docstr: entrance of the whole training process
          
docstr_tok: ["entrance","of","the","whole","training","process"]
code: def train(self):
      """entrance of the whole training process"""
      max_epoch = int(
          math.ceil(1.0 * self.max_iter / len(self.train_loader))
      )
      for epoch in tqdm.trange(
          self.epoch, max_epoch, desc="Train", ncols=80
      ):
          self.epoch = epoch
          time.sleep(
              2
          )  # Prevent possible deadlock during epoch transition
          self.train_epoch()
          if self.iteration >= self.max_iter:
              break
  
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 3 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.train() without return types