code: def make_detection_masks(
      size=DEFAULT_SIZE, *, num_masks=1, dtype=None, device="cpu"
  ):
      """Make a "detection" mask, i.e. (*, N, H, W), where each object is encoded as one of N boolean masks"""
      return tv_tensors.Mask(
          torch.testing.make_tensor(
              (num_masks, *size),
              low=0,
              high=2,
              dtype=dtype or torch.bool,
              device=device,
          )
      )
  
imports: import contextlib
  import functools
  import itertools
  import os
  import pathlib
  import random
  import re
  import shutil
  import sys
  import tempfile
  import warnings
  from subprocess import CalledProcessError, check_output, STDOUT
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torch.testing
  from PIL import Image
  from torch.testing._comparison import BooleanPair, NonePair, not_close_error_metas, NumberPair, TensorLikePair
  from torchvision import io, tv_tensors
  from torchvision.transforms._functional_tensor import _max_value
  from torchvision.transforms.v2.functional import to_image, to_pil_image
label_desc: Use image node_id 27 for calling a global function with example usage: make_detection_masks(size) and returns: tv_tensors