code: def forward(self, x):
      if self.training and self.sigma != 0:
          self.noise = self.noise.to(x.device)
          scale = (
              self.sigma * x.detach()
              if self.is_relative_detach
              else self.sigma * x
          )
          sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
          x = x + sampled_noise
      return x
  
imports: from collections import OrderedDict
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GaussianNoise obj's underlying member method code with example usage: obj.forward(x) and returns: x