import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs

# Load data on movie ratings.
ratings = tfds.load("movielens/100k-ratings", split="train")
movies = tfds.load("movielens/100k-movies", split="train")

# Build flexible representation models.
user_model = tf.keras.Sequential([...])
movie_model = tf.keras.Sequential([...])

# Define your objectives.
task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(
    movies.batch(128).map(movie_model)
  )
)

# Create a retrieval model.
model = MovielensModel