class_docstr: Data augmentation method in `InstaBoost: Boosting Instance
  Segmentation Via Probability Map Guided Copy-Pasting
  <https://arxiv.org/abs/1908.07801>`_.
  
  Refer to https://github.com/GothicAi/Instaboost for implementation details.
code: def _load_anns(self, results):
      labels = results["ann_info"]["labels"]
      masks = results["ann_info"]["masks"]
      bboxes = results["ann_info"]["bboxes"]
      n = len(labels)
  
      anns = []
      for i in range(n):
          label = labels[i]
          bbox = bboxes[i]
          mask = masks[i]
          x1, y1, x2, y2 = bbox
          # assert (x2 - x1) >= 1 and (y2 - y1) >= 1
          bbox = [x1, y1, x2 - x1, y2 - y1]
          anns.append(
              {"category_id": label, "segmentation": mask, "bbox": bbox}
          )
  
      return anns
  
file_path: insightface/detection/scrfd/mmdet/datasets/pipelines/instaboost.py
imports: import numpy
  from ..builder import PIPELINES
label_desc: Use image node_id 2 for calling the InstaBoost obj's underlying member method code with example usage: obj._load_anns(results) and returns: anns