code: def validate_and_infer_config(config: AttrDict):
      if config.IMG_RETRIEVAL.DEBUG_MODE:
          # Set data limits for the number of training, query, and database samples.
          if config.IMG_RETRIEVAL.NUM_TRAINING_SAMPLES == -1:
              config.IMG_RETRIEVAL.NUM_TRAINING_SAMPLES = 10
  
          if config.IMG_RETRIEVAL.NUM_QUERY_SAMPLES == -1:
              config.IMG_RETRIEVAL.NUM_QUERY_SAMPLES = 10
  
          if config.IMG_RETRIEVAL.NUM_DATABASE_SAMPLES == -1:
              config.IMG_RETRIEVAL.NUM_DATABASE_SAMPLES = 50
  
      if config.IMG_RETRIEVAL.EVAL_DATASET_NAME in ["OXFORD", "PARIS"]:
          # InstanceRetrievalDataset#score requires the features to be saved.
          config.IMG_RETRIEVAL.SAVE_FEATURES = True
  
      assert (
          config.IMG_RETRIEVAL.SPATIAL_LEVELS > 0
      ), "Spatial levels must be greater than 0."
      if config.IMG_RETRIEVAL.FEATS_PROCESSING_TYPE == "rmac":
          assert (
              config.IMG_RETRIEVAL.TRAIN_PCA_WHITENING
          ), "PCA Whitening is built-in to the RMAC algorithm and is required"
          assert (
              len(config.IMG_RETRIEVAL.IMG_SCALINGS) == 1
          ), "Multiple image scalings is not compatible with the rmac algorithm."
  
      assert config.IMG_RETRIEVAL.SIMILARITY_MEASURE in [
          "cosine_similarity",
          "l2",
      ]
  
      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 14 for calling a global function with example usage: validate_and_infer_config(config) and returns: config