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.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
              with torch.no_grad():
                  self.validate()
  
          self.train_batch(data_pack)
  
          if self.iteration >= self.max_iter:
              break
          self.iteration += 1
  
imports: import os
  import math
  import time
  import random
  import tqdm
  import torch
  import torch.nn.functional
  import numpy
  import torchvision.utils
  import igl
  import lpips
  from utils import utils, render_utils, ray_utils
  from trainers import tensorboard_helper
  from options import options
  from cameras.captures import ResizedPinholeCapture
  from cameras.pinhole_camera import PinholeCamera
  from models.vanilla import weight_reset
  from utils.constant import HARD_SURFACE_OFFSET, PATCH_SIZE, PATCH_SIZE_SQUARED, CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
label_desc: Use image node_id 16 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.train_epoch() without return types