text: import scipy.spatial
  import torch
  
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
  
  
  @functional_transform('delaunay')
  class Delaunay(BaseTransform):
      r"""Computes the delaunay triangulation of a set of points
      (functional name: :obj:`delaunay`)."""
      def __call__(self, data):
          if data.pos.size(0) < 2:
              data.edge_index = torch.tensor([], dtype=torch.long,
                                             device=data.pos.device).view(2, 0)
          if data.pos.size(0) == 2:
              data.edge_index = torch.tensor([[0, 1], [1, 0]], dtype=torch.long,
                                             device=data.pos.device)
          elif data.pos.size(0) == 3:
              data.face = torch.tensor([[0], [1], [2]], dtype=torch.long,
                                       device=data.pos.device)
          if data.pos.size(0) > 3:
              pos = data.pos.cpu().numpy()
              tri = scipy.spatial.Delaunay(pos, qhull_options='QJ')
              face = torch.from_numpy(tri.simplices)
  
              data.face = face.t().contiguous().to(data.pos.device, torch.long)
  
          return data
  