code: def convert_to_coco_api(self):
      coco_ds = COCO()
      # annotation IDs need to start at 1, not 0, see torchvision issue #1530
      ann_id = 1
      dataset = {"images": [], "categories": [], "annotations": []}
      categories = set()
      for img_idx in range(len(self)):
          targets, h, w = self.get_annotations(img_idx)
          img_id = targets["image_id"].item()
          img_dict = {"id": img_id, "height": h, "width": w}
          dataset["images"].append(img_dict)
          bboxes = targets["boxes"].clone()
          # convert (x_min, ymin, xmax, ymax) to (xmin, ymin, w, h)
          bboxes[:, 2:] -= bboxes[:, :2]
          bboxes = bboxes.tolist()
          labels = targets["labels"].tolist()
          areas = targets["area"].tolist()
          iscrowd = targets["iscrowd"].tolist()
          if "masks" in targets:
              masks = targets["masks"]
              # make masks Fortran contiguous for coco_mask
              masks = (
                  masks.permute(0, 2, 1).contiguous().permute(0, 2, 1)
              )
          num_objs = len(bboxes)
          for i in range(num_objs):
              ann = {
                  "image_id": img_id,
                  "bbox": bboxes[i],
                  "category_id": labels[i],
                  "area": areas[i],
                  "iscrowd": iscrowd[i],
                  "id": ann_id,
              }
              categories.add(labels[i])
              if "masks" in targets:
                  ann["segmentation"] = coco_mask.encode(
                      masks[i].numpy()
                  )
              dataset["annotations"].append(ann)
              ann_id += 1
      dataset["categories"] = [{"id": i} for i in sorted(categories)]
      coco_ds.dataset = dataset
      coco_ds.createIndex()
      return coco_ds
  
imports: import torch
  import torch.utils.data
  from pycocotools import mask
  from pycocotools.coco import COCO
label_desc: Use image node_id 3 for calling a global function with example usage: convert_to_coco_api() and returns: coco_ds