class_docstr: base trainer class.
  contains methods for training, validation, and writing output.
class_docstr_tok: ["base","trainer","class",".","contains","methods","for","training",",","validation",",","and","writing","output","."]
docstr: train for one epoch
  one epoch is iterating the whole training dataset once
docstr_tok: ["train","for","one","epoch","one","epoch","is","iterating","the","whole","training","dataset","once"]
code: def train_epoch(self):
      """train for one epoch
      one epoch is iterating the whole training dataset once
      """
      self.coarse_net.train()
      if self.fine_net is not None:
          self.fine_net.train()
      for batch_idx, data_pack in tqdm.tqdm(
          enumerate(self.train_loader),
          initial=self.iteration % len(self.train_loader),
          total=len(self.train_loader),
          desc="Train epoch={0}".format(self.epoch),
          ncols=80,
          leave=True,
      ):
          if self.iteration % self.valid_iter == 0:
              time.sleep(
                  2
              )  # Prevent possible deadlock during epoch transition
              self.validate()
          self.train_batch(data_pack)
  
          if self.iteration >= self.max_iter:
              break
          self.iteration += 1
  
file_path: ml-neuman/trainers/base_trainer.py
imports: import os
  import math
  import abc
  import time
  import tqdm
  from trainers import tensorboard_helper
  from options import options
label_desc: Use image node_id 6 for calling the BaseTrainer obj's underlying member method code with example usage: obj.train_epoch() without return types