code: def get_xyz_vertex_embedding(mesh_name: str, device: torch.device):
      if mesh_name == "smpl_27554":
          embed_path = PathManager.get_local_path(
              "https://dl.fbaipublicfiles.com/densepose/data/cse/mds_d=256.npy"
          )
          embed_map, _ = np.load(embed_path, allow_pickle=True)
          embed_map = torch.tensor(embed_map).float()[:, 0]
          embed_map -= embed_map.min()
          embed_map /= embed_map.max()
      else:
          mesh = create_mesh(mesh_name, device)
          embed_map = mesh.vertices.sum(dim=1)
          embed_map -= embed_map.min()
          embed_map /= embed_map.max()
          embed_map = embed_map**2
      return embed_map
  
imports: import json
  import numpy
  from functools import lru_cache
  from typing import Dict, List, Optional, Tuple
  import cv2
  import torch
  from detectron2.utils.file_io import PathManager
  from densepose.modeling import build_densepose_embedder
  from densepose.modeling.cse.utils import get_closest_vertices_mask_from_ES
  from ..data.utils import get_class_to_mesh_name_mapping
  from ..structures import DensePoseEmbeddingPredictorOutput
  from ..structures.mesh import create_mesh
  from .base import Boxes, Image, MatrixVisualizer
  from .densepose_results_textures import get_texture_atlas
label_desc: Use image node_id 1 for calling a global function with example usage: get_xyz_vertex_embedding(mesh_name, device) and returns: embed_map