          self._kernel_size = kernel_size
          self._strides = strides
          self._padding = "valid" if self._use_reflect_padding else padding
          self._kwargs = kwargs
          self._use_instance_norm = use_instance_norm
          self._res_block_follows = res_block_follows
  
      def __call__(self, inputs):
          """ Call the Faceswap Convolutional Layer.
  
          Parameters
 
</s> remove         self._use_instance_norm = use_instance_norm
        self._res_block_follows = res_block_follows
 </s> add         self._normalization = normalization
        self._activation = activation </s> remove         self._filters = filters
        self._kernel_size = kernel_size
 </s> add         self._args = (kernel_size, ) if use_depthwise else (filters, kernel_size) </s> remove         self._res_block_follows = res_block_follows
 </s> add         self._activation = activation </s> remove         initializer = self._get_default_initializer(kwargs.pop("kernel_initializer", None))
 </s> add         initializer = _get_default_initializer(kwargs.pop("kernel_initializer", None)) </s> remove     @classmethod
    def _get_default_initializer(cls, initializer):
        """ Returns a default initializer of Convolutional Aware or he_uniform for convolutional
        layers.

        Parameters
        ----------
        initializer: :class:`keras.initializers.Initializer` or None
            The initializer that has been passed into the model. If this value is ``None`` then a
            default initializer will be returned based on the configuration choices, otherwise
            the given initializer will be returned.

        Returns
        -------
        :class:`keras.initializers.Initializer`
            The kernel initializer to use for this convolutional layer. Either the original given
            initializer, he_uniform or convolutional aware (if selected in config options)
        """
        if initializer is None:
            retval = ConvolutionAware() if _CONFIG["conv_aware_init"] else he_uniform()
            logger.debug("Set default kernel_initializer: %s", retval)
        else:
            retval = initializer
            logger.debug("Using model supplied initializer: %s", retval)
        return retval

 </s> add 
        self._normalization = None if not normalization else normalization.lower()
        self._activation = None if not activation else activation.lower()
        self._use_depthwise = use_depthwise

        self._assert_arguments()

    def _assert_arguments(self):
        """ Validate the given arguments. """
        assert self._normalization in ("batch", "instance", None), (
            "normalization should be 'batch', 'instance' or None")
        assert self._activation in ("leakyrelu", "swish", None), (
            "activation should be 'leakyrelu', 'swish' or None")
          self._kernel_size = kernel_size
          self._strides = strides
          self._padding = "valid" if self._use_reflect_padding else padding
          self._kwargs = kwargs
        self._normalization = None if not normalization else normalization.lower()
        self._activation = None if not activation else activation.lower()
        self._use_depthwise = use_depthwise

        self._assert_arguments()

    def _assert_arguments(self):
        """ Validate the given arguments. """
        assert self._normalization in ("batch", "instance", None), (
            "normalization should be 'batch', 'instance' or None")
        assert self._activation in ("leakyrelu", "swish", None), (
            "activation should be 'leakyrelu', 'swish' or None")
        self._normalization = None if not normalization else normalization.lower()
        self._activation = None if not activation else activation.lower()
        self._use_depthwise = use_depthwise

        self._assert_arguments()

    def _assert_arguments(self):
        """ Validate the given arguments. """
        assert self._normalization in ("batch", "instance", None), (
            "normalization should be 'batch', 'instance' or None")
        assert self._activation in ("leakyrelu", "swish", None), (
            "activation should be 'leakyrelu', 'swish' or None")
  
      def __call__(self, inputs):
          """ Call the Faceswap Convolutional Layer.
  
          Parameters
keep keep keep keep replace replace keep keep keep keep keep
 lib.model - Maintenance
  - Add Depthwise option to Conv Block
  - Add Swish Activation function
  - remove res_block_follows and add_instance_norm_args
  - Add explicit normalization and activation args
  - Add K.resize_images layer
plugins.train.model - Inference creation bugfix 
lib/model/nn_blocks.py