code: def test_gather_all_objects(self):
      ts = torch.full((10,), DistributedHelper.rank, dtype=torch.long)
  
      device = DistributedHelper.make_device()
      ts = ts.to(device)
  
      my_object = {"a": DistributedHelper.rank, "b": ts}
      all_objects = DistributedHelper.gather_all_objects(my_object)
      self.assertIsInstance(all_objects, list)
      self.assertEqual(DistributedHelper.world_size, len(all_objects))
  
      for rank in range(DistributedHelper.world_size):
          expect = {
              "a": rank,
              "b": torch.full((10,), rank, dtype=torch.long).tolist(),
          }
  
          self.assertEqual(device, all_objects[rank]["b"].device)
          all_objects[rank]["b"] = all_objects[rank]["b"].tolist()
          self.assertEqual(expect, all_objects[rank])
  
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 5 for calling the DistributedHelperTests obj's underlying member method code with example usage: obj.test_gather_all_objects() without return types