code: def test_runtime_benchmark(
      duration: float, num_repeats: int, forward_only: bool
  ) -> None:
      """Tests runtime benchmarks on a dummy layer.
  
      Args:
          duration: duration (seconds) to test runtime benchmarks for
          num_repeats: number of repeats for each benchmark
          forward_only: whether to benchmark forward passes or forward/backward passes
      """
      runtime, memory_stats = run_layer_benchmark(
          num_repeats=num_repeats,
          forward_only=forward_only,
          create_layer=FakeLayer,
          runtime=duration,
      )
      # account for small variations
      assert abs(runtime - ((2 - forward_only) * duration)) < 0.01
  
      # check that no memory allocation took place
      assert (
          memory_stats["layer"] == 0 and memory_stats["max_memory"] == 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 1 for calling a global function with example usage: test_runtime_benchmark(duration, num_repeats, forward_only) without return types