Putting different hidden size for multi layer LSTM

Hi,

I was looking in to a way by which we could put different hidden in a 2 layer LSTM size using standard nn.LSTM

If we see the input arguments for nn.LSTM=(input_size, hidden_size, num_layers)

I see no documentation or could not find anything online where it explains in PyTorch how we could have a different hidden size for layer 1 and layer 2.When I tried around with the code to provide more than one input to input and hidden sizes for multi layer LSTM it doesnt seem to work.
This seems to be pretty straight forward in Keras using the argument units.

Eg: below code explicitly specify that the hidden unit in layer 2 will have size=50

model.add(LSTM(
input_shape=(sequence_length, number_features),
units=100)
model.add(LSTM(
input_shape=(sequence_length, number_features),
units=50)

2.Is there a way to stack LSTMs with anything similar to nn.Sequential().I understand that nn.Sequential does not work for LSTMs?

Thank You
Rahul M