code: def update_iter(self, epoch, iteration, loss):
      if self.local_rank != 0:
          return
  
      if iteration % self.print_freq == 0:
          print(
              "Epoch: {:2d}, Iteration: {}, Loss: {}".format(
                  epoch, iteration, loss
              )
          )
  
      self.processed_samples = self.processed_samples + self.batch_size
  
imports: import os
  import sys
  import time
  from argparse import ArgumentParser
  import math
  import numpy
  import time
  import torch
  from torch.optim.lr_scheduler import MultiStepLR
  import torch.utils.data.distributed
  from src.model import model, Loss
  from src.utils import dboxes300_coco, Encoder
  from src.evaluate import evaluate
  from src.train import train_loop, tencent_trick
  from src.data import *
label_desc: Use image node_id 2 for calling the Logger obj's underlying member method code with example usage: obj.update_iter(epoch, iteration, loss) without return types