              var_x = PixelShuffler()(var_x)
          return var_x
  
      # <<< DFaker Model Blocks >>> #
      def res_block(self, inp, filters, kernel_size=3, padding= 'same', **kwargs):
          """ Residual block """
          logger.debug("inp: %s, filters: %s, kernel_size: %s, kwargs: %s)",
                       inp, filters, kernel_size, kwargs)
          kwargs = self.update_kwargs(kwargs)
          var_x = LeakyReLU(alpha=0.2)(inp)
 
</s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add  </s> remove     def upscale(self, inp, filters, kernel_size=3, padding= 'same', use_instance_norm=False, res_block_follows=False, **kwargs):
 </s> add     def upscale(self, inp, filters, kernel_size=3, padding="same",
                use_instance_norm=False, res_block_follows=False, **kwargs): </s> remove     def conv(self, inp, filters, kernel_size=5, strides=2, padding='same', use_instance_norm=False, res_block_follows=False, **kwargs):
 </s> add     def conv(self, inp, filters, kernel_size=5, strides=2, padding="same",
             use_instance_norm=False, res_block_follows=False, **kwargs): </s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add  </s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add 
    def res_block(self, inp, filters, kernel_size=3, padding="same", **kwargs):
              var_x = PixelShuffler()(var_x)
          return var_x
  
      # <<< DFaker Model Blocks >>> #
    def res_block(self, inp, filters, kernel_size=3, padding="same", **kwargs):
          """ Residual block """
          logger.debug("inp: %s, filters: %s, kernel_size: %s, kwargs: %s)",
                       inp, filters, kernel_size, kwargs)
          kwargs = self.update_kwargs(kwargs)
          var_x = LeakyReLU(alpha=0.2)(inp)
keep keep keep keep replace keep keep keep keep keep
 Add Convolutional Aware Initialization (#795)

* Training: Add Convolutional Aware Initialization config option

* Centralize Conv2D layer for handling initializer

* Add 'is-output' to NNMeta to indicate that network is an output to the Model 
lib/model/nn_blocks.py