code: def bm_packed_to_padded() -> None:
      kwargs_list = []
      backend = ["cpu"]
      if torch.cuda.is_available():
          backend.append("cuda:0")
  
      num_meshes = [2, 10, 32]
      num_verts = [100, 1000]
      num_faces = [300, 3000]
      num_ds = [0, 1, 16]
  
      test_cases = product(
          num_meshes, num_verts, num_faces, num_ds, backend
      )
      for case in test_cases:
          n, v, f, d, b = case
          kwargs_list.append(
              {
                  "num_meshes": n,
                  "num_verts": v,
                  "num_faces": f,
                  "num_d": d,
                  "device": b,
              }
          )
      benchmark(
          TestPackedToPadded.packed_to_padded_with_init,
          "PACKED_TO_PADDED",
          kwargs_list,
          warmup_iters=1,
      )
  
      benchmark(
          TestPackedToPadded.packed_to_padded_with_init_torch,
          "PACKED_TO_PADDED_TORCH",
          kwargs_list,
          warmup_iters=1,
      )
  
imports: from itertools import product
  import torch
  from fvcore.common.benchmark import benchmark
  from tests.test_packed_to_padded import TestPackedToPadded
label_desc: Use image node_id 1 for calling a global function with example usage: bm_packed_to_padded() without return types