code: def convert_to_coco_api(ds):
      coco_ds = COCO()
      # annotation IDs need to start at 1, not 0
      ann_id = 1
      dataset = {"images": [], "categories": [], "annotations": []}
      categories = set()
      for img_idx in range(len(ds)):
          # find better way to get target
          hw, targets = ds.coco_index(img_idx)
          image_id = targets["image_id"].item()
          img_dict = {}
          img_dict["id"] = image_id
          img_dict["height"] = hw[0]
          img_dict["width"] = hw[1]
          dataset["images"].append(img_dict)
          bboxes = targets["boxes"]
          bboxes[:, 2:] -= bboxes[:, :2]
          bboxes = bboxes.tolist()
          labels = targets["labels"].tolist()
          areas = targets["area"].tolist()
          iscrowd = targets["iscrowd"].tolist()
          num_objs = len(bboxes)
          for i in range(num_objs):
              ann = {}
              ann["image_id"] = image_id
              ann["bbox"] = bboxes[i]
              ann["category_id"] = labels[i]
              categories.add(labels[i])
              ann["area"] = areas[i]
              ann["iscrowd"] = iscrowd[i]
              ann["id"] = ann_id
              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 torchvision
  import torch.utils.data
  from pycocotools.coco import COCO
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_coco_api(ds) and returns: coco_ds