code: def test_wrap_model(self):
      mb_size = 1 * 2 * 2 * 3 * 5
      num_classes = 11
      torch.manual_seed(1234 + DistributedHelper.rank)
      mb_x = torch.randn((mb_size, 32))
      mb_y = torch.randint(0, num_classes, (mb_size,))
      mb_t = torch.full((mb_size,), 1)
      model = SimpleMLP(num_classes=num_classes, input_size=32)
      model = as_multitask(model, "classifier")
      self.assertIsInstance(model, Module)
  
      device = DistributedHelper.make_device()
  
      if device.type == "cuda":
          # Additional test: must raise an error if the model
          # is not already in the correct device
          with self.assertRaises(Exception):
              model_wrapped = DistributedHelper.wrap_model(model)
  
      model = model.to(device)
  
      model_wrapped = DistributedHelper.wrap_model(model)
      self.assertIsInstance(model_wrapped, DistributedDataParallel)
      self.assertNotIsInstance(model, DistributedDataParallel)
  
      device = DistributedHelper.make_device()
      mb_x = mb_x.to(device)
      mb_y = mb_y.to(device)
      mb_t = mb_t.to(device)
      model = model.to(device)
  
      model.eval()
      model_wrapped.eval()
  
      benchmark = dataset_benchmark(
          [
              _make_taskaware_tensor_classification_dataset(
                  mb_x, mb_y, mb_t, task_labels=mb_t.tolist()
              )
          ],
          [
              _make_taskaware_tensor_classification_dataset(
                  mb_x, mb_y, mb_t, task_labels=mb_t.tolist()
              )
          ],
      )
  
      avalanche_model_adaptation(model, benchmark.train_stream[0])
  
      with torch.no_grad():
          mb_out1 = model(mb_x, mb_t).detach()
          self.assertEqual(mb_out1.device, device)
          self.assertSequenceEqual(
              [mb_size, num_classes], mb_out1.shape
          )
  
          mb_out2 = model_wrapped(mb_x, mb_t).detach()
          self.assertEqual(mb_out2.device, device)
          self.assertSequenceEqual(
              [mb_size, num_classes], mb_out2.shape
          )
  
          self.assertTrue(torch.equal(mb_out1, mb_out2))
  
          mb_out_all = DistributedHelper.cat_all(mb_out2)
  
          start_idx = mb_size * DistributedHelper.rank
          end_idx = start_idx + mb_size
  
          self.assertTrue(
              torch.equal(mb_out1, mb_out_all[start_idx:end_idx])
          )
  
      self.assertTrue(
          model is DistributedHelper.unwrap_model(model_wrapped)
      )
  
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 3 for calling the DistributedHelperTests obj's underlying member method code with example usage: obj.test_wrap_model() without return types