          if use_instance_norm:
              var_x = InstanceNormalization(name="{}_instancenorm".format(name))(var_x)
          if not res_block_follows:
              var_x = LeakyReLU(0.1, name="{}_leakyrelu".format(name))(var_x)
          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)
          return var_x
  
      # <<< DFaker Model Blocks >>> #
      def res_block(self, input_tensor, filters, kernel_size=3, padding="same", **kwargs):
          """ Residual block.
 
</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         self.use_subpixel = use_subpixel
 </s> add  </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 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 _PARAMS = ["use_subpixel", "use_icnr_init", "use_convaware_init", "use_reflect_padding"]
 </s> add _PARAMS = ["use_icnr_init", "use_convaware_init", "use_reflect_padding"]
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
          if use_instance_norm:
              var_x = InstanceNormalization(name="{}_instancenorm".format(name))(var_x)
          if not res_block_follows:
              var_x = LeakyReLU(0.1, name="{}_leakyrelu".format(name))(var_x)
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
        var_x = PixelShuffler(name="{}_pixelshuffler".format(name), size=scale_factor)(var_x)
          return var_x
  
      # <<< DFaker Model Blocks >>> #
      def res_block(self, input_tensor, filters, kernel_size=3, padding="same", **kwargs):
          """ Residual block.
keep keep keep keep replace replace replace replace replace keep keep keep keep keep
 Remove subpixel upscaling option (#1024) 
lib/model/nn_blocks.py