class_docstr: Implementation of the Communicator class via torch.distributed. Use this
  communicator to communicate between different processes, potentially,
  running on different nodes.
docstr: Reduces the input data across all parties; all get the final result.
code: def all_reduce(self, input, op=ReduceOp.SUM, batched=False):
      """Reduces the input data across all parties; all get the final result."""
      assert dist.is_initialized(), "initialize the communicator first"
  
      if batched:
          assert isinstance(
              input, list
          ), "batched reduce input must be a list"
          reqs = []
          result = [x.clone() for x in input]
          for tensor in result:
              reqs.append(
                  dist.all_reduce(
                      tensor.data,
                      op=op,
                      group=self.main_group,
                      async_op=True,
                  )
              )
          for req in reqs:
              req.wait()
      else:
          assert torch.is_tensor(
              input.data
          ), "unbatched input for reduce must be a torch tensor"
          result = input.clone()
          dist.all_reduce(result.data, op=op, group=self.main_group)
      return result
  
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 12 for calling the DistributedCommunicator obj's underlying member method code with example usage: obj.all_reduce(input, op, batched) and returns: result