code: def preprocess_images(
      images: List[np.array], feature_extractor: CLIPFeatureExtractor
  ) -> torch.FloatTensor:
      """
      Preprocesses a list of images into a batch of tensors.
  
      Args:
          images (:obj:`List[Image.Image]`):
              A list of images to preprocess.
  
      Returns:
          :obj:`torch.FloatTensor`: A batch of tensors.
      """
      images = [np.array(image) for image in images]
      images = [(image + 1.0) / 2.0 for image in images]
      images = feature_extractor(
          images, return_tensors="pt"
      ).pixel_values
      return images
  
imports: import os
  from typing import List
  import faiss
  import numpy
  import torch
  from datasets import Dataset, load_dataset
  from PIL import Image
  from transformers import CLIPFeatureExtractor, CLIPModel, PretrainedConfig
  from diffusers import logging
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_images(images, feature_extractor) and returns: images