class_docstr: Implementation of the Communicator class via torch.distributed. Use this
  communicator to communicate between different processes, potentially,
  running on different nodes.
code: def initialize(cls, rank, world_size, init_ttp=False):
      import os
  
      if os.name == "nt":
          raise OSError(
              "Multiprocessing is not supported on Windows. "
              + "Please initialize CrypTen via crypten.init_thread() instead."
          )
  
      # set default arguments for communicator:
      randomized_path = "crypten-".join(
          random.choice(string.ascii_letters) for i in range(10)
      )
      default_args = {
          "DISTRIBUTED_BACKEND": "gloo",
          "RENDEZVOUS": f"file:///tmp/{randomized_path}",
          "WORLD_SIZE": world_size,
          "RANK": rank,
      }
      for key, val in default_args.items():
          if key not in os.environ:
              os.environ[key] = str(val)
  
      cls.instance = DistributedCommunicator(init_ttp=init_ttp)
  
imports: import logging
  import os
  import pickle
  import random
  import string
  import numpy
  import torch
  import torch.distributed
  from crypten.common import serial
  from torch.distributed import ReduceOp
  from .communicator import _logging, Communicator
label_desc: Use image node_id 3 for calling the DistributedCommunicator obj's underlying member method code with example usage: obj.initialize(cls, rank, world_size, init_ttp) without return types