code: def eval_epoch(
      val_loader, model, val_meter, cur_epoch, cfg, train_loader, writer
  ):
      """
      Evaluate the model on the val set.
      Args:
          val_loader (loader): data loader to provide validation data.
          model (model): model to evaluate the performance.
          val_meter (ValMeter): meter instance to record and calculate the metrics.
          cur_epoch (int): number of the current epoch of training.
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
          writer (TensorboardWriter, optional): TensorboardWriter object
              to writer Tensorboard log.
      """
  
      # Evaluation mode enabled. The running stats would not be updated.
      model.eval()
      val_meter.iter_tic()
  
      for cur_iter, (inputs, labels, index, time, meta) in enumerate(
          val_loader
      ):
          if cfg.NUM_GPUS:
              # Transferthe data to the current GPU device.
              if isinstance(inputs, (list,)):
                  for i in range(len(inputs)):
                      inputs[i] = inputs[i].cuda(non_blocking=True)
              else:
                  inputs = inputs.cuda(non_blocking=True)
              labels = labels.cuda()
              for key, val in meta.items():
                  if isinstance(val, (list,)):
                      for i in range(len(val)):
                          val[i] = val[i].cuda(non_blocking=True)
                  else:
                      meta[key] = val.cuda(non_blocking=True)
              index = index.cuda()
              time = time.cuda()
          batch_size = (
              inputs[0][0].size(0)
              if isinstance(inputs[0], list)
              else inputs[0].size(0)
          )
          val_meter.data_toc()
  
          if cfg.DETECTION.ENABLE:
              # Compute the predictions.
              preds = model(inputs, meta["boxes"])
              ori_boxes = meta["ori_boxes"]
              metadata = meta["metadata"]
  
              if cfg.NUM_GPUS:
                  preds = preds.cpu()
                  ori_boxes = ori_boxes.cpu()
                  metadata = metadata.cpu()
  
              if cfg.NUM_GPUS > 1:
                  preds = torch.cat(
                      du.all_gather_unaligned(preds), dim=0
                  )
                  ori_boxes = torch.cat(
                      du.all_gather_unaligned(ori_boxes), dim=0
                  )
                  metadata = torch.cat(
                      du.all_gather_unaligned(metadata), dim=0
                  )
  
              val_meter.iter_toc()
              # Update and log stats.
              val_meter.update_stats(preds, ori_boxes, metadata)
  
          else:
              if (
                  cfg.TASK == "ssl"
                  and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
              ):
                  if not cfg.CONTRASTIVE.KNN_ON:
                      return
                  train_labels = (
                      model.module.train_labels
                      if hasattr(model, "module")
                      else model.train_labels
                  )
                  yd, yi = model(inputs, index, time)
                  K = yi.shape[1]
                  C = (
                      cfg.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM
                  )  # eg 400 for Kinetics400
                  candidates = train_labels.view(1, -1).expand(
                      batch_size, -1
                  )
                  retrieval = torch.gather(candidates, 1, yi)
                  retrieval_one_hot = torch.zeros(
                      (batch_size * K, C)
                  ).cuda()
                  retrieval_one_hot.scatter_(
                      1, retrieval.view(-1, 1), 1
                  )
                  yd_transform = (
                      yd.clone().div_(cfg.CONTRASTIVE.T).exp_()
                  )
                  probs = torch.mul(
                      retrieval_one_hot.view(batch_size, -1, C),
                      yd_transform.view(batch_size, -1, 1),
                  )
                  preds = torch.sum(probs, 1)
              else:
                  preds = model(inputs)
  
              if cfg.DATA.MULTI_LABEL:
                  if cfg.NUM_GPUS > 1:
                      preds, labels = du.all_gather([preds, labels])
              else:
                  if cfg.DATA.IN22k_VAL_IN1K != "":
                      preds = preds[:, :1000]
                  # Compute the errors.
                  num_topks_correct = metrics.topks_correct(
                      preds, labels, (1, 5)
                  )
  
                  # Combine the errors across the GPUs.
                  top1_err, top5_err = [
                      (1.0 - x / preds.size(0)) * 100.0
                      for x in num_topks_correct
                  ]
                  if cfg.NUM_GPUS > 1:
                      top1_err, top5_err = du.all_reduce(
                          [top1_err, top5_err]
                      )
  
                  # Copy the errors from GPU to CPU (sync point).
                  top1_err, top5_err = top1_err.item(), top5_err.item()
  
                  val_meter.iter_toc()
                  # Update and log stats.
                  val_meter.update_stats(
                      top1_err,
                      top5_err,
                      batch_size
                      * max(
                          cfg.NUM_GPUS, 1
                      ),  # If running  on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
                  )
                  # write to tensorboard format if available.
                  if writer is not None:
                      writer.add_scalars(
                          {
                              "Val/Top1_err": top1_err,
                              "Val/Top5_err": top5_err,
                          },
                          global_step=len(val_loader) * cur_epoch
                          + cur_iter,
                      )
  
              val_meter.update_predictions(preds, labels)
  
          val_meter.log_iter_stats(cur_epoch, cur_iter)
          val_meter.iter_tic()
  
      # Log epoch stats.
      val_meter.log_epoch_stats(cur_epoch)
      # write to tensorboard format if available.
      if writer is not None:
          if cfg.DETECTION.ENABLE:
              writer.add_scalars(
                  {"Val/mAP": val_meter.full_map}, global_step=cur_epoch
              )
          else:
              all_preds = [
                  pred.clone().detach() for pred in val_meter.all_preds
              ]
              all_labels = [
                  label.clone().detach()
                  for label in val_meter.all_labels
              ]
              if cfg.NUM_GPUS:
                  all_preds = [pred.cpu() for pred in all_preds]
                  all_labels = [label.cpu() for label in all_labels]
              writer.plot_eval(
                  preds=all_preds,
                  labels=all_labels,
                  global_step=cur_epoch,
              )
  
      val_meter.reset()
  
imports: import math
  import numpy
  import pprint
  import torch
  from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
  import slowfast.models.losses
  import slowfast.models.optimizer
  import slowfast.utils.checkpoint
  import slowfast.utils.distributed
  import slowfast.utils.logging
  import slowfast.utils.metrics
  import slowfast.utils.misc
  import slowfast.visualization.tensorboard_vis
  from slowfast.datasets import loader
  from slowfast.datasets.mixup import MixUp
  from slowfast.models import build_model
  from slowfast.models.contrastive import contrastive_forward, contrastive_parameter_surgery
  from slowfast.utils.meters import AVAMeter, EpochTimer, TrainMeter, ValMeter
  from slowfast.utils.multigrid import MultigridSchedule
label_desc: Use image node_id 2 for calling a global function with example usage: eval_epoch(val_loader, model, val_meter, cur_epoch, cfg, train_loader, writer) without return types