code: def forward(self, x):
      mean = x.mean(dim=-1, keepdim=True)
      std = x.std(dim=-1, keepdim=True)
      return self.gain * (x - mean) / (std + self.epsilon) + self.bias
  
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 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.forward(x) and returns: unknown