input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=6, strides=1,

This code defines a neural network model with two inputs (`input1` and `input2`) and one output (`output`). The first input (`input1`) is passed through a 1D convolutional layer (`conv1`) with 48 filters, a kernel size of 6, and a ReLU activation function. The output of this layer is then passed through a CuDNNGRU layer with 64 units. The output of the GRU layer is then passed through a multi-head attention layer with 8 heads and a key dimension of 64. The output of the attention layer is then added to the output of the GRU layer and passed through a layer normalization layer. The second input (`input2`) is passed through another 1D convolutional layer (`conv2`) with the same parameters as `conv1`. The output of this layer is then passed through another CuDNNGRU layer with 64 units. Finally, the outputs of the attention layer and the second GRU layer are concatenated together and passed through a dense layer with the same number of units as the number of classes in the training data (`trainY`).