code: def _create_dummy_lvis_json(self, json_name):
      dummy_mask = np.zeros((10, 10), order="F", dtype=np.uint8)
      dummy_mask[:5, :5] = 1
      rle_mask = mask_util.encode(dummy_mask)
      rle_mask["counts"] = rle_mask["counts"].decode("utf-8")
      image = {
          "id": 0,
          "width": 640,
          "height": 640,
          "neg_category_ids": [],
          "not_exhaustive_category_ids": [],
          "coco_url": "http://images.cocodataset.org/val2017/0.jpg",
      }
  
      annotation_1 = {
          "id": 1,
          "image_id": 0,
          "category_id": 1,
          "area": 400,
          "bbox": [50, 60, 20, 20],
          "segmentation": rle_mask,
      }
  
      annotation_2 = {
          "id": 2,
          "image_id": 0,
          "category_id": 1,
          "area": 900,
          "bbox": [100, 120, 30, 30],
          "segmentation": rle_mask,
      }
  
      annotation_3 = {
          "id": 3,
          "image_id": 0,
          "category_id": 2,
          "area": 1600,
          "bbox": [150, 160, 40, 40],
          "segmentation": rle_mask,
      }
  
      annotation_4 = {
          "id": 4,
          "image_id": 0,
          "category_id": 1,
          "area": 10000,
          "bbox": [250, 260, 100, 100],
          "segmentation": rle_mask,
      }
  
      categories = [
          {
              "id": 1,
              "name": "aerosol_can",
              "frequency": "c",
              "image_count": 64,
          },
          {
              "id": 2,
              "name": "air_conditioner",
              "frequency": "f",
              "image_count": 364,
          },
      ]
  
      fake_json = {
          "images": [image],
          "annotations": [
              annotation_1,
              annotation_2,
              annotation_3,
              annotation_4,
          ],
          "categories": categories,
      }
  
      dump(fake_json, json_name)
  
imports: import os.path
  import tempfile
  import unittest
  import numpy
  import pycocotools.mask
  import torch
  from mmdet.evaluation.metrics import LVISMetric
  from mmengine.fileio import dump
label_desc: Use image node_id 1 for calling the TestLVISMetric obj's underlying member method code with example usage: obj._create_dummy_lvis_json(json_name) without return types