3-3-bilstm-torch comment error

class BiLSTM(nn.Module):
def __init__(self):
super(BiLSTM, self).__init__()

self.lstm = nn.LSTM(input_size=n_class, hidden_size=n_hidden, bidirectional=True)
self.W = nn.Parameter(torch.randn([n_hidden * 2, n_class]).type(dtype))
self.b = nn.Parameter(torch.randn([n_class]).type(dtype))

def forward(self, X):
input = X.transpose(0, 1) # input : [n_step, batch_size, n_class]

hidden_state = Variable(torch.zeros(1*2, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
cell_state = Variable(torch.zeros(1*2, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]

outputs, (_, _) = self.lstm(input, (hidden_state, cell_state))
**outputs = outputs[-1] # [batch_size, n_hidden]**
model = torch.mm(outputs, self.W) + self.b # model : [batch_size, n_class]
return model

error: "outputs = outputs[-1] # [batch_size, n_hidden]"
the shape should be [batch_size,2*n_hidden]

Start in the 3-3-bilstm-torch example at BiLSTM.forward and reproduce the reported shape error. Trace the dimensions returned by self.lstm and verify that the final outputs shape matches the bidirectional model and the subsequent matrix multiplication.

Written by the indexing model from the issue text.