class_docstr: Turn samples from datasets.CocoDetection into the same format as VOCSegmentation.
  
  This is achieved in two steps:
  
  1. COCO differentiates between 91 categories while VOC only supports 21, including background for both. Fortunately,
     the COCO categories are a superset of the VOC ones and thus can be mapped. Instances of the 70 categories not
     present in VOC are dropped and replaced by background.
  2. COCO only offers detection masks, i.e. a (N, H, W) bool-ish tensor, where the truthy values in each individual
     mask denote the instance. However, a segmentation mask is a (H, W) integer tensor (typically torch.uint8), where
     the value of each pixel denotes the category it belongs to. The detection masks are merged into one segmentation
     mask while pixels that belong to multiple detection masks are marked as invalid.
code: def forward(self, image, target):
      segmentation_mask = (
          self._coco_detection_masks_to_voc_segmentation_mask(target)
      )
      if segmentation_mask is None:
          segmentation_mask = torch.zeros(
              v2.functional.get_size(image), dtype=torch.uint8
          )
  
      return image, tv_tensors.Mask(segmentation_mask)
  
file_path: vision/references/segmentation/v2_extras.py
imports: import torch
  from torchvision import tv_tensors
  from torchvision.transforms import v2
label_desc: Use image node_id 2 for calling the CocoDetectionToVOCSegmentation obj's underlying member method code with example usage: obj.forward(image, target) and returns: image,  tv_tensors