code: def __getitem__(self, item):
      targets = self.targets[item]
      if self.backend == "pil":
          image = Image.open(self.image_paths[item])
          image = np.array(image)
          if self.augmentations is not None:
              augmented = self.augmentations(image=image)
              image = augmented["image"]
      elif self.backend == "cv2":
          if self.grayscale is False:
              image = cv2.imread(self.image_paths[item])
              image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
          else:
              image = cv2.imread(
                  self.image_paths[item], cv2.IMREAD_GRAYSCALE
              )
          if self.augmentations is not None:
              augmented = self.augmentations(image=image)
              image = augmented["image"]
      else:
          raise Exception("Backend not implemented")
      if self.channel_first is True and self.grayscale is False:
          image = np.transpose(image, (2, 0, 1)).astype(np.float32)
  
      image_tensor = torch.tensor(image)
      if self.grayscale:
          image_tensor = image_tensor.unsqueeze(0)
      return {
          "image": image_tensor,
          "targets": torch.tensor(targets),
      }
  
file_path: tez/tez/datasets/image_classification.py
imports: import cv2
  import numpy
  import torch
  from PIL import Image, ImageFile
label_desc: Use image node_id 3 for calling the ImageDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: dict