          """ Build and return Optimizer """
          opt_kwargs = dict(lr=lr, beta_1=beta_1, beta_2=beta_2)
          if (self.config.get("clipnorm", False) and
                  keras.backend.backend() != "plaidml.keras.backend"):
              # 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
              # See: https://github.com/plaidml/plaidml/issues/228
              # Workaround by simply removing it.
              # TODO: Remove this as soon it is fixed in PlaidML.
              opt_kwargs["clipnorm"] = 1.0
          logger.debug("Optimizer kwargs: %s", opt_kwargs)
 
</s> remove             not ``None`` otherwised it is set to ``False``.
 </s> add             not ``None`` otherwise it is set to ``False``. </s> remove def test_blocks(use_subpixel, use_icnr_init, use_convaware_init, use_reflect_padding):
 </s> add def test_blocks(use_icnr_init, use_convaware_init, use_reflect_padding): </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     use_subpixel: bool, Optional
        ``True`` if sub-pixel up-scaling layer should be used instead of pixel shuffler for
        up-scaling. This option is deprecated as sub-pixel up-scaling is Nvidia only, but is kept
        for legacy models. Default: ``False``
 </s> add  </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,
            # 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
          """ Build and return Optimizer """
          opt_kwargs = dict(lr=lr, beta_1=beta_1, beta_2=beta_2)
          if (self.config.get("clipnorm", False) and
                  keras.backend.backend() != "plaidml.keras.backend"):
            # 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
            # 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
            # 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
              # See: https://github.com/plaidml/plaidml/issues/228
              # Workaround by simply removing it.
              # TODO: Remove this as soon it is fixed in PlaidML.
              opt_kwargs["clipnorm"] = 1.0
          logger.debug("Optimizer kwargs: %s", opt_kwargs)
keep keep keep keep replace replace replace keep keep keep keep keep
 Remove subpixel upscaling option (#1024) 
plugins/train/model/_base.py