def METHOD_NAME(self, validate_and_fix):
    """Tests before training method with different scenarios"""
    #model_false = MagicMock()
    opt_false = MagicMock()
    loader_false = MagicMock()
    model = Module()
    model_wrapper = MagicMock(spec=TorchModel)
    model_wrapper.model = model
    opt = Adam([torch.zeros([2, 4])])
    loader = DataLoader(TestDPController.DS())
    optim_wrapper = NativeTorchOptimizer(model_wrapper, opt)
    with self.assertRaises(FedbiomedDPControllerError):
        self.dpl.before_training(opt_false, loader)
    with self.assertRaises(FedbiomedDPControllerError):
        self.dpl.before_training(optim_wrapper, loader_false)
    validate_and_fix.return_value = model
    self.privacy_engine_make_private.side_effect = Exception
    with self.assertRaises(FedbiomedDPControllerError):
        self.dpl.before_training(optim_wrapper, loader)
    self.privacy_engine_make_private.side_effect = None
    self.privacy_engine_make_private.reset_mock()
    self.dpl.before_training(optim_wrapper, loader)
    self.privacy_engine_make_private.assert_called_once_with(
                                                             module=model,
                                                             optimizer=opt,
                                                             data_loader=loader,
                                                             noise_multiplier=self.dp_args_l.get('sigma'),
                                                             max_grad_norm=self.dp_args_l.get('clip'))