code: def densification_postfix(
      self,
      new_xyz,
      new_features_dc,
      new_features_rest,
      new_opacities,
      new_scaling,
      new_rotation,
  ):
      d = {
          "xyz": new_xyz,
          "f_dc": new_features_dc,
          "f_rest": new_features_rest,
          "opacity": new_opacities,
          "scaling": new_scaling,
          "rotation": new_rotation,
      }
  
      optimizable_tensors = self.cat_tensors_to_optimizer(d)
      self._xyz = optimizable_tensors["xyz"]
      self._features_dc = optimizable_tensors["f_dc"]
      self._features_rest = optimizable_tensors["f_rest"]
      self._opacity = optimizable_tensors["opacity"]
      self._scaling = optimizable_tensors["scaling"]
      self._rotation = optimizable_tensors["rotation"]
  
      self.xyz_gradient_accum = torch.zeros(
          (self.get_xyz.shape[0], 1), device="cuda"
      )
      self.denom = torch.zeros(
          (self.get_xyz.shape[0], 1), device="cuda"
      )
      self.max_radii2D = torch.zeros(
          (self.get_xyz.shape[0]), device="cuda"
      )
  
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 23 for calling the GaussianModel obj's underlying member method code with example usage: obj.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) without return types