  
  from .normalization import *
  if get_backend() == "amd":
      from . import losses_plaid as losses
  else:
      from . import losses_tf as losses
      from . import optimizers_tf as optimizers
 
</s> remove from lib.model import losses
 </s> add from lib.model import losses, optimizers </s> remove                  "by zero' errors. Generally this option should be left at default value, however "
                 "if you are getting 'NaN' loss values, and have been unable to resolve the issue "
                 "any other way (for example, increasing batch size, or lowering learning rate, "
                 "then raising the epsilon can lead to a more stable model. It may, however, come "
                 "at the cost of slower training and a less accurate final result.\n"
 </s> add                  "by zero' errors. Unless you are using the AdaBelief Optimizer, then Generally "
                 "this option should be left at default value, For AdaBelief, setting this to "
                 "around '-16' should work.\n"
                 "In all instances if you are getting 'NaN' loss values, and have been unable to "
                 "resolve the issue any other way (for example, increasing batch size, or "
                 "lowering learning rate), then raising the epsilon can lead to a more stable "
                 "model. It may, however, come at the cost of slower training and a less accurate "
                 "final result.\n" </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)
    from . import optimizers_plaid as optimizers
  
  from .normalization import *
  if get_backend() == "amd":
      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 keep keep keep
 lib.model - Add AdaBelief Optimizer 
lib/model/__init__.py