          self._filters = filters
          self._kernel_size = kernel_size
          self._strides = strides
  
          initializer = self._get_default_initializer(kwargs.pop("kernel_initializer", None))
          kwargs["kernel_initializer"] = initializer
          self._kwargs = kwargs
  
      @classmethod
      def _get_default_initializer(cls, initializer):
 
</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         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  </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._res_block_follows = res_block_follows
 </s> add         self._activation = activation
        initializer = _get_default_initializer(kwargs.pop("kernel_initializer", None))
          self._filters = filters
          self._kernel_size = kernel_size
          self._strides = strides
  
        initializer = _get_default_initializer(kwargs.pop("kernel_initializer", None))
          kwargs["kernel_initializer"] = initializer
          self._kwargs = kwargs
  
      @classmethod
      def _get_default_initializer(cls, initializer):
keep keep keep keep 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