      var_x = normalization(var_x, norm, filters) if use_norm else var_x
      return var_x
  
  
  def res_block_gan(inp, filters, use_norm=False, norm='none'):
      """ GAN Res Block """
      var_x = Conv2D(filters,
                     kernel_size=3,
                     kernel_regularizer=regularizers.l2(GAN22_REGULARIZER),
                     kernel_initializer=GAN22_CONV_INIT,
 
</s> remove def conv_d_gan(inp, filters, use_norm=False, norm='none'):
 </s> add def conv_d_gan(inp, filters, use_norm=False, norm="none"): </s> remove def conv_gan(inp, filters, use_norm=False, strides=2, norm='none'):
 </s> add def conv_gan(inp, filters, use_norm=False, strides=2, norm="none"): </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     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):
def res_block_gan(inp, filters, use_norm=False, norm="none"):
      var_x = normalization(var_x, norm, filters) if use_norm else var_x
      return var_x
  
  
def res_block_gan(inp, filters, use_norm=False, norm="none"):
      """ GAN Res Block """
      var_x = Conv2D(filters,
                     kernel_size=3,
                     kernel_regularizer=regularizers.l2(GAN22_REGULARIZER),
                     kernel_initializer=GAN22_CONV_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