code: def adapt_query_extract_config(config, checkpoint_folder):
      config.DATA.TRAIN.DATA_SOURCES = []
      config.DATA.TRAIN.DATA_PATHS = []
      config.DATA.TRAIN.DATASET_NAMES = []
      config.DATA.TRAIN.DATA_LIMIT = 0
  
      config.DATA.TEST.DATA_SOURCES = ["disk_filelist"]
      config.DATA.TEST.DATA_PATHS = [
          f"{config.IMG_RETRIEVAL.DATASET_PATH}/{config.IMG_RETRIEVAL.EVAL_DATASET_NAME}/query_images.npy"  # NOQA
      ]
  
      output_dir = os.path.join(checkpoint_folder, "query")
      g_pathmgr.mkdirs(output_dir)
      config.EXTRACT_FEATURES.OUTPUT_DIR = output_dir
  
      if config.IMG_RETRIEVAL.DEBUG_MODE:
          config.DATA.TEST.DATA_LIMIT = 10
  
      # Images are all of different sizes.
      config.DATA.TEST.BATCHSIZE_PER_REPLICA = 1
  
      config.DATA.TEST.TRANSFORMS = get_extract_features_transforms(
          config
      )
  
      return config
  
imports: import logging
  import os
  import sys
  from argparse import Namespace
  from typing import Any, List
  import numpy
  import torch
  import torchvision
  from classy_vision.generic.util import copy_model_to_gpu, load_checkpoint
  from iopath.common.file_io import g_pathmgr
  from vissl.config import AttrDict
  from vissl.hooks import default_hook_generator
  from vissl.models import build_model
  from vissl.utils.checkpoint import get_checkpoint_folder, init_model_from_consolidated_weights
  from vissl.utils.distributed_launcher import launch_distributed
  from vissl.utils.env import set_env_vars, setup_path_manager
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict, print_cfg
  from vissl.utils.instance_retrieval_utils.data_util import CopyDaysDataset, GenericInstanceRetrievalDataset, get_average_gem, InstanceRetrievalDataset, InstanceRetrievalImageLoader, InstreDataset, is_copdays_dataset, is_instre_dataset, is_oxford_paris_dataset, is_revisited_dataset, is_whiten_dataset, l2n, MultigrainResize, RevisitedInstanceRetrievalDataset, WhiteningTrainingImageDataset
  from vissl.utils.instance_retrieval_utils.rmac import get_rmac_descriptors
  from vissl.utils.io import load_file, makedir, save_file
  from vissl.utils.logger import setup_logging, shutdown_logging
  from vissl.utils.pca import load_pca, train_and_save_pca
  from vissl.utils.perf_stats import PerfStats, PerfTimer
label_desc: Use image node_id 17 for calling a global function with example usage: adapt_query_extract_config(config, checkpoint_folder) and returns: config