human
Solve the following python programming problem given the following code:
def __init__(self, incoming, memory_shape, num_units, num_reads, W_in_to_hid = lasagne.init.GlorotUniform (), b_in_to_hid = lasagne.init.Constant (0.0), W_reads_to_hid = lasagne.init.GlorotUniform (), b_reads_to_hid = lasagne.init.Constant (0.0), W_hid_to_hid = lasagne.init.GlorotUniform (), b_hid_to_hid = lasagne.init.Constant (0.0), nonlinearity = lasagne.nonlinearities.rectify, hid_init = lasagne.init.GlorotUniform (), learn_init = False, **kwargs) : 
    super (RecurrentController, self).__init__ (incoming, memory_shape, num_units, num_reads, hid_init, nonlinearity, ** kwargs) 
    self.nonlinearity = (lasagne.nonlinearities.identity if (nonlinearity is None) else nonlinearity) 
    def add_weight_and_bias_params(input_dim, W, b, name) : 
        return (self.add_param (W, (input_dim, self.num_units), name = 'W_{}'.format (name)), (self.add_param (b, (self.num_units,), name = 'b_{}'.format (name)) if (b is not None) else None)) 
num_inputs = int (np.prod (self.input_shape [2 :])) 
    (self.W_in_to_hid, self.b_in_to_hid) = add_weight_and_bias_params (num_inputs, W_in_to_hid, b_in_to_hid, name = 'in_to_hid') 
    (self.W_reads_to_hid, self.b_reads_to_hid) = add_weight_and_bias_params ((self.num_reads * self.memory_shape [1]), W_reads_to_hid, b_reads_to_hid, name = 'reads_to_hid') 
    (self.W_hid_to_hid, self.b_hid_to_hid) = add_weight_and_bias_params (self.num_units, W_hid_to_hid, b_hid_to_hid, name = 'hid_to_hid') 

    
    

    Fix the buggy line: super (RecurrentController, self).__init__ (incoming, memory_shape, num_units, num_reads, hid_init, nonlinearity, ** kwargs)
assistant
#Here is Python code to fix the buggy line: super (RecurrentController, self).

super (RecurrentController, self).__init__ (incoming, memory_shape, num_units, num_reads, hid_init, learn_init, ** kwargs)