LSTMCell
class torch.nn.LSTMCell(input_size, hidden_size, bias=True, device=None, dtype=None)
A long short-term memory (LSTM) cell.
  \begin{array}{ll} i = \sigma(W_{ii} x + b_{ii} + W_{hi} h +
  b_{hi}) \\ f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\
  g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\ o =
  \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\ c' = f * c + i
  * g \\ h' = o * \tanh(c') \\ \end{array}

where \sigma is the sigmoid function, and * is the Hadamard
   product.
Parameters:
      * input_size (int) -- The number of expected features in
        the input x
  * **hidden_size** (*int*) -- The number of features in the
    hidden state *h*

  * **bias** (*bool*) -- If "False", then the layer does not use
    bias weights *b_ih* and *b_hh*. Default: "True"

Inputs: input, (h_0, c_0)
      * input of shape (batch, input_size) or (input_size):
        tensor containing input features