code: def bm_graph_conv() -> None:
      backends = ["cpu"]
      if torch.cuda.is_available():
          backends.append("cuda")
  
      kwargs_list = []
      gconv_dim = [128, 256]
      num_meshes = [32, 64]
      num_verts = [100]
      num_faces = [1000]
      directed = [False, True]
      test_cases = product(
          gconv_dim,
          num_meshes,
          num_verts,
          num_faces,
          directed,
          backends,
      )
      for case in test_cases:
          g, n, v, f, d, b = case
          kwargs_list.append(
              {
                  "gconv_dim": g,
                  "num_meshes": n,
                  "num_verts": v,
                  "num_faces": f,
                  "directed": d,
                  "backend": b,
              }
          )
      benchmark(
          TestGraphConv.graph_conv_forward_backward,
          "GRAPH CONV",
          kwargs_list,
          warmup_iters=1,
      )
  
imports: from itertools import product
  import torch
  from fvcore.common.benchmark import benchmark
  from tests.test_graph_conv import TestGraphConv
label_desc: Use image node_id 1 for calling a global function with example usage: bm_graph_conv() without return types