def target_optimizer_arguments(self):
        """
        Returns the target optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Returns:
            Target optimizer arguments as dict.
        """
        variables = self.target_network.get_variables() + [
            variable for name in sorted(self.target_distributions)
            for variable in self.target_distributions[name].get_variables()
        ]
        source_variables = self.network.get_variables() + [
            variable for name in sorted(self.distributions)
            for variable in self.distributions[name].get_variables()
        ]
        arguments = dict(
            time=self.global_timestep,
            variables=variables,
            source_variables=source_variables
        )
        if self.global_model is not None:
            arguments['global_variables'] = self.global_model.target_network.get_variables() + [
                variable for name in sorted(self.global_model.target_distributions)
                for variable in self.global_model.target_distributions[name].get_variables()
            ]
        return arguments
def target_optimizer_arguments(self):
        """
        Returns the target optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Returns:
            Target optimizer arguments as dict.
        """
        variables = self.target_network.get_variables() + [
            variable for name in sorted(self.target_distributions)
            for variable in self.target_distributions[name].get_variables()
        ]
        source_variables = self.network.get_variables() + [
            variable for name in sorted(self.distributions)
            for variable in self.distributions[name].get_variables()
        ]
        arguments = dict(
            time=self.global_timestep,
            variables=variables,
            source_variables=source_variables
        )
        if self.global_model is not None:
            arguments['global_variables'] = self.global_model.target_network.get_variables() + [
                variable for name in sorted(self.global_model.target_distributions)
                for variable in self.global_model.target_distributions[name].get_variables()
            ]
        return arguments
def target_optimizer_arguments ( self ) : variables = self . target_network . get_variables ( ) + [ variable for name in sorted ( self . target_distributions ) for variable in self . target_distributions [ name ] . get_variables ( ) ] source_variables = self . network . get_variables ( ) + [ variable for name in sorted ( self . distributions ) for variable in self . distributions [ name ] . get_variables ( ) ] arguments = dict ( time = self . global_timestep , variables = variables , source_variables = source_variables ) if self . global_model is not None : arguments [ 'global_variables' ] = self . global_model . target_network . get_variables ( ) + [ variable for name in sorted ( self . global_model . target_distributions ) for variable in self . global_model . target_distributions [ name ] . get_variables ( ) ] return arguments
Returns the target optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Returns:
            Target optimizer arguments as dict.
Returns the target optimizer arguments including the time the list of variables to optimize and various functions which the optimizer might require to perform an update step .