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 __init__(self, n_state, e=1e-5):
      super(LayerNorm, self).__init__()
      self.g = torch.nn.Parameter(torch.ones(n_state))
      self.b = torch.nn.Parameter(torch.zeros(n_state))
      self.e = e
  
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 1 to create a new LayerNorm object from inherited base classes: torch.nn with example: obj = LayerNorm(n_state, e)