code: def __getitem__(self, idx):
      # load images and masks
      img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
      mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
  
      img = read_image(img_path)
      mask = read_image(mask_path)
  
      img = F.convert_image_dtype(img, dtype=torch.float)
      mask = F.convert_image_dtype(mask, dtype=torch.float)
  
      # We get the unique colors, as these would be the object ids.
      obj_ids = torch.unique(mask)
  
      # first id is the background, so remove it.
      obj_ids = obj_ids[1:]
  
      # split the color-encoded mask into a set of boolean masks.
      masks = mask == obj_ids[:, None, None]
  
      boxes = masks_to_boxes(masks)
  
      # there is only one class
      labels = torch.ones((masks.shape[0],), dtype=torch.int64)
  
      target = {}
      target["boxes"] = boxes
      target["labels"] = labels
  
      if self.transforms is not None:
          img, target = self.transforms(img, target)
  
      return img, target
  
file_path: vision/gallery/others/plot_repurposing_annotations.py
imports: import os
  import numpy
  import torch
  import matplotlib.pyplot
  import torchvision.transforms.functional
  from torchvision.io import read_image
  from torchvision.utils import draw_segmentation_masks
  from torchvision.ops import masks_to_boxes
  from torchvision.utils import draw_bounding_boxes
  from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights
label_desc: Use image node_id 2 for calling the SegmentationToDetectionDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  target