class_docstr: A prior that generates clip image feature based on the text prompt.
  
  :param config: yaml config to define the decoder.
  :param tokenizer: tokenizer used in clip.
  :param clip_mean: mean to normalize the clip image feature (zero-mean, unit variance).
  :param clip_std: std to noramlize the clip image feature (zero-mean, unit variance).
class_docstr_tok: ["A","prior","that","generates","clip","image","feature","based","on","the","text","prompt",".",":","param","config",":","yaml","config","to","define","the","decoder",".",":","param","tokenizer",":","tokenizer","used","in","clip",".",":","param","clip_mean",":","mean","to","normalize","the","clip","image","feature","(","zero-mean",",","unit","variance",")",".",":","param","clip_std",":","std","to","noramlize","the","clip","image","feature","(","zero-mean",",","unit","variance",")","."]
code: def set_cf_text_tensor(self):
      return self._tokenizer.padded_tokens_and_mask(
          [""], self.model.text_ctx
      )
  
file_path: stablediffusion/ldm/modules/karlo/kakao/models/prior_model.py
imports: import copy
  import torch
  from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
  from ldm.modules.karlo.kakao.modules.xf import PriorTransformer
label_desc: Use image node_id 3 for calling the PriorDiffusionModel obj's underlying member method code with example usage: obj.set_cf_text_tensor() and returns: self