             (losses.GMSDLoss(), (2, 1, 1)),
             # TODO Make sure these output dimensions are correct
             (losses.LInfNorm(), (2, 1, 1))]
  _IDS = ["GeneralizedLoss", "GradientLoss", "GMSDLoss", "LInfNorm"]
  _IDS = ["{}[{}]".format(loss, get_backend().upper()) for loss in _IDS]
  
  
  @pytest.mark.parametrize(["loss_func", "output_shape"], _PARAMS, ids=_IDS)
  def test_loss_output(loss_func, output_shape):
      """ Basic shape tests for loss functions. """
 
</s> remove _LWIDS = ["{}[{}]".format(loss, get_backend().upper()) for loss in _LWIDS]
 </s> add _LWIDS = [f"{loss}[{get_backend().upper()}]" for loss in _LWIDS] </s> remove _IDS = ["partial={}|bias={}[{}]".format(v[0], v[1], get_backend().upper())
        for v in _VALUES]
 </s> add _IDS = [f"partial={v[0]}|bias={v[1]}[{get_backend().upper()}]" for v in _VALUES] </s> remove         ret = list()
 </s> add         ret = [] </s> remove         retval = list()
 </s> add         retval = []
_IDS = [f"{loss}[{get_backend().upper()}]" for loss in _IDS]
             (losses.GMSDLoss(), (2, 1, 1)),
             # TODO Make sure these output dimensions are correct
             (losses.LInfNorm(), (2, 1, 1))]
  _IDS = ["GeneralizedLoss", "GradientLoss", "GMSDLoss", "LInfNorm"]
_IDS = [f"{loss}[{get_backend().upper()}]" for loss in _IDS]
  
  
  @pytest.mark.parametrize(["loss_func", "output_shape"], _PARAMS, ids=_IDS)
  def test_loss_output(loss_func, output_shape):
      """ Basic shape tests for loss functions. """
keep keep keep keep replace keep keep keep keep keep
 Update all Keras Imports to be conditional (#1214)

* Remove custom keras importer

* first round keras imports fix

* launcher.py: Remove KerasFinder references

* 2nd round keras imports update (lib and extract)

* 3rd round keras imports update (train)

* remove KerasFinder from tests

* 4th round keras imports update (tests) 
tests/lib/model/losses_test.py