def METHOD_NAME(n_embeds):
    # define column types
    plurality_values = ['Single(1)', 'Twins(2)', 'Triplets(3)',
                        'Quadruplets(4)', 'Quintuplets(5)', 'Multiple(2+)']
    is_male = tf.feature_column.categorical_column_with_vocabulary_list(
        'is_male', ['True', 'False', 'Unknown'])
    mother_age = tf.feature_column.numeric_column('mother_age')
    plurality = tf.feature_column.categorical_column_with_vocabulary_list(
        'plurality', plurality_values)
    gestation_weeks = tf.feature_column.numeric_column('gestation_weeks')
    # discretize
    age_buckets = tf.feature_column.bucketized_column(
        mother_age,
        boundaries=np.arange(15, 45, 1).tolist())
    gestation_buckets = tf.feature_column.bucketized_column(
        gestation_weeks,
        boundaries=np.arange(17, 47, 1).tolist())
    # sparse columns are wide
    wide = [is_male, plurality, age_buckets, gestation_buckets]
    # feature cross all the wide columns and embed into a lower dimension
    crossed = tf.feature_column.crossed_column(wide, hash_bucket_size=20000)
    embed = tf.feature_column.embedding_column(crossed, n_embeds)
    # continuous columns are deep
    deep = [mother_age, gestation_weeks, embed]
    return wide, deep