def METHOD_NAME():
    wide_column = []
    deep_column = []
    fm_column = []
    for column_name in FEATURE_COLUMNS:
        if column_name in CATEGORICAL_COLUMNS:
            evict_opt = None
            filter_option = None
            ev_opt = tf.EmbeddingVariableOption(
                evict_option=evict_opt, filter_option=filter_option
            )
            categorical_column = (
                tf.feature_column.categorical_column_with_embedding(
                    column_name, dtype=tf.string, ev_option=ev_opt
                )
            )
            embedding_column = tf.feature_column.embedding_column(
                categorical_column, dimension=16, combiner="mean"
            )
            wide_column.append(embedding_column)
            deep_column.append(embedding_column)
            fm_column.append(embedding_column)
        else:
            column = tf.feature_column.numeric_column(column_name, shape=(1,))
            wide_column.append(column)
            deep_column.append(column)
    return wide_column, fm_column, deep_column