code: def smpl_verts_to_center_and_up(verts):
      device = torch.device("cpu")
      body_model = SMPL(
          os.path.join(
              os.path.abspath(
                  os.path.join(os.path.dirname(__file__), "..")
              ),
              "data/smplx/smpl",
          ),
          gender="neutral",
          device=device,
      )
      joints = vertices2joints(
          body_model.J_regressor, torch.from_numpy(verts)[None]
      ).numpy()[0]
      spine_ind = [0, 3, 6, 9]
      spine = joints[spine_ind]
      center = spine.mean(axis=0)
      _, _, vv = np.linalg.svd(spine - center)
      linepts = vv[0] * np.mgrid[-7:7:2j][:, np.newaxis]
      linepts += center
      spine_dir = spine[3] - spine[0]
      up = linepts[1] - linepts[0]
      if np.dot(spine_dir, up) < 0:
          up = linepts[0] - linepts[1]
      up /= np.linalg.norm(up)
      return center, up
  
imports: import os
  import random
  import numpy
  import open3d
  import torch
  import pytorch3d
  import matplotlib.pyplot
  import matplotlib
  from scipy import ndimage
  from tqdm import tqdm
  from models.smpl import SMPL, vertices2joints
label_desc: Use image node_id 15 for calling a global function with example usage: smpl_verts_to_center_and_up(verts) and returns: center,  up