code: def __init__(
      self,
      normalized_shape: int,
      eps: float = 1e-5,
      elementwise_affine: bool = True,
  ):
      super(LayerNormalization, self).__init__()
      self._normalized_shape = tuple(normalized_shape)
      self._eps = eps
      self._elementwise_affine = elementwise_affine
      if self._elementwise_affine:
          self._weight = nn.Parameter(torch.Tensor(*normalized_shape))
          self._bias = nn.Parameter(torch.Tensor(*normalized_shape))
      else:
          self.register_parameter("_weight", None)
          self.register_parameter("_bias", None)
      self._reset_parameters()
  
imports: from __future__ import division
  import numbers
  from typing import Optional
  import torch
  import torch.nn
  from torch.nn import init
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new LayerNormalization object from inherited base classes: nn with example: obj = LayerNormalization(normalized_shape, eps, elementwise_affine)