1.10.py

def build_model1(lr=0.0, lr_d=0.0, units=0, spatial_dr=0.0, kernel_size1=3, kernel_size2=2, dense_units=128, dr=0.1, conv_size=32):
    file_path = "best_model.hdf5"
    check_point = ModelCheckpoint(file_path, monitor = "val_loss", verbose = 1,
                                  save_best_only = True, mode = "min")
    early_stop = EarlyStopping(monitor = "val_loss", mode = "min", patience = 3)
    
    inp = Input(shape = (max_len,))
    x = Embedding(30001, embed_size, weights = [embedding_matrix], trainable = False)(inp)
    x1 = SpatialDropout1D(spatial_dr)(x)

    x_gru = Bidirectional(CuDNNGRU(units, return_sequences = True))(x1)
    x1 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_gru)
    avg_pool1_gru = GlobalAveragePooling1D()(x1)
    max_pool1_gru = GlobalMaxPooling1D()(x1)
    
    x3 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_gru)
    avg_pool3_gru = GlobalAveragePooling1D()(x3)
    max_pool3_gru = GlobalMaxPooling1D()(x3)
    
    x_lstm = Bidirectional(CuDNNLSTM(units, return_sequences = True))(x1)
    x1 = Conv1D(conv_size, kernel_size=kernel_size1, padding='valid', kernel_initializer='he_uniform')(x_lstm)
    avg_pool1_lstm = GlobalAveragePooling1D()(x1)
    max_pool1_lstm = GlobalMaxPooling1D()(x1)
    
    x3 = Conv1D(conv_size, kernel_size=kernel_size2, padding='valid', kernel_initializer='he_uniform')(x_lstm)
    avg_pool3_lstm = GlobalAveragePooling1D()(x3)
    max_pool3_lstm = GlobalMaxPooling1D()(x3)
    
    
    x = concatenate([avg_pool1_gru, max_pool1_gru, avg_pool3_gru, max_pool3_gru,
                    avg_pool1_lstm, max_pool1_lstm, avg_pool3_lstm, max_pool3_lstm])
    x = BatchNormalization()(x)
    x = Dropout(dr)(Dense(dense_units, activation='relu') (x))
    x = BatchNormalization()(x)
    x = Dropout(dr)(Dense(int(dense_units / 2), activation='relu') (x))
    x = Dense(1, activation = "sigmoid")(x)
    model = Model(inputs = inp, outputs = x)
    model.compile(loss = "binary_crossentropy", optimizer = Adam(lr = lr, decay = lr_d), metrics = ["accuracy"])
    history = model.fit(X_train, y, batch_size = 128, epochs = 10, validation_split=0.1, 
                        verbose = 1, callbacks = [check_point, early_stop])
    model = load_model(file_path)
    return model

can you please write about the defination of embedding_matrix(Line 8)