code: def train_and_evaluate(
      config: ml_collections.ConfigDict, workdir: str
  ) -> TrainState:
      """Execute model training and evaluation loop.
  
      Args:
        config: Hyperparameter configuration for training and evaluation.
        workdir: Directory where the tensorboard summaries are written to.
      Returns:
        The final train state that includes the trained parameters.
      """
      # Prepare datasets.
      train_dataset = input_pipeline.TextDataset(
          tfds_name="glue/sst2", split="train"
      )
      eval_dataset = input_pipeline.TextDataset(
          tfds_name="glue/sst2", split="validation"
      )
      train_batches = train_dataset.get_bucketed_batches(
          config.batch_size,
          config.bucket_size,
          max_input_length=config.max_input_length,
          drop_remainder=True,
          shuffle=True,
          shuffle_seed=config.seed,
      )
      eval_batches = eval_dataset.get_batches(
          batch_size=config.batch_size
      )
  
      # Keep track of vocab size in the config so that the embedder knows it.
      config.vocab_size = len(train_dataset.vocab)
  
      # Compile step functions.
      train_step_fn = jax.jit(train_step)
      eval_step_fn = jax.jit(eval_step)
  
      # Create model and a state that contains the parameters.
      rng = jax.random.key(config.seed)
      model = model_from_config(config)
      state = create_train_state(rng, config, model)
  
      summary_writer = tensorboard.SummaryWriter(workdir)
      summary_writer.hparams(dict(config))
  
      # Main training loop.
      logging.info("Starting training...")
      for epoch in range(1, config.num_epochs + 1):
          # Train for one epoch.
          rng, epoch_rng = jax.random.split(rng)
          rngs = {"dropout": epoch_rng}
          state, train_metrics = train_epoch(
              train_step_fn, state, train_batches, epoch, rngs
          )
  
          # Evaluate current model on the validation data.
          eval_metrics = evaluate_model(
              eval_step_fn, state, eval_batches, epoch
          )
  
          # Write metrics to TensorBoard.
          summary_writer.scalar("train_loss", train_metrics.loss, epoch)
          summary_writer.scalar(
              "train_accuracy", train_metrics.accuracy * 100, epoch
          )
          summary_writer.scalar("eval_loss", eval_metrics.loss, epoch)
          summary_writer.scalar(
              "eval_accuracy", eval_metrics.accuracy * 100, epoch
          )
  
      summary_writer.flush()
      return state
  
imports: from typing import Any, Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
  from absl import logging
  from flax import struct
  from flax.metrics import tensorboard
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
  import tensorflow
  import input_pipeline
  import models
label_desc: Use image node_id 12 for calling a global function with example usage: train_and_evaluate(config, workdir) and returns: state