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 __init__(self, init_ttp=False):
      # no need to do anything if we already initialized the communicator:
      if not dist.is_initialized():
          # get configuration variables from environmens:
          for key in [
              "distributed_backend",
              "rendezvous",
              "world_size",
              "rank",
          ]:
              if key.upper() not in os.environ:
                  raise ValueError(
                      "Environment variable %s must be set." % key
                  )
              setattr(self, key.lower(), os.environ[key.upper()])
  
          # make sure world size and rank are integers; comms stats are reset:
          self.world_size = int(self.world_size)
          self.rank = int(self.rank)
          self.reset_communication_stats()
          self._name = f"rank{self.rank}"
  
          # logging:
          logging.info("==================")
          logging.info(
              "DistributedCommunicator with rank %d" % self.rank
          )
          logging.info("==================")
  
          # initialize process group:
          total_ws = (
              self.world_size + 1 if init_ttp else self.world_size
          )
          dist.init_process_group(
              backend=self.distributed_backend,
              init_method=self.rendezvous,
              world_size=total_ws,
              rank=self.rank,
          )
          self.ttp_group = dist.new_group(list(range(total_ws)))
          if total_ws > 1:
              self.ttp_comm_group = dist.new_group([0, total_ws - 1])
          self.main_group = dist.new_group(list(range(self.world_size)))
          self.ttp_initialized = init_ttp
          logging.info("World size = %d" % self.world_size)
  
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 1 to create a new DistributedCommunicator object from inherited base classes: Communicator with example: obj = DistributedCommunicator(init_ttp)