      def upscale(self, inp, filters, kernel_size=3, padding= 'same', use_instance_norm=False, res_block_follows=False, **kwargs):
          """ Upscale Layer """
          logger.debug("inp: %s, filters: %s, kernel_size: %s, use_instance_norm: %s, kwargs: %s)",
                       inp, filters, kernel_size, use_instance_norm, kwargs)
          kwargs = self.update_kwargs(kwargs)
          if self.use_reflect_padding:
              inp = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(inp)
              padding = 'valid'
          temp = kwargs["kernel_initializer"]
          if self.use_icnr_init:
 
</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         kwargs = self.update_kwargs(kwargs)
 </s> add  </s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add  </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     def res_block(self, inp, filters, kernel_size=3, padding= 'same', **kwargs):
 </s> add     def res_block(self, inp, filters, kernel_size=3, padding="same", **kwargs):
      def upscale(self, inp, filters, kernel_size=3, padding= 'same', use_instance_norm=False, res_block_follows=False, **kwargs):
          """ Upscale Layer """
          logger.debug("inp: %s, filters: %s, kernel_size: %s, use_instance_norm: %s, kwargs: %s)",
                       inp, filters, kernel_size, use_instance_norm, kwargs)

          if self.use_reflect_padding:
              inp = ReflectionPadding2D(stride=1, kernel_size=kernel_size)(inp)
              padding = 'valid'
          temp = kwargs["kernel_initializer"]
          if self.use_icnr_init:
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