code: def test(cfg):
      """
      Perform multi-view testing on the pretrained video model.
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
      """
      # Set up environment.
      du.init_distributed_training(cfg)
      # Set random seed from configs.
      np.random.seed(cfg.RNG_SEED)
      torch.manual_seed(cfg.RNG_SEED)
  
      # Setup logging format.
      logging.setup_logging(cfg.OUTPUT_DIR)
  
      if len(cfg.TEST.NUM_TEMPORAL_CLIPS) == 0:
          cfg.TEST.NUM_TEMPORAL_CLIPS = [cfg.TEST.NUM_ENSEMBLE_VIEWS]
  
      test_meters = []
      for num_view in cfg.TEST.NUM_TEMPORAL_CLIPS:
          cfg.TEST.NUM_ENSEMBLE_VIEWS = num_view
  
          # Print config.
          logger.info("Test with config:")
          logger.info(cfg)
  
          # Build the video model and print model statistics.
          model = build_model(cfg)
          flops, params = 0.0, 0.0
          if du.is_master_proc() and cfg.LOG_MODEL_INFO:
              model.eval()
              flops, params = misc.log_model_info(
                  model, cfg, use_train_input=False
              )
  
          if du.is_master_proc() and cfg.LOG_MODEL_INFO:
              misc.log_model_info(model, cfg, use_train_input=False)
          if (
              cfg.TASK == "ssl"
              and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
              and cfg.CONTRASTIVE.KNN_ON
          ):
              train_loader = loader.construct_loader(cfg, "train")
              if hasattr(model, "module"):
                  model.module.init_knn_labels(train_loader)
              else:
                  model.init_knn_labels(train_loader)
  
          cu.load_test_checkpoint(cfg, model)
  
          # Create video testing loaders.
          test_loader = loader.construct_loader(cfg, "test")
          logger.info(
              "Testing model for {} iterations".format(len(test_loader))
          )
  
          if cfg.DETECTION.ENABLE:
              assert (
                  cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE
                  or cfg.NUM_GPUS == 0
              )
              test_meter = AVAMeter(len(test_loader), cfg, mode="test")
          else:
              assert (
                  test_loader.dataset.num_videos
                  % (
                      cfg.TEST.NUM_ENSEMBLE_VIEWS
                      * cfg.TEST.NUM_SPATIAL_CROPS
                  )
                  == 0
              )
              # Create meters for multi-view testing.
              test_meter = TestMeter(
                  test_loader.dataset.num_videos
                  // (
                      cfg.TEST.NUM_ENSEMBLE_VIEWS
                      * cfg.TEST.NUM_SPATIAL_CROPS
                  ),
                  cfg.TEST.NUM_ENSEMBLE_VIEWS
                  * cfg.TEST.NUM_SPATIAL_CROPS,
                  cfg.MODEL.NUM_CLASSES
                  if not cfg.TASK == "ssl"
                  else cfg.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM,
                  len(test_loader),
                  cfg.DATA.MULTI_LABEL,
                  cfg.DATA.ENSEMBLE_METHOD,
              )
  
          # Set up writer for logging to Tensorboard format.
          if cfg.TENSORBOARD.ENABLE and du.is_master_proc(
              cfg.NUM_GPUS * cfg.NUM_SHARDS
          ):
              writer = tb.TensorboardWriter(cfg)
          else:
              writer = None
  
          # # Perform multi-view test on the entire dataset.
          test_meter = perform_test(
              test_loader, model, test_meter, cfg, writer
          )
          test_meters.append(test_meter)
          if writer is not None:
              writer.close()
  
      result_string_views = "_p{:.2f}_f{:.2f}".format(
          params / 1e6, flops
      )
  
      for view, test_meter in zip(
          cfg.TEST.NUM_TEMPORAL_CLIPS, test_meters
      ):
          logger.info(
              "Finalized testing with {} temporal clips and {} spatial crops".format(
                  view, cfg.TEST.NUM_SPATIAL_CROPS
              )
          )
          result_string_views += "_{}a{}" "".format(
              view, test_meter.stats["top1_acc"]
          )
  
          result_string = (
              "_p{:.2f}_f{:.2f}_{}a{} Top5 Acc: {} MEM: {:.2f} f: {:.4f}"
              "".format(
                  params / 1e6,
                  flops,
                  view,
                  test_meter.stats["top1_acc"],
                  test_meter.stats["top5_acc"],
                  misc.gpu_mem_usage(),
                  flops,
              )
          )
  
          logger.info("{}".format(result_string))
      logger.info("{}".format(result_string_views))
      return result_string + " \n " + result_string_views
  
file_path: SlowFast/tools/test_net.py
imports: import numpy
  import os
  import pickle
  import torch
  import slowfast.utils.checkpoint
  import slowfast.utils.distributed
  import slowfast.utils.logging
  import slowfast.utils.misc
  import slowfast.visualization.tensorboard_vis
  from slowfast.datasets import loader
  from slowfast.models import build_model
  from slowfast.utils.env import pathmgr
  from slowfast.utils.meters import AVAMeter, TestMeter
label_desc: Use image node_id 2 for calling a global function with example usage: test(cfg) and returns: unknown