code: def log_number_of_parameters(model):
      num_params = reduce(
          operator.add,
          (reduce(operator.mul, x.size()) for x in model.parameters()),
      )
      if hasattr(model, "group"):
          total = torch.Tensor([num_params])
          if torch.cuda.is_available():
              total = total.cuda()
          torch.distributed.all_reduce(total, group=model.group)
          print(
              f"training model, #params = {num_params/10**6}M, group: {model.group.rank()}, grank:"
              f" {torch.distributed.get_rank()}, sizes {model.group.size()}"
          )
          torch.distributed.barrier()
          if model.group.rank() == 0:
              print(f"total #params = {total.item()}")
      else:
          print(f"training model, #params = {num_params/10**6}M")
  
imports: import argparse
  from collections import defaultdict
  from functools import reduce
  import gc
  import logging
  import math
  import operator
  import time
  from datasets.wikitext2_data import get_real_dataloaders
  from datasets.wikitext2_data import get_synthetic_dataloaders
  from models import transformer_lm
  import numpy
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim import Adam
  from benchmarks.golden_configs.lm_wikitext2 import FSDP
  from fairscale.nn import auto_wrap, default_auto_wrap_policy, enable_wrap
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 7 for calling a global function with example usage: log_number_of_parameters(model) without return types