How to implement LSTM layer with multiple cells per memory block in Pytorch?

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 multi-layer 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?