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 = {
        'filters': self.filters,
        'kernel_size': self.kernel_size,
        'strides': self.strides,
        'padding': self.padding,
        'data_format': self.data_format,
        'dilation_rate': self.dilation_rate,
        'activation': (tf.keras.activations.serialize(self.activation)
                       if self.activation else None),
        'activity_regularizer':
            tf.keras.initializers.serialize(self.activity_regularizer),
    }
    function_keys = [
        'kernel_posterior_fn',
        'kernel_posterior_tensor_fn',
        'kernel_prior_fn',
        'kernel_divergence_fn',
        'bias_posterior_fn',
        'bias_posterior_tensor_fn',
        'bias_prior_fn',
        'bias_divergence_fn',
    ]
    for function_key in function_keys:
      function = getattr(self, function_key)
      if function is None:
        function_name = None
        function_type = None
      else:
        function_name, function_type = tfp_layers_util.serialize_function(
            function)
      config[function_key] = function_name
      config[function_key + '_type'] = function_type
    base_config = super(_ConvVariational, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
def get_config(self):
    
    config = {
        'filters': self.filters,
        'kernel_size': self.kernel_size,
        'strides': self.strides,
        'padding': self.padding,
        'data_format': self.data_format,
        'dilation_rate': self.dilation_rate,
        'activation': (tf.keras.activations.serialize(self.activation)
                       if self.activation else None),
        'activity_regularizer':
            tf.keras.initializers.serialize(self.activity_regularizer),
    }
    function_keys = [
        'kernel_posterior_fn',
        'kernel_posterior_tensor_fn',
        'kernel_prior_fn',
        'kernel_divergence_fn',
        'bias_posterior_fn',
        'bias_posterior_tensor_fn',
        'bias_prior_fn',
        'bias_divergence_fn',
    ]
    for function_key in function_keys:
      function = getattr(self, function_key)
      if function is None:
        function_name = None
        function_type = None
      else:
        function_name, function_type = tfp_layers_util.serialize_function(
            function)
      config[function_key] = function_name
      config[function_key + '_type'] = function_type
    base_config = super(_ConvVariational, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))
def get_config ( self ) : config = { 'filters' : self . filters , 'kernel_size' : self . kernel_size , 'strides' : self . strides , 'padding' : self . padding , 'data_format' : self . data_format , 'dilation_rate' : self . dilation_rate , 'activation' : ( tf . keras . activations . serialize ( self . activation ) if self . activation else None ) , 'activity_regularizer' : tf . keras . initializers . serialize ( self . activity_regularizer ) , } function_keys = [ 'kernel_posterior_fn' , 'kernel_posterior_tensor_fn' , 'kernel_prior_fn' , 'kernel_divergence_fn' , 'bias_posterior_fn' , 'bias_posterior_tensor_fn' , 'bias_prior_fn' , 'bias_divergence_fn' , ] for function_key in function_keys : function = getattr ( self , function_key ) if function is None : function_name = None function_type = None else : function_name , function_type = tfp_layers_util . serialize_function ( function ) config [ function_key ] = function_name config [ function_key + '_type' ] = function_type base_config = super ( _ConvVariational , 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 .