code: def __init__(
      self, input_size, hidden_size, decompose_layernorm=False
  ):
      super().__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.weight_ih = Parameter(
          torch.randn(4 * hidden_size, input_size)
      )
      self.weight_hh = Parameter(
          torch.randn(4 * hidden_size, hidden_size)
      )
      # The layernorms provide learnable biases
  
      if decompose_layernorm:
          ln = LayerNorm
      else:
          ln = nn.LayerNorm
  
      self.layernorm_i = ln(4 * hidden_size)
      self.layernorm_h = ln(4 * hidden_size)
      self.layernorm_c = ln(hidden_size)
  
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 1 to create a new LayerNormLSTMCell object from inherited base classes: jit with example: obj = LayerNormLSTMCell(input_size, hidden_size, decompose_layernorm)