code: def _make_empty_sample(self, add_masks=False, add_keypoints=False):
      images = [torch.rand((3, 100, 100), dtype=torch.float32)]
      boxes = torch.zeros((0, 4), dtype=torch.float32)
      negative_target = {
          "boxes": boxes,
          "labels": torch.zeros(0, dtype=torch.int64),
          "image_id": 4,
          "area": (boxes[:, 3] - boxes[:, 1])
          * (boxes[:, 2] - boxes[:, 0]),
          "iscrowd": torch.zeros((0,), dtype=torch.int64),
      }
  
      if add_masks:
          negative_target["masks"] = torch.zeros(
              0, 100, 100, dtype=torch.uint8
          )
  
      if add_keypoints:
          negative_target["keypoints"] = torch.zeros(
              17, 0, 3, dtype=torch.float32
          )
  
      targets = [negative_target]
      return images, targets
  
file_path: vision/test/test_models_detection_negative_samples.py
imports: import pytest
  import torch
  import torchvision.models
  from common_utils import assert_equal
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
  from torchvision.models.detection.roi_heads import RoIHeads
  from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
  from torchvision.ops import MultiScaleRoIAlign
label_desc: Use image node_id 1 for calling the TestModelsDetectionNegativeSamples obj's underlying member method code with example usage: obj._make_empty_sample(add_masks, add_keypoints) and returns: images,  targets