      kwargs: dict
          Any additional Keras standard layer keyword arguments to pass to the Convolutional 2D layer
      """
      def __init__(self, filters, kernel_size=3, padding="same", interpolation="bilinear",
                   res_block_follows=False, sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
          self._name = _get_name("upscale2x_{}_{}".format(filters, "fast" if fast else "hyb"))
  
          self._fast = fast
          self._filters = filters if self._fast else filters - int(filters * sr_ratio)
          self._kernel_size = kernel_size
 
</s> remove         self._res_block_follows = res_block_follows
 </s> add         self._activation = activation </s> remove     use_instance_norm: bool, optional
        ``True`` if instance normalization should be applied after the convolutional layer.
        Default: ``False``
    res_block_follows: bool, optional
        If a residual block will follow this layer, then this should be set to ``True`` to add a
        leaky ReLu after the convolutional layer. Default: ``False``
 </s> add     normalization: str or ``None``, optional
        Normalization to apply after the Convolution Layer. Select one of "batch" or "instance".
        Set to ``None`` to not apply normalization. Default: ``None``
    activation: str or ``None``, optional
        The activation function to use. This is applied at the end of the convolution block. Select
        one of `"leakyrelu"` or `"swish"`. Set to ``None`` to not apply an activation function.
        Default: `"leakyrelu"`
    use_depthwise: bool, optional
        Set to ``True`` to use a Depthwise Convolution 2D layer rather than a standard Convolution
        2D layer. Default: ``False`` </s> remove     use_instance_norm: bool, optional
        ``True`` if instance normalization should be applied after the convolutional layer.
        Default: ``False``
    res_block_follows: bool, optional
        If a residual block will follow this layer, then this should be set to ``True`` to add
        a leaky ReLu after the convolutional layer. Default: ``False``
 </s> add     normalization: str or ``None``, optional
        Normalization to apply after the Convolution Layer. Select one of "batch" or "instance".
        Set to ``None`` to not apply normalization. Default: ``None``
    activation: str or ``None``, optional
        The activation function to use. This is applied at the end of the convolution block. Select
        one of `"leakyrelu"` or `"swish"`. Set to ``None`` to not apply an activation function.
        Default: `"leakyrelu"` </s> remove                      "use_instance_norm: %s, res_block_follows: %s, kwargs: %s)",
                     self._name, filters, kernel_size, strides, padding, use_instance_norm,
                     res_block_follows, kwargs)
 </s> add                      "normalization: %s, activation: %s, use_depthwise: %s, kwargs: %s)",
                     self._name, filters, kernel_size, strides, padding, normalization,
                     activation, use_depthwise, kwargs)
 </s> remove                      "use_instance_norm: %s, res_block_follows: %s, kwargs: %s)",
                     self._name, filters, kernel_size, padding, scale_factor, use_instance_norm,
                     res_block_follows, kwargs)
 </s> add                      "normalization: %s, activation: %s, kwargs: %s)",
                     self._name, filters, kernel_size, padding, scale_factor, normalization,
                     activation, kwargs)
                 activation="leakyrelu", sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
      kwargs: dict
          Any additional Keras standard layer keyword arguments to pass to the Convolutional 2D layer
      """
      def __init__(self, filters, kernel_size=3, padding="same", interpolation="bilinear",
                 activation="leakyrelu", sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
          self._name = _get_name("upscale2x_{}_{}".format(filters, "fast" if fast else "hyb"))
  
          self._fast = fast
          self._filters = filters if self._fast else filters - int(filters * sr_ratio)
          self._kernel_size = kernel_size
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