code: def _sparsity_regularization(self, device):
      sparsity_reg = (
          torch.tensor(0.0, requires_grad=True).float().to(device)
      )
      # pick a random camera
      num_can_rays = 128
      can_cap = random.choice(self.can_caps)
      coords = np.argwhere(np.ones(can_cap.shape))
      coords = coords[np.random.randint(0, len(coords), num_can_rays)][
          :, ::-1
      ]  # could get duplicated rays
      can_orig, can_dir = ray_utils.shot_rays(can_cap, coords)
      can_pts, can_dirs, can_z_vals = ray_utils.ray_to_samples(
          {
              "origin": torch.from_numpy(can_orig).float().to(device),
              "direction": torch.from_numpy(can_dir).float().to(device),
              "near": torch.zeros(num_can_rays, 1).float().to(device),
              "far": torch.ones(num_can_rays, 1).float().to(device)
              * CANONICAL_CAMERA_DIST
              * 1.667,
          },
          samples_per_ray=self.opt.samples_per_ray,
          device=device,
          perturb=self.opt.perturb,
      )
      can_out = self.net.coarse_human_net(can_pts, can_dirs)
      # compensate the interval difference between observation space and canonical space
      can_out[..., -1] *= self.interval_comp
      _, _, can_mask, can_weights, _ = render_utils.raw2outputs(
          can_out,
          can_z_vals.clone(),
          can_dirs[:, 0, :].clone(),
          white_bkg=True,
      )
      can_weights = torch.clip(can_weights, 0.0, 1.0)
      can_mask = torch.clip(can_mask, 0.0, 1.0)
      # sharp edge loss
      if self.penalize_sharp_edge > 0:
          sparsity_reg = (
              sparsity_reg
              + torch.mean(
                  -torch.log(
                      torch.exp(-torch.abs(can_mask))
                      + torch.exp(-torch.abs(1 - can_mask))
                  )
                  + HARD_SURFACE_OFFSET
              )
              * self.penalize_sharp_edge
          )
      # hard surface loss
      if self.penalize_hard_surface > 0:
          sparsity_reg = (
              sparsity_reg
              + torch.mean(
                  -torch.log(
                      torch.exp(-torch.abs(can_weights))
                      + torch.exp(-torch.abs(1 - can_weights))
                  )
                  + HARD_SURFACE_OFFSET
              )
              * self.penalize_hard_surface
          )
      return sparsity_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 9 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj._sparsity_regularization(device) and returns: sparsity_reg