code: def train_one_epoch(
      model,
      criterion,
      optimizer,
      lr_scheduler,
      data_loader,
      device,
      epoch,
      print_freq,
      scaler=None,
  ):
      model.train()
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter(
          "lr", utils.SmoothedValue(window_size=1, fmt="{value}")
      )
      metric_logger.add_meter(
          "clips/s",
          utils.SmoothedValue(window_size=10, fmt="{value:.3f}"),
      )
  
      header = f"Epoch: [{epoch}]"
      for video, target, _ in metric_logger.log_every(
          data_loader, print_freq, header
      ):
          start_time = time.time()
          video, target = video.to(device), target.to(device)
          with torch.cuda.amp.autocast(enabled=scaler is not None):
              output = model(video)
              loss = criterion(output, target)
  
          optimizer.zero_grad()
  
          if scaler is not None:
              scaler.scale(loss).backward()
              scaler.step(optimizer)
              scaler.update()
          else:
              loss.backward()
              optimizer.step()
  
          acc1, acc5 = utils.accuracy(output, target, topk=(1, 5))
          batch_size = video.shape[0]
          metric_logger.update(
              loss=loss.item(), lr=optimizer.param_groups[0]["lr"]
          )
          metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
          metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
          metric_logger.meters["clips/s"].update(
              batch_size / (time.time() - start_time)
          )
          lr_scheduler.step()
  
file_path: vision/references/video_classification/train.py
imports: import datetime
  import os
  import time
  import warnings
  import datasets
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import torchvision.datasets.video_utils
  import utils
  from torch import nn
  from torch.utils.data.dataloader import default_collate
  from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
label_desc: Use image node_id 1 for calling a global function with example usage: train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader, device, epoch, print_freq, scaler) without return types