I intend to implement an LSTM in Pytorch with multiple memory cell blocks  or multiple LSTM units, an LSTM unit being the set of a memory block and its gates  per layer, but it seems that the base class torch.nn.LSTM
enables only to implement a multilayer LSTM with one LSTM unit per layer:
lstm = torch.nn.LSTM(input_size, hidden_size, num_layers)
where (from the Pytorch’s documentation):

input_size
is the input dimension of the network, 
hidden_size
is the hidden state dimension for every layer (i.e. the dimension of every layer), 
num_layer
is the number of layers of the network
Thereupon, from above, each LSTM unit has exactly one cell (the cell state for each LSTM unit is thus a scalar) because for each layer the dimension of the cell state corresponds to the dimension of the hidden state (i.e. hidden_size
).
However in the original LSTM model proposed by Hochreiter and Schmidhuber
[1997], each LSTM block/unit can contains several cells:
LSTM Network [Hochreiter, 1997]
Is there a way to do so?