code: def train(model, data_loader, optimizer, epoch, device, config):
      # train
      model.train()
  
      metric_logger = utils.MetricLogger(delimiter="  ")
      metric_logger.add_meter(
          "lr", utils.SmoothedValue(window_size=50, fmt="{value:.6f}")
      )
      metric_logger.add_meter(
          "loss_ita",
          utils.SmoothedValue(window_size=50, fmt="{value:.4f}"),
      )
      metric_logger.add_meter(
          "loss_itm",
          utils.SmoothedValue(window_size=50, fmt="{value:.4f}"),
      )
      metric_logger.add_meter(
          "loss_lm",
          utils.SmoothedValue(window_size=50, fmt="{value:.4f}"),
      )
  
      header = "Train Epoch: [{}]".format(epoch)
      print_freq = 50
  
      if config["laion_path"]:
          data_loader.dataset.reload_laion(epoch)
  
      data_loader.sampler.set_epoch(epoch)
  
      for i, (image, caption) in enumerate(
          metric_logger.log_every(data_loader, print_freq, header)
      ):
          if epoch == 0:
              warmup_lr_schedule(
                  optimizer,
                  i,
                  config["warmup_steps"],
                  config["warmup_lr"],
                  config["init_lr"],
              )
  
          optimizer.zero_grad()
  
          image = image.to(device, non_blocking=True)
  
          # ramp up alpha in the first 2 epochs
          alpha = config["alpha"] * min(
              1, (epoch * len(data_loader) + i) / (2 * len(data_loader))
          )
  
          loss_ita, loss_itm, loss_lm = model(
              image, caption, alpha=alpha
          )
          loss = loss_ita + loss_itm + loss_lm
  
          loss.backward()
          optimizer.step()
  
          metric_logger.update(loss_ita=loss_ita.item())
          metric_logger.update(loss_itm=loss_itm.item())
          metric_logger.update(loss_lm=loss_lm.item())
          metric_logger.update(lr=optimizer.param_groups[0]["lr"])
  
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger.global_avg())
      return {
          k: "{:.3f}".format(meter.global_avg)
          for k, meter in metric_logger.meters.items()
      }
  
imports: import argparse
  import os
  import ruamel_yaml
  import numpy
  import random
  import time
  import datetime
  import json
  from pathlib import Path
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.backends.cudnn
  import torch.distributed
  from torch.utils.data import DataLoader
  from models.blip_pretrain import blip_pretrain
  import utils
  from utils import warmup_lr_schedule, step_lr_schedule
  from data import create_dataset, create_sampler, create_loader
label_desc: Use image node_id 1 for calling a global function with example usage: train(model, data_loader, optimizer, epoch, device, config) and returns: unknown