code: def train_one_epoch(
      model,
      criterion,
      optimizer,
      data_loader,
      lr_scheduler,
      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}")
      )
      header = f"Epoch: [{epoch}]"
      for image, target in metric_logger.log_every(
          data_loader, print_freq, header
      ):
          image, target = image.to(device), target.to(device)
          with torch.cuda.amp.autocast(enabled=scaler is not None):
              output = model(image)
              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()
  
          lr_scheduler.step()
  
          metric_logger.update(
              loss=loss.item(), lr=optimizer.param_groups[0]["lr"]
          )
  
file_path: vision/references/segmentation/train.py
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import utils
  from coco_utils import get_coco
  from torch import nn
  from torch.optim.lr_scheduler import PolynomialLR
  from torchvision.transforms import functional, InterpolationMode
label_desc: Use image node_id 5 for calling a global function with example usage: train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, device, epoch, print_freq, scaler) without return types