_summary["best_dev_score"] = best_dev_score
              logging.info(
                  "best development model score %.4f", best_dev_score
              )
              if jax.process_index() == 0:
                  for key, val in eval_summary.items():
                      eval_summary_writer.scalar(key, val, step)
                  eval_summary_writer.flush()
  
imports: import functools
  import os
  import time
  from absl import app
  from absl import flags
  from absl import logging
  from flax import jax_utils
  from flax import linen
  from flax.metrics import tensorboard
  from flax.training import common_utils
  from flax.training import train_state
  import jax
  import jax.numpy
  from jax import random
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 7 for calling a global function with example usage: main(argv) and returns: init_variables, compute_metrics