class_docstr: LayerNorm for DRL.
docstr: Construct a layernorm module in the TF style (epsilon inside the square root).
docstr_tok: ["Construct","a","layernorm","module","in","the","TF","style","(","epsilon","inside","the","square","root",")","."]
code: def __init__(self, hidden_size, eps=1e-12):
      """
      Construct a layernorm module in the TF style (epsilon inside the square root).
      """
      super().__init__()
      self.weight = nn.Parameter(torch.ones(hidden_size))
      self.bias = nn.Parameter(torch.zeros(hidden_size))
      self.variance_epsilon = eps
  
imports: import logging
  import torch
  from torch import nn
  import torch.nn.functional
  import math
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(hidden_size, eps)