code: def train_and_evaluate(
      config: ConfigDict = _DefaultConfig, workdir: str = "."
  ) -> train_state.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 train state (which includes the `.params`).
      """
      train_ds, test_ds = get_datasets()
      rng = jax.random.PRNGKey(0)
      summary_writer: tensorboard.SummaryWriter | None = None
  
      if config.enable_tensorboard:
          summary_writer = tensorboard.SummaryWriter(workdir)
          summary_writer.hparams(config.to_dict())
  
      rng, init_rng = jax.random.split(rng)
      state = create_train_state(init_rng, config)
  
      for epoch in range(1, config.num_epochs + 1):
          if not config.hermetic:
              rng, input_rng = jax.random.split(rng)
          state, train_loss, train_accuracy = train_epoch(
              state, train_ds, config.batch_size, input_rng
          )
          _, test_loss, test_accuracy = apply_model(
              state, test_ds["image"], test_ds["label"]
          )
  
          print(
              "epoch:% 3d, train_loss: %.4f, train_accuracy: %.2f, test_loss: %.4f, test_accuracy: %.2f"
              % (
                  epoch,
                  train_loss,
                  train_accuracy * 100,
                  test_loss,
                  test_accuracy * 100,
              )
          )
  
          if config.enable_tensorboard:
              assert summary_writer is not None
              summary_writer.scalar("train_loss", train_loss, epoch)
              summary_writer.scalar(
                  "train_accuracy", train_accuracy, epoch
              )
              summary_writer.scalar("test_loss", test_loss, epoch)
              summary_writer.scalar(
                  "test_accuracy", test_accuracy, epoch
              )
  
      if config.enable_tensorboard:
          assert summary_writer is not None
          summary_writer.flush()
  
      return state
  
imports: from __future__ import annotations
  import argparse
  import os
  import typing
  from typing import TYPE_CHECKING
  import attrs
  import cattrs
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow_datasets
  from flax import linen
  from flax import serialization
  from flax.metrics import tensorboard
  from flax.training import train_state
  import bentoml
  from bentoml._internal.utils.pkg import pkg_version_info
label_desc: Use image node_id 6 for calling a global function with example usage: train_and_evaluate(config, workdir) and returns: state