      from . import losses_plaid as losses
      from . import optimizers_plaid as optimizers
  else:
      from . import losses_tf as losses
 
</s> remove from lib.model import losses
 </s> add from lib.model import losses, optimizers </s> remove     _test_optimizer(k_optimizers.Adam(), target=0.6)
    _test_optimizer(k_optimizers.Adam(decay=1e-3), target=0.6)
 </s> add     _test_optimizer(k_optimizers.Adam(), target=0.5)
    _test_optimizer(k_optimizers.Adam(decay=1e-3), target=0.5)


@pytest.mark.parametrize("dummy", [None], ids=[get_backend().upper()])
def test_adabelief(dummy):  # pylint:disable=unused-argument
    """ Test for custom Adam optimizer """
    _test_optimizer(optimizers.AdaBelief(), target=0.6) </s> remove         optimizers = {"adam": Adam, "nadam": Nadam, "rms-prop": RMSprop}
        self._optimizer = optimizers[optimizer]

        base_kwargs = {"adam": dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon),
                       "nadam": dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon),
                       "rms-prop": dict(epsilon=epsilon)}
        self._kwargs = base_kwargs[optimizer]
 </s> add         valid_optimizers = {"adabelief": (optimizers.AdaBelief,
                                          dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                            "adam": (Adam, dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                            "nadam": (Nadam, dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                            "rms-prop": (RMSprop, dict(epsilon=epsilon))}
        self._optimizer, self._kwargs = valid_optimizers[optimizer]
    from . import optimizers_tf as optimizers
      from . import losses_plaid as losses
      from . import optimizers_plaid as optimizers
  else:
      from . import losses_tf as losses
    from . import optimizers_tf as optimizers
keep keep keep add
 lib.model - Add AdaBelief Optimizer 
lib/model/__init__.py