code: def bm_compute_packed_padded_meshes() -> None:
      devices = ["cpu"]
      if torch.cuda.is_available():
          devices.append("cuda")
  
      kwargs_list = []
      num_meshes = [32, 128]
      max_v = [100, 1000, 10000]
      max_f = [300, 3000, 30000]
      test_cases = product(num_meshes, max_v, max_f, devices)
      for case in test_cases:
          n, v, f, d = case
          kwargs_list.append(
              {"num_meshes": n, "max_v": v, "max_f": f, "device": d}
          )
      benchmark(
          TestMeshes.compute_packed_with_init,
          "COMPUTE_PACKED",
          kwargs_list,
          warmup_iters=1,
      )
      benchmark(
          TestMeshes.compute_padded_with_init,
          "COMPUTE_PADDED",
          kwargs_list,
          warmup_iters=1,
      )
  
imports: from itertools import product
  import torch
  from fvcore.common.benchmark import benchmark
  from tests.test_meshes import TestMeshes
label_desc: Use image node_id 1 for calling a global function with example usage: bm_compute_packed_padded_meshes() without return types