code: def densify_and_clone(self, grads, grad_threshold, scene_extent):
      # Extract points that satisfy the gradient condition
      selected_pts_mask = torch.where(
          torch.norm(grads, dim=-1) >= grad_threshold, True, False
      )
      selected_pts_mask = torch.logical_and(
          selected_pts_mask,
          torch.max(self.get_scaling, dim=1).values
          <= self.percent_dense * scene_extent,
      )
  
      new_xyz = self._xyz[selected_pts_mask]
      new_features_dc = self._features_dc[selected_pts_mask]
      new_features_rest = self._features_rest[selected_pts_mask]
      new_opacities = self._opacity[selected_pts_mask]
      new_scaling = self._scaling[selected_pts_mask]
      new_rotation = self._rotation[selected_pts_mask]
  
      self.densification_postfix(
          new_xyz,
          new_features_dc,
          new_features_rest,
          new_opacities,
          new_scaling,
          new_rotation,
      )
  
imports: import torch
  import numpy
  from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
  from torch import nn
  import os
  from utils.system_utils import mkdir_p
  from plyfile import PlyData, PlyElement
  from utils.sh_utils import RGB2SH
  from simple_knn._C import distCUDA2
  from utils.graphics_utils import BasicPointCloud
  from utils.general_utils import strip_symmetric, build_scaling_rotation
label_desc: Use image node_id 25 for calling the GaussianModel obj's underlying member method code with example usage: obj.densify_and_clone(grads, grad_threshold, scene_extent) without return types