code: def _create_dummy_results(self, track_id):
      bboxes = np.array([[100, 100, 150, 150]])
      scores = np.array([1.0])
      labels = np.array([0])
      instance_id = np.array([track_id])
      dummy_mask = np.zeros((1, 720, 1280), dtype=np.uint8)
      dummy_mask[:, 100:150, 100:150] = 1
      return dict(
          bboxes=torch.from_numpy(bboxes),
          scores=torch.from_numpy(scores),
          labels=torch.from_numpy(labels),
          instances_id=torch.from_numpy(instance_id),
          masks=torch.from_numpy(dummy_mask),
      )
  
file_path: mmdetection/tests/test_evaluation/test_metrics/test_youtube_vis_metric.py
imports: import os
  import tempfile
  from unittest import TestCase
  import numpy
  import pycocotools.mask
  import torch
  from mmengine.registry import init_default_scope
  from mmengine.structures import BaseDataElement, InstanceData
  from mmdet.registry import METRICS
  from mmdet.structures import DetDataSample, TrackDataSample
label_desc: Use image node_id 4 for calling the TestYouTubeVISMetric obj's underlying member method code with example usage: obj._create_dummy_results(track_id) and returns: dict