code: def verify_peak_memory(rank, golden_config, std_dev):
      logging.debug(
          "Peak allocated bytes on cuda:0: {:1d}".format(
              torch.cuda.memory_stats(rank)["allocated_bytes.all.peak"]
          )
      )
      current_device_usage = torch.cuda.memory_stats(rank)[
          "allocated_bytes.all.peak"
      ]
      golden_ref = golden_config["peak_mem_usage"][rank]
      if not current_device_usage < golden_ref * std_dev:
          raise RuntimeError(
              "Peak memory usage for cuda device {:d} is {:d} which"
              "is less than golden reference value of {:d}".format(
                  rank, current_device_usage, golden_ref
              )
          )
  
imports: from collections import defaultdict
  import gc
  import logging
  import math
  import time
  import torch
  import torch.distributed
  from torch.distributed import rpc
  from torch.nn.parallel import DistributedDataParallel
  import utils
  from benchmarks.golden_configs.lm_wikitext2 import Pipe
  from fairscale.fair_dev.testing.testing import dist_init
  from fairscale.nn import Pipe
  from fairscale.nn.model_parallel import initialize_model_parallel
label_desc: Use image node_id 7 for calling a global function with example usage: verify_peak_memory(rank, golden_config, std_dev) without return types