code: def _test_outputs(self, module, clf_name):
      test_input = torch.rand(10, 3, 32, 32)
      test_input = test_input.to(self.device)
      module_singletask = copy.deepcopy(module)
      module_multitask = as_multitask(module, clf_name)
      module_multitask = module_multitask.to(self.device)
      module_singletask = module_singletask.to(self.device)
  
      # Put in eval mode to deactivate dropouts
      module_singletask.eval()
      module_multitask.eval()
  
      # disable masking to check output equivalence
      module_multitask.classifier.masking = False
      out_single_task = module_singletask(test_input)
      out_multi_task = module_multitask(test_input, task_labels=0)
      self.assertTrue(torch.equal(out_single_task, out_multi_task))
  
file_path: avalanche/tests/models/test_helper_method.py
imports: import copy
  import unittest
  import torch
  from torch.optim import SGD
  from avalanche.models import SimpleCNN, SimpleMLP, as_multitask
  from avalanche.models.dynamic_modules import MultiTaskModule, MultiHeadClassifier
  from avalanche.training.plugins import LwFPlugin, EWCPlugin
  from avalanche.training.supervised import Naive
  from tests.unit_tests_utils import common_setups, get_fast_benchmark, get_device
label_desc: Use image node_id 6 for calling the ConversionMethodTests obj's underlying member method code with example usage: obj._test_outputs(module, clf_name) without return types