code: def bm_sample_points() -> None:
      backend = ["cpu"]
      if torch.cuda.is_available():
          backend.append("cuda:0")
      kwargs_list = []
      num_meshes = [2, 10, 32]
      num_verts = [100, 1000]
      num_faces = [300, 3000]
      num_samples = [5000, 10000]
      test_cases = product(
          num_meshes, num_verts, num_faces, num_samples, backend
      )
      for case in test_cases:
          n, v, f, s, b = case
          kwargs_list.append(
              {
                  "num_meshes": n,
                  "num_verts": v,
                  "num_faces": f,
                  "num_samples": s,
                  "device": b,
              }
          )
      benchmark(
          TestSamplePoints.sample_points_with_init,
          "SAMPLE_MESH",
          kwargs_list,
          warmup_iters=1,
      )
  
imports: from itertools import product
  import torch
  from fvcore.common.benchmark import benchmark
  from tests.test_sample_points_from_meshes import TestSamplePoints
label_desc: Use image node_id 1 for calling a global function with example usage: bm_sample_points() without return types