code: def train_epoch(
      train_loader,
      model,
      optimizer,
      scaler,
      train_meter,
      cur_epoch,
      cfg,
      writer=None,
  ):
      """
      Perform the video training for one epoch.
      Args:
          train_loader (loader): video training loader.
          model (model): the video model to train.
          optimizer (optim): the optimizer to perform optimization on the model's
              parameters.
          train_meter (TrainMeter): training meters to log the training performance.
          cur_epoch (int): 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.
      """
      # Enable train mode.
      model.train()
      train_meter.iter_tic()
      data_size = len(train_loader)
  
      if cfg.MIXUP.ENABLE:
          mixup_fn = MixUp(
              mixup_alpha=cfg.MIXUP.ALPHA,
              cutmix_alpha=cfg.MIXUP.CUTMIX_ALPHA,
              mix_prob=cfg.MIXUP.PROB,
              switch_prob=cfg.MIXUP.SWITCH_PROB,
              label_smoothing=cfg.MIXUP.LABEL_SMOOTH_VALUE,
              num_classes=cfg.MODEL.NUM_CLASSES,
          )
  
      if cfg.MODEL.FROZEN_BN:
          misc.frozen_bn_stats(model)
      # Explicitly declare reduction to mean.
      loss_fun = losses.get_loss_func(cfg.MODEL.LOSS_FUNC)(
          reduction="mean"
      )
  
      for cur_iter, (inputs, labels, index, time, meta) in enumerate(
          train_loader
      ):
          # Transfer the data to the current GPU device.
          if cfg.NUM_GPUS:
              if isinstance(inputs, (list,)):
                  for i in range(len(inputs)):
                      if isinstance(inputs[i], (list,)):
                          for j in range(len(inputs[i])):
                              inputs[i][j] = inputs[i][j].cuda(
                                  non_blocking=True
                              )
                      else:
                          inputs[i] = inputs[i].cuda(non_blocking=True)
              else:
                  inputs = inputs.cuda(non_blocking=True)
              if not isinstance(labels, list):
                  labels = labels.cuda(non_blocking=True)
                  index = index.cuda(non_blocking=True)
                  time = time.cuda(non_blocking=True)
              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)
  
          batch_size = (
              inputs[0][0].size(0)
              if isinstance(inputs[0], list)
              else inputs[0].size(0)
          )
          # Update the learning rate.
          epoch_exact = cur_epoch + float(cur_iter) / data_size
          lr = optim.get_epoch_lr(epoch_exact, cfg)
          optim.set_lr(optimizer, lr)
  
          train_meter.data_toc()
          if cfg.MIXUP.ENABLE:
              samples, labels = mixup_fn(inputs[0], labels)
              inputs[0] = samples
  
          with torch.cuda.amp.autocast(
              enabled=cfg.TRAIN.MIXED_PRECISION
          ):
              # Explicitly declare reduction to mean.
              perform_backward = True
              optimizer.zero_grad()
  
              if cfg.MODEL.MODEL_NAME == "ContrastiveModel":
                  (
                      model,
                      preds,
                      partial_loss,
                      perform_backward,
                  ) = contrastive_forward(
                      model,
                      cfg,
                      inputs,
                      index,
                      time,
                      epoch_exact,
                      scaler,
                  )
              elif cfg.DETECTION.ENABLE:
                  # Compute the predictions.
                  preds = model(inputs, meta["boxes"])
              elif cfg.MASK.ENABLE:
                  preds, labels = model(inputs)
              else:
                  preds = model(inputs)
              if (
                  cfg.TASK == "ssl"
                  and cfg.MODEL.MODEL_NAME == "ContrastiveModel"
              ):
                  labels = torch.zeros(
                      preds.size(0),
                      dtype=labels.dtype,
                      device=labels.device,
                  )
  
              if (
                  cfg.MODEL.MODEL_NAME == "ContrastiveModel"
                  and partial_loss
              ):
                  loss = partial_loss
              else:
                  # Compute the loss.
                  loss = loss_fun(preds, labels)
  
          loss_extra = None
          if isinstance(loss, (list, tuple)):
              loss, loss_extra = loss
  
