                   filters,
                   kernel_size=3,
                   padding="same",
                   scale_factor=2,
                   use_instance_norm=False,
                   res_block_follows=False,
                   **kwargs):
          self._name = _get_name("upscale_{}".format(filters))
          logger.debug("name: %s. filters: %s, kernel_size: %s, padding: %s, scale_factor: %s, "
                       "use_instance_norm: %s, res_block_follows: %s, kwargs: %s)",
                       self._name, filters, kernel_size, padding, scale_factor, use_instance_norm,
 
</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) </s> remove                  use_instance_norm=False,
                 res_block_follows=False,
 </s> add                  normalization=None,
                 activation="leakyrelu",
                 use_depthwise=False, </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                  res_block_follows=False, sr_ratio=0.5, scale_factor=2, fast=False, **kwargs):
 </s> add                  activation="leakyrelu", sr_ratio=0.5, scale_factor=2, fast=False, **kwargs): </s> remove         self._filters = filters
        self._kernel_size = kernel_size
 </s> add         self._args = (kernel_size, ) if use_depthwise else (filters, kernel_size)
                 normalization=None,
                 activation="leakyrelu",
                   filters,
                   kernel_size=3,
                   padding="same",
                   scale_factor=2,
                 normalization=None,
                 activation="leakyrelu",
                 normalization=None,
                 activation="leakyrelu",
                   **kwargs):
          self._name = _get_name("upscale_{}".format(filters))
          logger.debug("name: %s. filters: %s, kernel_size: %s, padding: %s, scale_factor: %s, "
                       "use_instance_norm: %s, res_block_follows: %s, kwargs: %s)",
                       self._name, filters, kernel_size, padding, scale_factor, use_instance_norm,
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