code: def load_and_transform_vision_data(image_paths, device):
      if image_paths is None:
          return None
  
      image_outputs = []
  
      data_transform = transforms.Compose(
          [
              transforms.Resize(
                  224,
                  interpolation=transforms.InterpolationMode.BICUBIC,
              ),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=(0.48145466, 0.4578275, 0.40821073),
                  std=(0.26862954, 0.26130258, 0.27577711),
              ),
          ]
      )
  
      for image_path in image_paths:
          with open(image_path, "rb") as fopen:
              image = Image.open(fopen).convert("RGB")
  
          image = data_transform(image).to(device)
          image_outputs.append(image)
      return torch.stack(image_outputs, dim=0)
  
imports: import logging
  import math
  import torch
  import torch.nn
  import torchaudio
  from PIL import Image
  from pytorchvideo import transforms
  from pytorchvideo.data.clip_sampling import ConstantClipsPerVideoSampler
  from pytorchvideo.data.encoded_video import EncodedVideo
  from torchvision import transforms
  from torchvision.transforms._transforms_video import NormalizeVideo
  from imagebind.models.multimodal_preprocessors import SimpleTokenizer
label_desc: Use image node_id 3 for calling a global function with example usage: load_and_transform_vision_data(image_paths, device) and returns: torch, None