human
Solve the following python programming problem given the following code:
def __init__(self, **kwargs) : 
    '
        The config used by `get_predict_func`.

        :param session_config: a `tf.ConfigProto` instance to instantiate the session.
        :param session_init: a `utils.sessinit.SessionInit` instance to
            initialize variables of a session.
        :param input_data_mapping: Decide the mapping from each component in data
            to the input tensor, since you may not need all input variables
            of the Model to run the graph for prediction (for example
            the `label` input is not used if you only need probability distribution).

            It should be a list of int with length equal to `len(data_point)`,
            where each element in the list defines which input variables each
            component in the data point should be fed into.
            If not given, defaults to range(len(input_vars))

            For example, in image classification task, the testing
            dataset only provides datapoints of images (no labels). When
            the input variables of the model is: ::

                input_vars: [image_var, label_var]

            the mapping should then look like: ::

                input_data_mapping: [0] # the first component in a datapoint should map to `image_var`

        :param model: a `ModelDesc` instance
        :param output_var_names: a list of names of the output variables to predict, the
            variables can be any computable tensor in the graph.
            Predict specific output might not require all input variables.
        :param nr_gpu: default to 1. Use CUDA_VISIBLE_DEVICES to control which GPU to use sepcifically.
        ' 
    def assert_type(v, tp) : 
        assert isinstance (v, tp), v.__class__ 
self.session_config = kwargs.pop ('session_config', get_default_sess_config ()) 
    assert_type (self.session_config, tf.ConfigProto) 
    self.session_init = kwargs.pop ('session_init') 
    self.model = kwargs.pop ('model') 
    self.input_data_mapping = kwargs.pop...

    
    

    Fix the buggy line: assert (len (assert_type) == 0), 'Unknown arguments: {}'.format (str (kwargs.keys ()))
assistant
#Here is Python code to fix the buggy line: assert (len (assert_type) == 0), 'Unknown arguments: {}'.

assert (len (kwargs) == 0), 'Unknown arguments: {}'.format (str (kwargs.keys ()))