code: def test_broadcast_tensor_or_objects(self):
      ts = torch.full((10,), DistributedHelper.rank, dtype=torch.long)
      DistributedHelper.broadcast(ts)
      self.assertTrue(
          torch.equal(ts, torch.zeros((10,), dtype=torch.long))
      )
  
      device = DistributedHelper.make_device()
      ts = ts.to(device)
  
      my_object = {"a": DistributedHelper.rank, "b": ts}
      my_object_from_main = DistributedHelper.broadcast_object(
          my_object
      )
  
      expect = {
          "a": 0,
          "b": torch.full((10,), 0, dtype=torch.long).tolist(),
      }
  
      self.assertEqual(device, my_object_from_main["b"].device)
      my_object_from_main["b"] = my_object_from_main["b"].tolist()
      self.assertEqual(expect, my_object_from_main)
  
imports: import os
  import random
  import shutil
  import tempfile
  import time
  import unittest
  import numpy
  import torch
  import torch.distributed
  from torch.nn import Module
  from torch.nn.parallel import DistributedDataParallel
  from avalanche.benchmarks.scenarios.deprecated.generators import dataset_benchmark
  from avalanche.benchmarks.utils.classification_dataset import _make_taskaware_tensor_classification_dataset
  from avalanche.distributed import DistributedHelper
  from avalanche.distributed.distributed_helper import RollingSeedContext, BroadcastSeedContext
  from avalanche.models import SimpleMLP, as_multitask
  from avalanche.models.utils import avalanche_model_adaptation
  from avalanche.training.determinism.rng_manager import RNGManager
  from tests.distributed.distributed_test_utils import check_skip_distributed_slow_test, check_skip_distributed_test, suppress_dst_tests_output, common_dst_tests_setup
label_desc: Use image node_id 4 for calling the DistributedHelperTests obj's underlying member method code with example usage: obj.test_broadcast_tensor_or_objects() without return types