code: def forward(self, x):
      means = torch.mean(x, dim=(2, 3))
      m = torch.mean(means, dim=-1, keepdim=True)
      v = torch.var(means, dim=-1, keepdim=True)
      means = (means - m) / (torch.sqrt(v + 1e-5))
      h = self.instance_norm(x)
  
      if self.bias:
          h = h + means[..., None, None] * self.alpha[..., None, None]
          out = self.gamma.view(
              -1, self.num_features, 1, 1
          ) * h + self.beta.view(-1, self.num_features, 1, 1)
      else:
          h = h + means[..., None, None] * self.alpha[..., None, None]
          out = self.gamma.view(-1, self.num_features, 1, 1) * h
      return out
  
imports: import functools
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the InstanceNorm2dPlus obj's underlying member method code with example usage: obj.forward(x) and returns: out