code: def bm_mesh_laplacian_smoothing() -> None:
      devices = ["cpu"]
      if torch.cuda.is_available():
          devices.append("cuda")
  
      kwargs_list = []
      num_meshes = [2, 10, 32]
      num_verts = [100, 1000]
      num_faces = [300, 3000]
      test_cases = product(num_meshes, num_verts, num_faces, devices)
      for case in test_cases:
          n, v, f, d = case
          kwargs_list.append(
              {
                  "num_meshes": n,
                  "num_verts": v,
                  "num_faces": f,
                  "device": d,
              }
          )
  
      benchmark(
          TestLaplacianSmoothing.laplacian_smoothing_with_init,
          "MESH_LAPLACIAN_SMOOTHING",
          kwargs_list,
          warmup_iters=1,
      )
  
imports: from itertools import product
  import torch
  from fvcore.common.benchmark import benchmark
  from tests.test_mesh_laplacian_smoothing import TestLaplacianSmoothing
label_desc: Use image node_id 1 for calling a global function with example usage: bm_mesh_laplacian_smoothing() without return types