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).
code: def load_from_checkpoint(
      cls,
      config,
      tokenizer,
      clip_mean,
      clip_std,
      ckpt_path,
      strict: bool = True,
  ):
      ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
  
      model = cls(config, tokenizer, clip_mean, clip_std)
      model.load_state_dict(ckpt, strict=strict)
      return model
  
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 2 for calling the PriorDiffusionModel obj's underlying member method code with example usage: obj.load_from_checkpoint(cls, config, tokenizer, clip_mean, clip_std, ckpt_path, strict) and returns: model