code: def sync_parameters(model: torch.nn.Module) -> bool:
      """
      Sync all parameters across all workers are the same.
  
      Always returns True, or raises an AssertionError if there was a failure.
  
      :param model: A pytorch model.
      :return: always True
      """
      if not is_distributed():
          # if things aren't distributed, of course things are in sync
          return True
  
      # sync all the parameters
      with torch.no_grad():
          for p in model.parameters():
              if not is_primary_worker():
                  # zero out parameters on all workers EXCEPT the primary worker
                  p.data.zero_()
              # sum the parameters across all workers, resulting in everyone having
              # the parameters of the primary worker
              dist.all_reduce(p.data, dist.ReduceOp.SUM)
  
      # double check everything synced correctly
      norm2 = sum(
          (p.data**2).sum().float().item() for p in model.parameters()
      )
      all_versions = all_gather_list(norm2)
      if not all(n == norm2 for n in all_versions):
          raise AssertionError(
              "Some models parameters were out of sync. Got the following norms: {}".format(
                  " ".join(str(x) for x in all_versions)
              )
          )
  
      return True
  
file_path: ParlAI/parlai/utils/distributed.py
imports: import builtins
  import copy
  import os
  import pickle
  import contextlib
  import subprocess
  import socket
  import parlai.utils.logging
label_desc: Use image node_id 8 for calling a global function with example usage: sync_parameters(model) and returns: True, True