          # check Nan Loss.
          misc.check_nan_losses(loss)
          if perform_backward:
              scaler.scale(loss).backward()
          # Unscales the gradients of optimizer's assigned params in-place
          scaler.unscale_(optimizer)
          # Clip gradients if necessary
          if cfg.SOLVER.CLIP_GRAD_VAL:
              grad_norm = torch.nn.utils.clip_grad_value_(
                  model.parameters(), cfg.SOLVER.CLIP_GRAD_VAL
              )
          elif cfg.SOLVER.CLIP_GRAD_L2NORM:
              grad_norm = torch.nn.utils.clip_grad_norm_(
                  model.parameters(), cfg.SOLVER.CLIP_GRAD_L2NORM
              )
          else:
              grad_norm = optim.get_grad_norm_(model.parameters())
          # Update the parameters. (defaults to True)
          model, update_param = contrastive_parameter_surgery(
              model, cfg, epoch_exact, cur_iter
          )
          if update_param:
              scaler.step(optimizer)
          scaler.update()
  
          if cfg.MIXUP.ENABLE:
              _top_max_k_vals, top_max_k_inds = torch.topk(
                  labels, 2, dim=1, largest=True, sorted=True
              )
              idx_top1 = (
                  torch.arange(labels.shape[0]),
                  top_max_k_inds[:, 0],
              )
              idx_top2 = (
                  torch.arange(labels.shape[0]),
                  top_max_k_inds[:, 1],
              )
              preds = preds.detach()
              preds[idx_top1] += preds[idx_top2]
              preds[idx_top2] = 0.0
              labels = top_max_k_inds[:, 0]
  
          if cfg.DETECTION.ENABLE:
              if cfg.NUM_GPUS > 1:
                  loss = du.all_reduce([loss])[0]
              loss = loss.item()
  
              # Update and log stats.
              train_meter.update_stats(None, None, None, loss, lr)
              # write to tensorboard format if available.
              if writer is not None:
                  writer.add_scalars(
                      {"Train/loss": loss, "Train/lr": lr},
                      global_step=data_size * cur_epoch + cur_iter,
                  )
  
          else:
              top1_err, top5_err = None, None
              if cfg.DATA.MULTI_LABEL:
                  # Gather all the predictions across all the devices.
                  if cfg.NUM_GPUS > 1:
                      loss, grad_norm = du.all_reduce([loss, grad_norm])
                  loss, grad_norm = (
                      loss.item(),
                      grad_norm.item(),
                  )
              elif cfg.MASK.ENABLE:
                  # Gather all the predictions across all the devices.
                  if cfg.NUM_GPUS > 1:
                      loss, grad_norm = du.all_reduce([loss, grad_norm])
                      if loss_extra:
                          loss_extra = du.all_reduce(loss_extra)
                  loss, grad_norm, top1_err, top5_err = (
                      loss.item(),
                      grad_norm.item(),
                      0.0,
                      0.0,
                  )
                  if loss_extra:
                      loss_extra = [
                          one_loss.item() for one_loss in loss_extra
                      ]
              else:
                  # Compute the errors.
                  num_topks_correct = metrics.topks_correct(
                      preds, labels, (1, 5)
                  )
                  top1_err, top5_err = [
                      (1.0 - x / preds.size(0)) * 100.0
                      for x in num_topks_correct
                  ]
                  # Gather all the predictions across all the devices.
                  if cfg.NUM_GPUS > 1:
                      (
                          loss,
                          grad_norm,
                          top1_err,
                          top5_err,
                      ) = du.all_reduce(
                          [loss.detach(), grad_norm, top1_err, top5_err]
                      )
  
                  # Copy the stats from GPU to CPU (sync point).
                  loss, grad_norm, top1_err, top5_err = (
                      loss.item(),
                      grad_norm.item(),
                      top1_err.item(),
                      top5_err.item(),
                  )
  
              # Update and log stats.
              train_meter.update_stats(
                  top1_err,
                  top5_err,
                  loss,
                  lr,
                  grad_norm,
                  batch_size
                  * max(
                      cfg.NUM_GPUS, 1
                  ),  # If running  on CPU (cfg.NUM_GPUS == 1), use 1 to represent 1 CPU.
                  loss_extra,
              )
              # write to tensorboard format if available.
              if writer is not None:
                  writer.add_scalars(
                      {
                          "Train/loss": loss,
                          "Train/lr": lr,
                          "Train/Top1_err": top1_err,
                          "Train/Top5_err": top5_err,
                      },
                      global_step=data_size * cur_epoch + cur_iter,
                  )
          train_meter.iter_toc()  # do measure allreduce for this meter
          train_meter.log_iter_stats(cur_epoch, cur_iter)
          torch.cuda.synchronize()
          train_meter.iter_tic()
      del inputs
  
      # in case of fragmented memory
      torch.cuda.empty_cache()
  
      # Log epoch stats.
      train_meter.log_epoch_stats(cur_epoch)
      train_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 1 for calling a global function with example usage: train_epoch(train_loader, model, optimizer, scaler, train_meter, cur_epoch, cfg, writer) without return types