code: def _smpl_shape_regularization(self, batch, pts, dirs, pred):
      device = pts.device
      smpl_reg = (
          torch.tensor(0.0, requires_grad=True).float().to(device)
      )
      can_mesh = self.val_dataset.scene.captures[
          batch["cap_id"]
      ].can_mesh
  
      dist_human, _, _ = igl.signed_distance(
          pts.reshape(-1, 3).detach().cpu().numpy(),
          can_mesh.verts_packed().cpu().numpy(),
          can_mesh.faces_packed().cpu().numpy(),
      )
      inside_volume = dist_human < 0
      if inside_volume.sum() > 0:
          smpl_reg = (
              smpl_reg
              + F.mse_loss(
                  1
                  - torch.exp(
                      -torch.relu(
                          pred.reshape(-1, 4)[inside_volume][:, 3]
                      )
                  ),
                  torch.ones_like(
                      pred.reshape(-1, 4)[inside_volume][:, 3]
                  ),
              )
              * self.penalize_smpl_alpha
          )
  
      # generate random samples inside a box in canonical space
      if self.penalize_dummy > 0:
          dummy_pts = (
              torch.rand(pts.shape, dtype=pts.dtype, device=device)
              - 0.5
          ) * 3
          dummy_out = self.net.coarse_human_net(dummy_pts, dirs)
          dist_dummy, _, _ = igl.signed_distance(
              dummy_pts.reshape(-1, 3).detach().cpu().numpy(),
              can_mesh.verts_packed().cpu().numpy(),
              can_mesh.faces_packed().cpu().numpy(),
          )
          dummy_inside = dist_dummy < 0
          dummy_outside = dist_dummy > 0
          if dummy_inside.sum() > 0:
              smpl_reg = (
                  smpl_reg
                  + F.mse_loss(
                      1
                      - torch.exp(
                          -torch.relu(
                              dummy_out.reshape(-1, 4)[dummy_inside][
                                  :, 3
                              ]
                          )
                      ),
                      torch.ones_like(
                          dummy_out.reshape(-1, 4)[dummy_inside][:, 3]
                      ),
                  )
                  * self.penalize_dummy
              )
          if dummy_outside.sum() > 0:
              smpl_reg = (
                  smpl_reg
                  + F.l1_loss(
                      (
                          1
                          - torch.exp(
                              -torch.relu(
                                  dummy_out.reshape(-1, 4)[
                                      dummy_outside
                                  ][:, 3]
                              )
                          )
                      )
                      * torch.pow(
                          torch.abs(
                              torch.from_numpy(
                                  dist_dummy[dummy_outside]
                              ).to(device)
                          )
                          * self.opt.penalize_outside_factor,
                          self.opt.dist_exponent,
                      ),
                      torch.zeros_like(
                          dummy_out.reshape(-1, 4)[dummy_outside][:, 3]
                      ),
                  )
                  * self.penalize_dummy
              )
      return smpl_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 8 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj._smpl_shape_regularization(batch, pts, dirs, pred) and returns: smpl_reg