code: def _make_fake_bbox(
      rectangles_dict: Dict[str, List[Dict[str, Any]]],
      timestamp: float,
      progressible_labels: Optional[Collection[int]] = None,
      required_keys: Optional[List[str]] = None,
  ) -> Dict[str, Any]:
      """Creates a fake bounding box annotation.
  
      Args:
          rectangles_dict: dictionary of annotations.
          timestamp: timestamp for make bounding box.
          progressive_labels: whether to add progressive labels or not.
          required_keys: A list of keywords that specifies which keywords need to be
              included in a new bounding_box in addition to the class_label_name. Defaults
              to None.
  
      Returns:
          bounding box annotation at @timestamp that is not visible with all other values
          set to -1.
      """
      keys = rectangles_dict[list(rectangles_dict.keys())[0]][0].keys()
      res = {key: -1 for key in keys}
      res["is_visible"] = False
      res["timestamp"] = timestamp
      if required_keys is not None:
          for key in required_keys:
              res[key] = -1
      if (
          progressible_labels is not None
          and len(progressible_labels) > 0
      ):
          res["progress"] = -1.0
      return res
  
file_path: ml-cvnets/data/datasets/utils/video.py
imports: import functools
  from typing import Any, Collection, Dict, List, Optional, Tuple
  import numpy
  from utils import logger
label_desc: Use image node_id 4 for calling a global function with example usage: _make_fake_bbox(rectangles_dict, timestamp, progressible_labels, required_keys) and returns: res