code: def __init__(self, normalized_shape):
      super().__init__()
      if isinstance(normalized_shape, numbers.Integral):
          normalized_shape = (normalized_shape,)
      normalized_shape = torch.Size(normalized_shape)
  
      # XXX: This is true for our LSTM / NLP use case and helps simplify code
      assert len(normalized_shape) == 1
  
      self.weight = Parameter(torch.ones(normalized_shape))
      self.bias = Parameter(torch.zeros(normalized_shape))
      self.normalized_shape = normalized_shape
  
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 LayerNorm object from inherited base classes: jit with example: obj = LayerNorm(normalized_shape)