class_docstr: Construct a layernorm module in the OpenAI style (epsilon inside the square root).
class_docstr_tok: ["Construct","a","layernorm","module","in","the","OpenAI","style","(","epsilon","inside","the","square","root",")","."]
code: def forward(self, x):
      u = x.mean(-1, keepdim=True)
      s = (x - u).pow(2).mean(-1, keepdim=True)
      x = (x - u) / torch.sqrt(s + self.e)
      return self.g * x + self.b
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  import copy
  import io
  import json
  import logging
  import math
  import re
  import tarfile
  import numpy
  import torch
  from torch.nn import Parameter
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.from_params import FromParams
  from io import open
label_desc: Use image node_id 2 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(x) and returns: unknown