code: def end_epoch(self):
      epoch_time = time.time() - self.epoch_start
      epoch_speed = self.processed_samples / epoch_time
  
      self.epochs_times.append(epoch_time)
      self.epochs_speeds.append(epoch_speed)
      self.processed_samples = 0
  
      if self.local_rank == 0:
          print(
              "Epoch {:2d} finished. Time: {:4f} s, Speed: {:4f} img/sec, Average speed: {:4f}".format(
                  len(self.epochs_times) - 1,
                  epoch_time,
                  epoch_speed * self.n_gpu,
                  self.average_speed() * self.n_gpu,
              )
          )
  
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 4 for calling the Logger obj's underlying member method code with example usage: obj.end_epoch() without return types