code: def main(
      image_dir: str,
      dataset_json_path: str,
      train_split: str = 0.9,
      project: str = "runs/coco2yolov5",
      name: str = "exp",
      seed: str = 1,
      disable_symlink=False,
  ):
      """
      Args:
          images_dir (str): directory for coco images
          dataset_json_path (str): file path for the coco json file to be converted
          train_split (str): set the training split ratio
          project (str): save results to project/name
          name (str): save results to project/name"
          seed (int): fix the seed for reproducibility
          disable_symlink (bool): required in google colab env
      """
  
      # increment run
      save_dir = Path(
          increment_path(Path(project) / name, exist_ok=False)
      )
      # load coco dict
      coco = Coco.from_coco_dict_or_path(
          coco_dict_or_path=dataset_json_path,
          image_dir=image_dir,
      )
      # export as yolov5
      coco.export_as_yolov5(
          output_dir=str(save_dir),
          train_split_rate=train_split,
          numpy_seed=seed,
          disable_symlink=disable_symlink,
      )
  
      print(
          f"COCO to YOLOv5 conversion results are successfully exported to {save_dir}"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: main(image_dir, dataset_json_path, train_split, project, name, seed, disable_symlink) without return types