code: def __getitem__(self, idx):
      image = cv2.imread(self.df.iloc[idx, 0])
      image = np.array(image) / 255.0
      mask = cv2.imread(self.df.iloc[idx, 1], 0)
      mask = np.array(mask) / 255.0
  
      if self.transform is not None:
          aug = self.transform(image=image, mask=mask)
          image = aug["image"]
          mask = aug["mask"]
  
      image = image.transpose((2, 0, 1))
      image = torch.from_numpy(image).type(torch.float32)
      image = tt.Normalize(
          (0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
      )(image)
      mask = np.expand_dims(mask, axis=-1).transpose((2, 0, 1))
      mask = torch.from_numpy(mask).type(torch.float32)
  
      return image, mask
  
file_path: BigDL/python/orca/example/learn/pytorch/brainMRI/dataset.py
imports: import numpy
  import random
  import cv2
  import torch
  import torchvision.transforms
  import torch.utils.data
label_desc: Use image node_id 3 for calling the BrainDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: image,  mask