      return actual_output
  
  
  @pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
  def test_blocks(use_subpixel, use_icnr_init, use_convaware_init, use_reflect_padding):
      """ Test for all blocks contained within the NNBlocks Class """
      if get_backend() == "amd" and use_subpixel:
          # Subpixel upscaling does not work on plaidml so skip this test
          pytest.skip("Subpixel upscaling not supported in plaidML")
      cls_ = NNBlocks(use_subpixel=use_subpixel,
 
</s> remove     if get_backend() == "amd" and use_subpixel:
        # Subpixel upscaling does not work on plaidml so skip this test
        pytest.skip("Subpixel upscaling not supported in plaidML")
    cls_ = NNBlocks(use_subpixel=use_subpixel,
                    use_icnr_init=use_icnr_init,
 </s> add     cls_ = NNBlocks(use_icnr_init=use_icnr_init, </s> remove     """Test routine for a faceswaps neural network blocks.
 </s> add     """Test routine for faceswap neural network blocks. </s> remove             # NB: Clipnorm is ballooning VRAM usage, which is not expected behavior
            # and may be a bug in Keras/TF.
            # PlaidML has a bug regarding the clipnorm parameter
 </s> add             # NB: Clip-norm is ballooning VRAM usage, which is not expected behavior
            # and may be a bug in Keras/Tensorflow.
            # PlaidML has a bug regarding the clip-norm parameter </s> remove         if self.use_subpixel:
            var_x = SubPixelUpscaling(name="{}_subpixel".format(name),
                                      scale_factor=scale_factor)(var_x)
        else:
            var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
 </s> add         var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x) </s> remove _PARAMS = ["use_subpixel", "use_icnr_init", "use_convaware_init", "use_reflect_padding"]
 </s> add _PARAMS = ["use_icnr_init", "use_convaware_init", "use_reflect_padding"]
def test_blocks(use_icnr_init, use_convaware_init, use_reflect_padding):
      return actual_output
  
  
  @pytest.mark.parametrize(_PARAMS, _VALUES, ids=_IDS)
def test_blocks(use_icnr_init, use_convaware_init, use_reflect_padding):
      """ Test for all blocks contained within the NNBlocks Class """
      if get_backend() == "amd" and use_subpixel:
          # Subpixel upscaling does not work on plaidml so skip this test
          pytest.skip("Subpixel upscaling not supported in plaidML")
      cls_ = NNBlocks(use_subpixel=use_subpixel,
keep keep keep keep replace keep keep keep keep keep
 Remove subpixel upscaling option (#1024) 
tests/lib/model/nn_blocks_test.py