code: def __init__(
      self,
      opt,
      net,
      optimizer,
      train_loader,
      val_loader,
      train_dataset,
      val_dataset,
      interval_comp=1.0,
  ):
      self.train_dataset = train_dataset
      self.val_dataset = val_dataset
      self.opt = opt
      self.net = net
      self.use_cuda = opt.use_cuda
      self.optim = optimizer
      self.train_loader = train_loader
      self.val_loader = val_loader
      self.out = opt.out
      if not os.path.exists(opt.out):
          os.makedirs(opt.out)
      self.epoch = 0
      self.iteration = 0
      self.max_iter = opt.max_iter
      self.valid_iter = opt.valid_iter
      self.tb_pusher = tensorboard_helper.TensorboardPusher(opt)
      self.push_opt_to_tb()
      self.need_resume = opt.resume
      if self.need_resume:
          self.resume()
      if self.opt.load_weights:
          self.load_pretrained_weights()
      self.penalize_smpl_alpha = opt.penalize_smpl_alpha
      self.penalize_symmetric_alpha = opt.penalize_symmetric_alpha
      self.penalize_dummy = opt.penalize_dummy
      self.penalize_hard_surface = opt.penalize_hard_surface
      self.penalize_color_range = opt.penalize_color_range
      self.penalize_outside = opt.penalize_outside
      self.penalize_mask = opt.penalize_mask
      self.penalize_lpips = opt.penalize_lpips
      self.penalize_sharp_edge = opt.penalize_sharp_edge
      if self.penalize_lpips > 0:
          self.lpips_loss_fn = lpips.LPIPS(net="alex").to(
              next(self.net.parameters()).device
          )
      self.interval_comp = interval_comp
  
      center, up = utils.smpl_verts_to_center_and_up(
          self.val_dataset.scene.static_vert[0]
      )
      render_poses = render_utils.default_360_path(
          center, up, CANONICAL_CAMERA_DIST, 100
      )
      if opt.tgt_size is not None:
          render_size = opt.tgt_size
      else:
          render_size = self.val_dataset.scene.captures[
              0
          ].pinhole_cam.shape
      self.can_caps = [
          ResizedPinholeCapture(
              PinholeCamera(
                  self.val_dataset.scene.captures[0].pinhole_cam.width,
                  self.val_dataset.scene.captures[0].pinhole_cam.height,
                  CANONICAL_ZOOM_FACTOR
                  * self.val_dataset.scene.captures[
                      0
                  ].pinhole_cam.width,
                  CANONICAL_ZOOM_FACTOR
                  * self.val_dataset.scene.captures[
                      0
                  ].pinhole_cam.width,
                  self.val_dataset.scene.captures[0].pinhole_cam.width
                  / 2.0,
                  self.val_dataset.scene.captures[0].pinhole_cam.height
                  / 2.0,
              ),
              rp,
              tgt_size=render_size,
          )
          for rp in render_poses
      ]
  
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 1 to create a new HumanNeRFTrainer object  with example: obj = HumanNeRFTrainer(opt, net, optimizer, train_loader, val_loader, train_dataset, val_dataset, interval_comp)