code: def test_check_equal_tensors(self):
      if (
          DistributedHelper.world_size == 1
          and DistributedHelper.get_device_id() >= 0
      ):
          self.skipTest(
              "When using CUDA, there must be at "
              "least two processes to run this test"
          )
      torch.manual_seed(1234)
      ts = torch.randn((100,))
      DistributedHelper.check_equal_tensors(ts)
  
      torch.manual_seed(1234 + DistributedHelper.rank)
      ts = torch.randn((100,))
      with self.assertRaises(Exception):
          DistributedHelper.check_equal_tensors(ts)
  
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 13 for calling the DistributedHelperTests obj's underlying member method code with example usage: obj.test_check_equal_tensors() without return types