code: def load_clip_models(
      enabled: List[str], clip_models: Dict[str, Any] = {}
  ):
      import disco_diffusion_clip_rn50.clip.clip as clip
      from disco_diffusion_clip_rn50.clip.clip import (
          build_model,
          tokenize,
          transform,
      )
  
      # load enabled models
      for k in enabled:
          if k not in clip_models:
              clip_models[k] = build_model(name=k)
              clip_models[k].eval()
              for parameter in clip_models[k].parameters():
                  parameter.stop_gradient = True
  
      # disable not enabled models to save memory
      for k in clip_models:
          if k not in enabled:
              clip_models.pop(k)
  
      return list(clip_models.values())
  
file_path: PaddleHub/modules/image/text_to_image/disco_diffusion_clip_rn50/reverse_diffusion/helper.py
imports: import hashlib
  import logging
  import os
  import subprocess
  import sys
  from os.path import expanduser
  from pathlib import Path
  from typing import Any
  from typing import Dict
  from typing import List
  import paddle
label_desc: Use image node_id 2 for calling a global function with example usage: load_clip_models(enabled, clip_models) and returns: list