code: def laplacian_uniform(self, verts, faces):
      V = verts.shape[0]
      F = faces.shape[0]
  
      # Neighbor indices
      ii = faces[:, [1, 2, 0]].flatten()
      jj = faces[:, [2, 0, 1]].flatten()
      adj = torch.stack(
          [torch.cat([ii, jj]), torch.cat([jj, ii])], dim=0
      ).unique(dim=1)
      adj_values = torch.ones(
          adj.shape[1], device=verts.device, dtype=torch.float
      )
  
      # Diagonal indices
      diag_idx = adj[0]
  
      # Build the sparse matrix
      idx = torch.cat(
          (adj, torch.stack((diag_idx, diag_idx), dim=0)), dim=1
      )
      values = torch.cat((-adj_values, adj_values))
  
      # The coalesce operation sums the duplicate indices, resulting in the
      # correct diagonal
      return torch.sparse_coo_tensor(idx, values, (V, V)).coalesce()
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the LaplacianSmoothLoss obj's underlying member method code with example usage: obj.laplacian_uniform(verts, faces) and returns: torch