code: def __init__(self, num_features, num_classes, bias=True):
      super().__init__()
      self.num_features = num_features
      self.bias = bias
      self.instance_norm = nn.InstanceNorm2d(
          num_features, affine=False, track_running_stats=False
      )
      if bias:
          self.embed = nn.Embedding(num_classes, num_features * 3)
          self.embed.weight.data[:, : 2 * num_features].normal_(
              1, 0.02
          )  # Initialise scale at N(1, 0.02)
          self.embed.weight.data[
              :, 2 * num_features :
          ].zero_()  # Initialise bias at 0
      else:
          self.embed = nn.Embedding(num_classes, 2 * 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 ConditionalInstanceNorm2dPlus object from inherited base classes: nn with example: obj = ConditionalInstanceNorm2dPlus(num_features, num_classes, bias)