code: def validate_benchmark(
      measurements, final_loss, args, check_regression
  ):
      """Validate the measurments against the golden benchmark config."""
  
      golden_data = oss_mnist.get_golden_real_stats()
  
      max_memory = -1.0
      rank = dist.get_rank()
      if not args.cpu:
          # TODO(anj-s): Check if we need to synchronize before we caculate total training time.
          torch.cuda.synchronize(rank)
          max_memory = torch.cuda.max_memory_allocated(rank) / 2**20
          logging.info(f"[{rank}] : Peak memory {max_memory:.1f}MiB")
  
      measurements.sort()
      median = measurements[len(measurements) // 2]
      # Compute the median and median of absolute differences img per second.
      abs_diff = list(map(lambda x: abs(x - median), measurements))
      abs_diff.sort()
      mad = abs_diff[len(measurements) // 2] if args.epochs > 2 else -1
  
      # TODO(anj-s): Add a debug flag to perform the above calculation only when required.
      logging.info(
          f"[{rank}] : Median speed: {median:.2f} +/- {mad:.2f}"
      )
  
      if check_regression and rank == 0:
          assert median + 8.0 * mad > golden_data["reference_speed"], (
              f"Speed regression detected: "
              f"{median + 8.0 * mad} vs.  {golden_data['reference_speed']}"
          )
          assert max_memory < 1.05 * golden_data["reference_memory"], (
              f"Memory use regression detected: "
              f"{max_memory} vs. {1.05* golden_data['reference_memory']}"
          )
          # any min_loss < than golden + epsilon is OK.
          assert (
              cast(float, final_loss) - golden_data["reference_loss"]
              < 1e-2
          ), (
              f"Loss regression detected: "
              f"{final_loss} vs. {golden_data['reference_loss']}"
          )
          logging.info("[Regression Test] VALID")
  
imports: import argparse
  from enum import Enum
  import importlib
  import logging
  import tempfile
  import time
  from typing import Any, List, Optional, cast
  from golden_configs import oss_mnist
  import numpy
  import torch
  import torch.autograd.profiler
  from torch.cuda.amp import GradScaler
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import BatchSampler, DataLoader, Sampler
  from torch.utils.data.distributed import DistributedSampler
  from torchvision.datasets import MNIST
  from torchvision.transforms import Compose, Resize, ToTensor
  from benchmarks.datasets.mnist import setup_cached_mnist
  from fairscale.nn.data_parallel import ShardedDataParallel
  from fairscale.optim import OSS
  from fairscale.optim.grad_scaler import ShardedGradScaler
label_desc: Use image node_id 3 for calling a global function with example usage: validate_benchmark(measurements, final_loss, args, check_regression) without return types