code: def test_memory_benchmark(
      pass_bytes: int,
      layer_bytes: int,
      num_repeats: int,
      forward_only: bool,
  ) -> None:
      """Tests CUDA memory benchmarks on a dummy layer.
  
      Args:
          pass_bytes: number of bytes allocated during a forward or forward/backward pass
          layer_bytes: number of bytes allocated for the layer
          num_repeats: number of repeats for each benchmark
          forward_only: whether to benchmark forward passes or forward/backward passes
      """
      device = torch.device("cuda:0")
  
      true_pass_memory = get_actual_memory_allocated(
          pass_bytes, device=device
      )
      true_layer_memory = get_actual_memory_allocated(
          layer_bytes, device=device
      )
  
      # reset memory stats and ensure there is no memory leakage
      assert reset_peak_memory_stats(device).cur_mem == 0
  
      runtime, memory_stats = run_layer_benchmark(
          num_repeats=num_repeats,
          forward_only=forward_only,
          create_layer=FakeLayer,
          layer_memory=layer_bytes,
          pass_memory=pass_bytes,
      )
  
      assert memory_stats["layer"] == true_layer_memory
      assert (
          memory_stats["max_memory"]
          == true_layer_memory + (2 - forward_only) * true_pass_memory
      )
  
      # reset memory stats and ensure there is no memory leakage
      assert reset_peak_memory_stats(device).cur_mem == 0
  
file_path: opacus/benchmarks/tests/test_benchmark_layer.py
imports: import math
  import time
  import pytest
  import torch
  import torch.nn
  from helpers import get_actual_memory_allocated, get_n_byte_tensor, skipifnocuda
  from benchmarks.benchmark_layer import run_layer_benchmark
  from benchmarks.layers import Layer
  from benchmarks.utils import reset_peak_memory_stats
label_desc: Use image node_id 2 for calling a global function with example usage: test_memory_benchmark(pass_bytes, layer_bytes, num_repeats, forward_only) without return types