code: def get_unconditional_conditioning(self, bs, device="cuda"):
      uc_class = (
          self.n_classes - 1
      )  # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
      uc = torch.ones((bs,), device=device) * uc_class
      uc = {self.key: uc}
      return uc
  
imports: import torch
  import torch.nn
  import kornia
  from torch.utils.checkpoint import checkpoint
  from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
  import open_clip
  from ldm.util import default, count_params, autocast
  from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
  from ldm.modules.diffusionmodules.openaimodel import Timestep
label_desc: Use image node_id 3 for calling the ClassEmbedder obj's underlying member method code with example usage: obj.get_unconditional_conditioning(bs, device) and returns: uc