class_docstr: Computes the delaunay triangulation of a set of points
  (functional name: :obj:`delaunay`).
code: def forward(self, data: Data) -> Data:
      assert data.pos is not None
  
      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
  
imports: import scipy.spatial
  import torch
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 1 for calling the Delaunay obj's underlying member method code with example usage: obj.forward(data) and returns: data