code: def main(opt):
      # read scene
      scene = neuman_helper.NeuManReader.read_scene(
          opt.scene_dir,
          tgt_size=opt.render_size,
          normalize=opt.normalize,
          bkg_range_scale=opt.bkg_range_scale,
          human_range_scale=opt.human_range_scale,
          smpl_type="optimized",
      )
      if opt.geo_threshold < 0:
          bones = []
          for i in range(len(scene.captures)):
              bones.append(
                  np.linalg.norm(
                      scene.smpls[i]["joints_3d"][3]
                      - scene.smpls[i]["joints_3d"][0]
                  )
              )
          opt.geo_threshold = np.mean(bones)
  
      # read human poses
      raw_verts, Ts = read_human_poses(opt, scene)
  
      # read novel captures(cameras)
      caps = read_novel_caps(opt, len(raw_verts), scene)
  
      # read network
      net = human_nerf.HumanNeRF(opt)
      weights = torch.load(opt.weights_path, map_location="cpu")
      utils.safe_load_weights(net, weights["hybrid_model_state_dict"])
  
      # render
      for i in range(len(raw_verts)):
          out = render_utils.render_hybrid_nerf(
              net,
              caps[i],
              raw_verts[i],
              scene.faces,
              Ts[i],
              rays_per_batch=opt.rays_per_batch,
              samples_per_ray=opt.samples_per_ray,
              geo_threshold=opt.geo_threshold,
              return_depth=False,
          )
          save_path = os.path.join(
              "./demo",
              f"reposing/{os.path.basename(opt.scene_dir)}",
              f"out_{str(i).zfill(4)}.png",
          )
          if not os.path.isdir(os.path.dirname(save_path)):
              os.makedirs(os.path.dirname(save_path))
          imageio.imsave(save_path, out)
          print(f"image saved: {save_path}")
  
file_path: ml-neuman/render_reposing.py
imports: import os
  import argparse
  import copy
  import imageio
  import torch
  import numpy
  from geometry import transformations
  from models import human_nerf
  from utils import render_utils, utils, ray_utils
  from data_io import neuman_helper
  from options import options
  from models.smpl import SMPL
label_desc: Use image node_id 5 for calling a global function with example usage: main(opt) without return types