code: def __getitem__(self, idx):
      img = Image.open(self.img_list[idx]).convert("RGB")
      manual = Image.open(self.manual[idx]).convert("L")
      manual = np.array(manual) / 255
      roi_mask = Image.open(self.roi_mask[idx]).convert("L")
      roi_mask = 255 - np.array(roi_mask)
      mask = np.clip(manual + roi_mask, a_min=0, a_max=255)
  
      # 这里转回PIL的原因是，transforms中是对PIL数据进行处理
      mask = Image.fromarray(mask)
  
      if self.transforms is not None:
          img, mask = self.transforms(img, mask)
  
      return img, mask
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/unet/my_dataset.py
imports: import os
  from PIL import Image
  import numpy
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the DriveDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  mask