code: def standalone_train_and_eval():
      """Train and evaluate ranking model."""
      train_dataset, eval_dataset = datasets()
  
      # Create optimizer, ranking loss and ranking metrics.
      optimizer = tf.keras.optimizers.Adagrad(
          learning_rate=FLAGS.learning_rate
      )
      loss = tfr.keras.losses.get(loss=FLAGS.loss, ragged=True)
      eval_metrics = tfr.keras.metrics.default_keras_metrics(
          ragged=True
      )
  
      # Create ranking model to train. This model operates on ragged tensors and
      # returns model scores as ragged tensors.
      model = AntiqueEmbeddingRankingModel(
          vocab_size=FLAGS.vocab_size,
          vocab_file_path=FLAGS.vocab_file_path,
          embedding_dim=FLAGS.embedding_dimension,
          hidden_dims=FLAGS.hidden_layer_dims,
      )
      model.compile(
          optimizer=optimizer, loss=loss, metrics=eval_metrics
      )
  
      # Train ranker.
      logging.info("Training the model...")
      tensorboard_callback = tf.keras.callbacks.TensorBoard(
          FLAGS.model_dir
      )
      model.fit(
          train_dataset,
          epochs=FLAGS.num_epochs,
          steps_per_epoch=FLAGS.num_train_steps // FLAGS.num_epochs,
          validation_data=eval_dataset,
          validation_steps=FLAGS.num_valid_steps,
          callbacks=[tensorboard_callback],
      )
      logging.info("Finished training the model.")
  
      # Save model.
      saved_model_path = os.path.join(FLAGS.model_dir, "export/")
      logging.info("Exporting to SavedModel...")
      model.save(filepath=saved_model_path)
      logging.info(
          "SavedModel exported successfully to: %s", saved_model_path
      )
  
imports: import os
  from typing import List
  from absl import app
  from absl import flags
  from absl import logging
  import tensorflow
  import tensorflow_ranking
label_desc: Use image node_id 3 for calling a global function with example usage: standalone_train_and_eval() without return types