code: def forward(
      self, input: Tensor, state: Tuple[Tensor, Tensor]
  ) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
      hx, cx = state
      igates = self.layernorm_i(torch.mm(input, self.weight_ih.t()))
      hgates = self.layernorm_h(torch.mm(hx, self.weight_hh.t()))
      gates = igates + hgates
      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 = self.layernorm_c((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 LayerNormLSTMCell obj's underlying member method code with example usage: obj.forward(input, state) and returns: hy,  hy,  cy