In MLP, there are neurons that form a layer. Each hidden layer gives a vector of number that is the output of that layer.
In CNN, there are kernels that form a convolutional layer. Each layer gives feature maps that are the output of that layer.
In LSTM, there are cells that form a recurrent layer. Each layer gives a sequence that is the output of that layer.
This is my understanding of the basic terminology regarding MLP, CNN, and LSTM.
But consider the following description regarding the number of layers in LSTM in PyTorch
num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results. Default: 1
The description uses the "number of recurrent layers" and the "LSTM" in a similar manner. How I can understand this? Is it costmary to consider a recurrent layer as an LSTM?