code: def forward(self, x, hidden):
      hx, cx = hidden
      gates = F.linear(x, self.weight_ih, self.bias_ih) + F.linear(
          hx, self.weight_hh, self.bias_hh
      )
  
      ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1)
  
      ingate = F.sigmoid(self._layerNormalization(ingate))
      forgetgate = F.sigmoid(self._layerNormalization(forgetgate))
      cellgate = F.tanh(self._layerNormalization(cellgate))
      outgate = F.sigmoid(self._layerNormalization(outgate))
  
      cy = (forgetgate * cx) + (ingate * cellgate)
  
      hy = outgate * F.tanh(cy)
  
      return hy, cy
  
file_path: translate/pytorch_translate/rnn_cell.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the LayerNormLSTMCellBackend obj's underlying member method code with example usage: obj.forward(x, hidden) and returns: hy,  cy