          kwargs = self.update_kwargs(kwargs)
          var_x = SeparableConv2D(filters,
                                  kernel_size=kernel_size,
                                  strides=strides,
                                  padding='same',
                                  **kwargs)(inp)
          var_x = Activation("relu")(var_x)
          return var_x
  
  # <<< GAN V2.2 Blocks >>> #
 
</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     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             padding = 'valid'
        temp = kwargs["kernel_initializer"]
        kwargs["kernel_initializer"] = VarianceScaling(scale=0.2,
                                                       mode='fan_in',
                                                       distribution='uniform')
        var_x = Conv2D(filters,
                       kernel_size=kernel_size,
                       padding=padding,
                       **kwargs)(var_x)
        kwargs["kernel_initializer"] = temp
 </s> add             padding = "valid"
        original_init = self.switch_kernel_initializer(kwargs, VarianceScaling(
            scale=0.2,
            mode="fan_in",
            distribution="uniform"))
        var_x = self.conv2d(var_x, filters,
                            kernel_size=kernel_size,
                            padding=padding,
                            force_initializer=True,
                            **kwargs)
        self.switch_kernel_initializer(kwargs, original_init) </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     @staticmethod
    def update_kwargs(kwargs):
        """ Set the default kernel initializer to he_uniform() """
        kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", he_uniform())
 </s> add     def update_kwargs(self, kwargs):
        """ Update Kwargs for conv2D and Seperable conv2D layers.
            Set the default kernel initializer to conv_aware or he_uniform()
            if a specific initializer has not been passed in """
        if self.use_convaware_init:
            default = ConvolutionAware()
            if self.first_run:
                # Indicate the Convolutional Aware should be calculated on first run
                default._init = True  # pylint:disable=protected-access
        else:
            default = he_uniform()
        kwargs["kernel_initializer"] = kwargs.get("kernel_initializer", default)
        logger.debug("Set default kernel_initializer to: %s", kwargs["kernel_initializer"])
                                padding="same",
          kwargs = self.update_kwargs(kwargs)
          var_x = SeparableConv2D(filters,
                                  kernel_size=kernel_size,
                                  strides=strides,
                                padding="same",
                                  **kwargs)(inp)
          var_x = Activation("relu")(var_x)
          return var_x
  
  # <<< GAN V2.2 Blocks >>> #
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