class_docstr: Implementation of the Communicator class via torch.distributed. Use this
  communicator to communicate between different processes, potentially,
  running on different nodes.
docstr: Broadcasts the tensor to all parties.
code: def broadcast(self, input, src, group=None, batched=False):
      """Broadcasts the tensor to all parties."""
      assert dist.is_initialized(), "initialize the communicator first"
      group = self.main_group if group is None else group
      if batched:
          assert isinstance(
              input, list
          ), "batched reduce input must be a list"
          reqs = []
          for tensor in input:
              reqs.append(
                  dist.broadcast(
                      tensor.data, src, group=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"
          dist.broadcast(input.data, src, group=group)
      return input
  
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 15 for calling the DistributedCommunicator obj's underlying member method code with example usage: obj.broadcast(input, src, group, batched) and returns: input