code: def norm_laplacian(
      verts: torch.Tensor, edges: torch.Tensor, eps: float = 1e-12
  ) -> torch.Tensor:
      """
      Norm laplacian computes a variant of the laplacian matrix which weights each
      affinity with the normalized distance of the neighboring nodes.
      More concretely,
      L[i, j] = 1. / wij where wij = ||vi - vj|| if (vi, vj) are neighboring nodes
  
      Args:
          verts: tensor of shape (V, 3) containing the vertices of the graph
          edges: tensor of shape (E, 2) containing the vertex indices of each edge
      Returns:
          L: Sparse FloatTensor of shape (V, V)
      """
      edge_verts = verts[edges]  # (E, 2, 3)
      v0, v1 = edge_verts[:, 0], edge_verts[:, 1]
  
      # Side lengths of each edge, of shape (E,)
      w01 = 1.0 / ((v0 - v1).norm(dim=1) + eps)
  
      # Construct a sparse matrix by basically doing:
      # L[v0, v1] = w01
      # L[v1, v0] = w01
      e01 = edges.t()  # (2, E)
  
      V = verts.shape[0]
      # pyre-fixme[16]: Module `sparse` has no attribute `FloatTensor`.
      L = torch.sparse.FloatTensor(e01, w01, (V, V))
      L = L + L.t()
  
      return L
  
imports: from typing import Tuple
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: norm_laplacian(verts, edges, eps) and returns: L