code: def __init__(self, num_features, num_classes, bias=True):
      super().__init__()
      self.num_features = num_features
      self.bias = bias
      if bias:
          self.embed = nn.Embedding(num_classes, num_features * 2)
          self.embed.weight.data[
              :, :num_features
          ].uniform_()  # Initialise scale at N(1, 0.02)
          self.embed.weight.data[
              :, num_features:
          ].zero_()  # Initialise bias at 0
      else:
          self.embed = nn.Embedding(num_classes, num_features)
          self.embed.weight.data.uniform_()
  
imports: import functools
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new ConditionalNoneNorm2d object from inherited base classes: nn with example: obj = ConditionalNoneNorm2d(num_features, num_classes, bias)