4;],
    verbose=1,
):
    embedding_matrix = np.ones((max_features, 1))
    embedding_matrix[0] = 0

    # set up the model
    inp = keras.layers.Input(shape=(maxlen,))
    r = keras.layers.Embedding(
        max_features,
        1,
        input_length=maxlen,
        weights=[embedding_matrix],
        trainable=False,
    )(inp)
    x = keras.layers.Embedding(
        max_features,
        num_classes,
        input_length=maxlen,
        embeddings_initializer=&#34;glorot_normal&#34;,
    )(inp)
    