  
      def call(self, x, mask=None):
          return self.gamma * x
  
      def get_config(self):
          config = {"axis": self.axis}
          base_config = super(Scale, self).get_config()
          return dict(list(base_config.items()) + list(config.items()))
  
  
  class SubPixelUpscaling(Layer):
      # pylint: disable=C0103
 
</s> remove         # Compatibility with TensorFlow >= 1.0.0
        self.gamma = K.variable(self.gamma_init((1,)), name='{}_gamma'.format(self.name))
        self.trainable_weights = [self.gamma]

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights

    def call(self, x, mask=None):
        return self.gamma * x
 </s> add         Returns
        --------
        dict
            A python dictionary containing the layer configuration
        """
        config = {'size': self.size,
                  'data_format': self.data_format}
        base_config = super(PixelShuffler, self).get_config() </s> remove         config = {'size': self.size,
                  'data_format': self.data_format}
        base_config = super(PixelShuffler, self).get_config()

        return dict(list(base_config.items()) + list(config.items()))
 </s> add         """Returns the config of the layer. </s> remove             'axis': self.axis,
            'epsilon': self.epsilon,
            'center': self.center,
            'scale': self.scale,
            'beta_initializer': initializers.serialize(self.beta_initializer),
            'gamma_initializer': initializers.serialize(self.gamma_initializer),
            'beta_regularizer': regularizers.serialize(self.beta_regularizer),
            'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
            'beta_constraint': constraints.serialize(self.beta_constraint),
            'gamma_constraint': constraints.serialize(self.gamma_constraint)
 </s> add             "axis": self.axis,
            "epsilon": self.epsilon,
            "center": self.center,
            "scale": self.scale,
            "beta_initializer": initializers.serialize(self.beta_initializer),
            "gamma_initializer": initializers.serialize(self.gamma_initializer),
            "beta_regularizer": regularizers.serialize(self.beta_regularizer),
            "gamma_regularizer": regularizers.serialize(self.gamma_regularizer),
            "beta_constraint": constraints.serialize(self.beta_constraint),
            "gamma_constraint": constraints.serialize(self.gamma_constraint) </s> remove class Scale(Layer):
    """
    GAN Custom Scal Layer
    Code borrows from https://github.com/flyyufelix/cnn_finetune
    """
    def __init__(self, weights=None, axis=-1, gamma_init='zero', **kwargs):
        self.axis = axis
        self.gamma = None
        self.gamma_init = initializers.get(gamma_init)
        self.initial_weights = weights
        super(Scale, self).__init__(**kwargs)

    def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
 </s> add         The configuration of a layer does not include connectivity information, nor the layer
        class name. These are handled by `Network` (one layer of abstraction above).
  
      def call(self, x, mask=None):
          return self.gamma * x
  



          return dict(list(base_config.items()) + list(config.items()))
  
  
  class SubPixelUpscaling(Layer):
      # pylint: disable=C0103
keep keep keep keep replace replace replace keep keep keep keep keep
 Simple backend unit tests (#1020)

* Add simple backend tests for lib.model
* Document lib.model
* Fix GMSD Loss for AMD
* Remove obsolete code from lib.model 
lib/model/layers.py