code: def test_memory_benchmark_strict(
      pass_bytes: int,
      layer_bytes: int,
      num_repeats: int,
      forward_only: bool,
  ) -> None:
      """Tests CUDA memory benchmarks on a dummy layer by predicting each measurement
      using the CUDA memory block size.
  
      Notes:
          During the experiments included in the paper, CUDA memory is allocated in
          blocks, where block sizes vary across kernels. New CUDA memory is allocated
          for each new tensor. This test will fail under a different allocation
          scheme.
  
      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")
  
      # find the block size by creating a tensor of size 1 byte
      tiny_tensor = get_n_byte_tensor(1, device=device)
      BLOCK_SIZE = torch.cuda.max_memory_allocated(device)
      del tiny_tensor
  
      num_blocks_pass = math.ceil(pass_bytes / BLOCK_SIZE)
      num_blocks_layer = math.ceil(layer_bytes / BLOCK_SIZE)
  
      # 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"] == num_blocks_layer * BLOCK_SIZE
      assert (
          memory_stats["max_memory"]
          == (num_blocks_layer + (2 - forward_only) * num_blocks_pass)
          * BLOCK_SIZE
      )
  
      # 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 3 for calling a global function with example usage: test_memory_benchmark_strict(pass_bytes, layer_bytes, num_repeats, forward_only) without return types