code: def forward(self, x, y):
      if self.bias:
          gamma, beta = self.embed(y).chunk(2, dim=-1)
          out = gamma.view(-1, self.num_features, 1, 1) * x + beta.view(
              -1, self.num_features, 1, 1
          )
      else:
          gamma = self.embed(y)
          out = gamma.view(-1, self.num_features, 1, 1) * x
      return out
  
imports: import functools
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the ConditionalNoneNorm2d obj's underlying member method code with example usage: obj.forward(x, y) and returns: out