code: def bm_blending() -> None:
      devices = ["cuda"]
      kwargs_list = []
      num_meshes = [8]
      image_size = [64, 128, 256]
      faces_per_pixel = [2, 50, 100]
      backend = ["pytorch", "custom"]
      test_cases = product(
          num_meshes, image_size, faces_per_pixel, devices, backend
      )
  
      for case in test_cases:
          n, s, k, d, b = case
          kwargs_list.append(
              {
                  "num_meshes": n,
                  "image_size": s,
                  "faces_per_pixel": k,
                  "device": d,
                  "backend": b,
              }
          )
  
      benchmark(
          TestBlending.bm_sigmoid_alpha_blending,
          "SIGMOID_ALPHA_BLENDING_PYTORCH",
          kwargs_list,
          warmup_iters=1,
      )
  
      kwargs_list = [
          case for case in kwargs_list if case["backend"] == "pytorch"
      ]
      benchmark(
          TestBlending.bm_softmax_blending,
          "SOFTMAX_BLENDING_PYTORCH",
          kwargs_list,
          warmup_iters=1,
      )
  
      kwargs_list = []
      faces_per_pixel = [2, 10]
      backend = ["pytorch"]
      test_cases = product(
          num_meshes, image_size, faces_per_pixel, devices, backend
      )
      for case in test_cases:
          n, s, k, d, b = case
          kwargs_list.append(
              {
                  "num_meshes": n,
                  "image_size": s,
                  "faces_per_pixel": k,
                  "device": d,
                  "backend": b,
              }
          )
      benchmark(
          TestBlending.bm_splatter_blending,
          "SPLATTER_BLENDING_PYTORCH",
          kwargs_list,
          warmup_iters=1,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: bm_blending() without return types