code: def __init__(self, features_count, epsilon=1e-6):
      super().__init__()
      self.gain = nn.Parameter(
          torch.ones(features_count), requires_grad=True
      )
      self.bias = nn.Parameter(
          torch.zeros(features_count), requires_grad=True
      )
      self.epsilon = epsilon
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 1 to create a new LayerNormalization object from inherited base classes: nn with example: obj = LayerNormalization(features_count, epsilon)