code: def _color_range_regularization(self, pts, dirs, tgts):
      device = pts.device
      dummy_dirs = torch.randn(
          dirs.shape, dtype=dirs.dtype, device=device
      )
      dummy_dirs = dummy_dirs / torch.norm(
          dummy_dirs, dim=-1, keepdim=True
      )
      dummy_out = self.net.coarse_human_net(pts, dummy_dirs)
      color_reg = (
          F.mse_loss(
              torch.sigmoid(dummy_out.reshape(-1, 4))[:, :3],
              torch.sigmoid(tgts.reshape(-1, 4))[:, :3],
          )
          * self.penalize_color_range
      )
      return color_reg
  
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 6 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj._color_range_regularization(pts, dirs, tgts) and returns: color_reg