          logger.debug('self.upscale_ratio: %r', self.upscale_ratio)
  
      def build(self):
          self._detail_level_setup()
          # monkey patch-in nn_blocks
          self.blocks.upscale2x_hyb = types.MethodType(upscale2x_hyb, self.blocks)
          self.blocks.upscale2x_fast = types.MethodType(upscale2x_fast, self.blocks)
          super().build()
  
      def add_networks(self):
          """ Add the DeLight model weights """
          logger.debug("Adding networks")
 
</s> remove     def compile_predictors(self, **kwargs):
 </s> add     def compile_predictors(self, **kwargs):  # pylint: disable=arguments-differ </s> remove # [P] TODO Move upscale2x_hyb to nnblocks.py (after testing)
# <<< DeLight Model Blocks >>> #
def upscale2x_hyb(self, inp, filters, kernel_size=3, padding='same',
                  sr_ratio=0.5, scale_factor=2, interpolation='bilinear',
                  res_block_follows=False, **kwargs):
    """Hybrid Upscale Layer"""
    name = self._get_name("upscale2x_hyb")
    var_x = inp

    sr_filters = int(filters * sr_ratio)
    upscale_filters = filters - sr_filters

    var_x_sr = self.upscale(var_x, upscale_filters, kernel_size=kernel_size,
                            padding=padding, scale_factor=scale_factor,
                            res_block_follows=res_block_follows, **kwargs)
    if upscale_filters > 0:
        var_x_us = self.conv2d(var_x, upscale_filters,  kernel_size=3, padding=padding,
                               name="{}_conv2d".format(name), **kwargs)
        var_x_us = UpSampling2D(size=(scale_factor, scale_factor), interpolation=interpolation,
                                name="{}_upsampling2D".format(name))(var_x_us)
        var_x = Concatenate(name="{}_concatenate".format(name))([var_x_sr, var_x_us])
    else:
        var_x = var_x_sr

    return var_x


def upscale2x_fast(self, inp, filters, kernel_size=3, padding='same',
                   sr_ratio=0.5, scale_factor=2, interpolation='bilinear',
                   res_block_follows=False, **kwargs):
    """Fast Upscale Layer"""
    name = self._get_name("upscale2x_fast")
    var_x = inp

    var_x2 = self.conv2d(var_x, filters,  kernel_size=3, padding=padding,
                         name="{}_conv2d".format(name), **kwargs)
    var_x2 = UpSampling2D(size=(scale_factor, scale_factor), interpolation=interpolation,
                          name="{}_upsampling2D".format(name))(var_x2)

    var_x1 = self.upscale(var_x, filters, kernel_size=kernel_size,
                          padding=padding, scale_factor=scale_factor,
                          res_block_follows=res_block_follows, **kwargs)
    var_x = Add()([var_x2, var_x1])
    return var_x


 </s> add  </s> remove             self.upscale_ratio = {
                128: 2,
                256: 4,
                384: 6
                }[self.config["output_size"]]
 </s> add             self.upscale_ratio = {128: 2,
                                  256: 4,
                                  384: 6}[self.config["output_size"]] </s> remove     Acknowledgements:
    kvrooman for numrious insights and invaluable aid
 </s> add     Acknowledgments:
    kvrooman for numerous insights and invaluable aid
          logger.debug('self.upscale_ratio: %r', self.upscale_ratio)
  
      def build(self):
          self._detail_level_setup()



          super().build()
  
      def add_networks(self):
          """ Add the DeLight model weights """
          logger.debug("Adding networks")
keep keep keep keep replace replace replace keep keep keep keep keep
 Move custom dlight blocks to nn_blocks 
plugins/train/model/dlight.py