code: def forward(
      self, input: Tensor, state: Tuple[Tensor, Tensor]
  ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
      hx, cx = state
      gates = (
          torch.mm(input, self.weight_ih.t())
          + self.bias_ih
          + torch.mm(hx, self.weight_hh.t())
          + self.bias_hh
      )
      ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
  
      ingate = torch.sigmoid(ingate)
      forgetgate = torch.sigmoid(forgetgate)
      cellgate = torch.tanh(cellgate)
      outgate = torch.sigmoid(outgate)
  
      cy = (forgetgate * cx) + (ingate * cellgate)
      hy = outgate * torch.tanh(cy)
  
      return hy, (hy, cy)
  
imports: import numbers
  import warnings
  from collections import namedtuple
  from typing import List, Tuple
  import torch
  import torch.jit
  import torch.nn
  from torch import Tensor
  from torch.nn import Parameter
label_desc: Use image node_id 2 for calling the LSTMCell obj's underlying member method code with example usage: obj.forward(input, state) and returns: hy,  hy,  cy