csr_matrix((data, (r_ids, c_ids)), shape=(num_rows, num_columns))

    # compute Naive Bayes log-count ratios
    U.vprint(&#34;computing log-count ratios...&#34;, verbose=verbose)
    nbratios = np.stack([calc_r(i, X, Y).A1 for i in range(num_classes)])
    nbratios = nbratios.T
    embedding_matrix = np.zeros((num_columns, num_classes))
    for i in range(1, num_columns):
        for j in range(num_classes):
            embedding_matrix[i, j] = nbratios[i, j]

    # set up the model
    inp = keras.layers