          kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", he_uniform())
          return kwargs
  
      # <<< Original Model Blocks >>> #
      def conv(self, inp, filters, kernel_size=5, strides=2, padding='same', use_instance_norm=False, res_block_follows=False, **kwargs):
          """ Convolution Layer"""
          logger.debug("inp: %s, filters: %s, kernel_size: %s, strides: %s, use_instance_norm: %s, "
                       "kwargs: %s)", inp, filters, kernel_size, strides, use_instance_norm, kwargs)
          kwargs = self.update_kwargs(kwargs)
          if self.use_reflect_padding:
 
</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         kwargs = self.update_kwargs(kwargs)
 </s> add  </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): </s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add 
    def conv(self, inp, filters, kernel_size=5, strides=2, padding="same",
             use_instance_norm=False, res_block_follows=False, **kwargs):
          kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", he_uniform())
          return kwargs
  
      # <<< Original Model Blocks >>> #
    def conv(self, inp, filters, kernel_size=5, strides=2, padding="same",
             use_instance_norm=False, res_block_follows=False, **kwargs):
          """ Convolution Layer"""
          logger.debug("inp: %s, filters: %s, kernel_size: %s, strides: %s, use_instance_norm: %s, "
                       "kwargs: %s)", inp, filters, kernel_size, strides, use_instance_norm, kwargs)
          kwargs = self.update_kwargs(kwargs)
          if self.use_reflect_padding:
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