code: def __getitem__(self, item):
      # image = Image.open(self.image_paths[item])
      image = cv2.imread(self.image_paths[item])
      image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
      bboxes = self.bounding_boxes[item]
      image = np.array(image)
      if self.augmentations is not None:
          augmented = self.augmentations(image=image, bboxes=bboxes)
          image = augmented["image"]
          bboxes = augmented["bboxes"]
  
      image = np.transpose(image, (2, 0, 1)).astype(np.float32)
      bboxes = np.array(bboxes)
  
      area = (bboxes[:, 3] - bboxes[:, 1]) * (
          bboxes[:, 2] - bboxes[:, 0]
      )
  
      is_crowd = torch.zeros((bboxes.shape[0],), dtype=torch.int64)
      if self.classes is None:
          labels = torch.ones((bboxes.shape[0],), dtype=torch.int64)
      else:
          labels = torch.tensor(self.classes[item], dtype=torch.int64)
  
      target = {
          "boxes": torch.as_tensor(
              bboxes.tolist(), dtype=torch.float32
          ),
          "area": torch.as_tensor(area.tolist(), dtype=torch.float32),
          "iscrowd": is_crowd,
          "labels": labels,
      }
  
      if self.torchvision_format:
          return torch.tensor(image, dtype=torch.float), target
  
      target["image"] = torch.tensor(image, dtype=torch.float)
      return target
  
file_path: tez/tez/datasets/image_segmentation.py
imports: import cv2
  import numpy
  import torch
  from PIL import ImageFile
label_desc: Use image node_id 3 for calling the RCNNDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: target, torch,  target