tensorflow_probability/python/layers/conv_variational.py
def get_config(self):
    """Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.
    """
    config = {
        'seed': self.seed,
    }
    base_config = super(_ConvFlipout, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
    """Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.
    """
    config = {
        'seed': self.seed,
    }
    base_config = super(_ConvFlipout, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
def get_config ( self ) : config = { 'seed' : self . seed , } base_config = super ( _ConvFlipout , self ) . get_config ( ) return dict ( list ( base_config . items ( ) ) + list ( config . items ( ) ) )
Returns the config of the layer.

    A layer config is a Python dictionary (serializable) containing the
    configuration of a layer. The same layer can be reinstantiated later
    (without its trained weights) from this configuration.

    Returns:
      config: A Python dictionary of class keyword arguments and their
        serialized values.
Returns the config of the layer .