code: def __init__(self, num_features, num_classes, bias=False):
      super().__init__()
      self.num_features = num_features
      self.bias = bias
      self.embed = nn.Embedding(num_classes, num_features)
      self.embed.weight.data.normal_(1, 0.02)
  
imports: import functools
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new ConditionalVarianceNorm2d object from inherited base classes: nn with example: obj = ConditionalVarianceNorm2d(num_features, num_classes, bias)