      var_x = normalization(var_x, norm, filters) if use_norm else var_x
      return var_x
  
  
  def conv_d_gan(inp, filters, use_norm=False, norm='none'):
      """ GAN Discriminator Conv Block """
      var_x = inp
      var_x = Conv2D(filters,
                     kernel_size=4,
                     strides=2,
 
</s> remove def res_block_gan(inp, filters, use_norm=False, norm='none'):
 </s> add def res_block_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             padding = 'valid'
        var_x = Conv2D(filters,
                       kernel_size=kernel_size,
                       strides=strides,
                       padding=padding,
                       **kwargs)(inp)
 </s> add             padding = "valid"
        var_x = self.conv2d(inp, filters,
                            kernel_size=kernel_size,
                            strides=strides,
                            padding=padding,
                            **kwargs) </s> remove         kwargs = self.update_kwargs(kwargs)
 </s> add 
def conv_d_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 conv_d_gan(inp, filters, use_norm=False, norm="none"):
      """ GAN Discriminator Conv Block """
      var_x = inp
      var_x = Conv2D(filters,
                     kernel_size=4,
                     strides=2,
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