code: def tokenize(self, texts):
      images = []
      texts_values = []
      image_text_info = []
  
      for idx, data in enumerate(texts):
          if isinstance(data, Image.Image):  # An Image
              images.append(data)
              image_text_info.append(0)
          else:  # A text
              texts_values.append(data)
              image_text_info.append(1)
  
      if len(texts_values) == 0:
          texts_values = None
      if len(images) == 0:
          images = None
  
      inputs = self.processor(
          text=texts_values,
          images=images,
          return_tensors="pt",
          padding=True,
      )
      inputs["image_text_info"] = image_text_info
      return inputs
  
file_path: sentence-transformers/sentence_transformers/models/CLIPModel.py
imports: from torch import nn
  import transformers
  import torch
  from PIL import Image
label_desc: Use image node_id 4 for calling the CLIPModel obj's underlying member method code with example usage: obj.tokenize(texts) and returns: inputs