code: def forward(self, x):
      shape = [-1] + [1] * (x.dim() - 1)
      mean = x.view(x.size(0), -1).mean(1).view(*shape)
      std = x.view(x.size(0), -1).std(1).view(*shape)
  
      y = (x - mean) / (std + self.eps)
      if self.affine:
          shape = [1, -1] + [1] * (x.dim() - 2)
          y = self.gamma.view(*shape) * y + self.beta.view(*shape)
      return y
  
imports: import torch
  import torch.nn
  import torchvision.utils
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the LayerNormConv2d obj's underlying member method code with example usage: obj.forward(x) and returns: y