code: def __init__(self, input_size, hidden_size, forget_gate_bias):
      super().__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.weight_ih = torch.nn.Parameter(
          torch.randn(4 * hidden_size, input_size)
      )
      self.weight_hh = torch.nn.Parameter(
          torch.randn(4 * hidden_size, hidden_size)
      )
  
      # LayerNorm provide learnable biases
      self.layernorm_i = torch.nn.LayerNorm(4 * hidden_size)
      self.layernorm_h = torch.nn.LayerNorm(4 * hidden_size)
      self.layernorm_c = torch.nn.LayerNorm(hidden_size)
  
      self.reset_parameters()
  
      self.layernorm_i.bias.data[hidden_size : 2 * hidden_size].fill_(
          0.0
      )
      self.layernorm_h.bias.data[hidden_size : 2 * hidden_size].fill_(
          forget_gate_bias
      )
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new LayerNormLSTMCell object from inherited base classes: torch.nn with example: obj = LayerNormLSTMCell(input_size, hidden_size, forget_gate_bias)