LSTM
class torch.nn.quantizable.LSTM(input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, device=None, dtype=None)
A quantizable long short-term memory (LSTM).
For the description and the argument types, please, refer to "LSTM"
Variables:
      layers -- instances of the _LSTMLayer
Note:
 To access the weights and biases, you need to access them per
 layer. See examples below.

Examples:
  >>> import torch.nn.quantizable as nnqa
  >>> rnn = nnqa.LSTM(10, 20, 2)
  >>> input = torch.randn(5, 3, 10)
  >>> h0 = torch.randn(2, 3, 20)
  >>> c0 = torch.randn(2, 3, 20)
  >>> output, (hn, cn) = rnn(input, (h0, c0))
  >>> # To get the weights:
  >>> print(rnn.layers[0].weight_ih)
  tensor([[...]])
  >>> print(rnn.layers[0].weight_hh)
  AssertionError: There is no reverse path in the non-bidirectional layer
