上述代码出现错误：2023-04-26 16:39:25.331107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localh

The error message indicates that there is an index out of range error in the `MultiHeadAttention` layer. This may be caused by the `num_heads` or `key_dim` parameters being set incorrectly. I would suggest checking the values of these parameters to make sure they are compatible with the input shapes. For example, if `num_heads` is too large relative to the input shape, it could cause an index out of range error. Here's an updated version of the code with some modifications that might help: ``` def LSTNet(trainX1, trainX2, trainY, config): input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu')(input1) gru1 = CuDNNGRU(64)(conv1) attention1 = MultiHeadAttention(num_heads=4, key_dim=16)(gru1, gru1) attention1 = LayerNormalization()(attention1 + gru1) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu')(input2) gru2 = CuDNNGRU(64)(conv2) output = concatenate([attention1, gru2], axis=2) output = Dense(trainY.shape[1])(output) # Highway network highway_window = config.highway_window z = Lambda(lambda k: k[:, -highway_window:, :])(input1) z = Lambda(lambda k: K.permute_dimensions(k, (0, 2, 1)))(z) z = Lambda(lambda k: K.reshape(k, (-1, highway_window * trainX1.shape[2])))(z) z = Dense(trainY.shape[1])(z) output = add([output, z]) output = Activation('sigmoid')(output) model = Model(inputs=[input1, input2], outputs=output) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model ``` In this version of the code, I've reduced the `num_heads` parameter to 4 and the `key_dim` parameter to 16. You may