code: def prepare_for_lvis_detection(self, predictions):
      lvis_results = []
      for original_id, prediction in predictions.items():
          if len(prediction) == 0:
              continue
  
          scores = prediction["scores"].tolist()
          labels = prediction["labels"].tolist()
  
          has_mask = "mask" in prediction
          has_bbox = "boxes" in prediction
          has_keypoint = "keypoints" in prediction
  
          if has_bbox:
              boxes = prediction["boxes"]
              boxes = convert_to_xywh(boxes).tolist()
  
          if has_mask:
              masks = prediction["masks"]
              masks = masks > 0.5
              rles = [
                  mask_util.encode(
                      np.array(
                          mask[0, :, :, np.newaxis],
                          dtype=np.uint8,
                          order="F",
                      )
                  )[0]
                  for mask in masks
              ]
              for rle in rles:
                  rle["counts"] = rle["counts"].decode("utf-8")
  
          if has_keypoint:
              keypoints = prediction["keypoints"]
              keypoints = keypoints.flatten(start_dim=1).tolist()
  
          for pred_idx in range(len(labels)):
              lvis_pred = {
                  "image_id": original_id,
                  "category_id": labels[pred_idx],
                  "score": scores[pred_idx],
              }
  
              if has_bbox:
                  lvis_pred["bbox"] = boxes[pred_idx]
  
              if has_mask:
                  lvis_pred["segmentation"] = rles[pred_idx]
  
              if has_keypoint:
                  lvis_pred["keypoints"] = keypoints[pred_idx]
  
              lvis_results.append(lvis_pred)
  
      return lvis_results
  
imports: import copy
  import itertools
  import logging
  from typing import List
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from lvis import LVISEval, LVISResults, LVIS
label_desc: Use image node_id 6 for calling the LvisEvaluator obj's underlying member method code with example usage: obj.prepare_for_lvis_detection(predictions) and returns: lvis_results