code: def get_NormalMaps(model):
      def get_detailed_mesh(input):
          origin_mesh = input.copy()
          mesh_points = (
              torch.tensor(
                  np.array(origin_mesh.vertices).astype(np.float32)
              )
              .cuda()
              .requires_grad_(True)
          )
          detailed_normal = model.implicit_network.gradient(mesh_points)
          detailed_normal = (
              detailed_normal.squeeze().detach().cpu().numpy()
          )
          vetices_normal = np.asarray(origin_mesh.vertex_normals)
          return vetices_normal, detailed_normal
  
      orimesh = model.mesh
      smooth1, detail1 = get_detailed_mesh(orimesh)
      return smooth1, detail1
  
imports: import plotly.graph_objs
  import plotly.offline
  import numpy
  import torch
  from skimage import measure
  import torchvision
  import trimesh
  from PIL import Image
  from utils import rend_util
  import pickle
label_desc: Use image node_id 9 for calling a global function with example usage: get_NormalMaps(model) and returns: smooth1,  detail1, vetices_normal,  detailed_normal