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
  
imports: import os
  import time
  from utils.render_utils import render_vanilla
  import tqdm
  import torch
  import torch.nn.functional
  import torchvision.utils
  import numpy
  from utils import utils, render_utils, ray_utils
  from trainers import base_trainer, tensorboard_helper
  from models.vanilla import weight_reset
label_desc: Use image node_id 8 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.train_epoch() without return types