human
Solve the following python programming problem given the following code:
def get_predict_func(config) : 
    '
    :param config: a `PredictConfig` instance.
    :returns: A prediction function that takes a list of input values, and return
        a list of output values defined in ``config.output_var_names``.
    ' 
    output_var_names = config.output_var_names 
    input_vars = config.model.get_input_vars () 
    cost_var = config.model.get_cost (sess, is_training = False) 
    if (config.input_data_mapping is None) : 
        input_map = input_vars 
else : 
        input_map = [input_vars [k] for k in config.input_data_mapping] 
output_vars = [tf.get_default_graph ().get_tensor_by_name (get_op_var_name (n) [1]) for n in output_var_names] 
    sess = tf.Session (config = config.session_config) 
    config.session_init.init (sess) 
    def run_input(dp) : 
        assert (len (input_map) == len (dp)), 'Graph has {} inputs but dataset only gives {} components!'.format (len (input_map), len (dp)) 
        feed = dict (zip (input_map, dp)) 
        return sess.run (output_vars, feed_dict = feed) 
return run_input 

    
    

    Fix the buggy line: cost_var = config.model.get_cost (sess, is_training = False)
assistant
#Here is Python code to fix the buggy line: cost_var = config.

cost_var = config.model.get_cost (input_vars, is_training = False)