human
Solve the following python programming problem given the following code:
@ staticmethod 
def punchcard_module_classification_performance(module, dataset, s = 800) : 
    "Punshcard-like clasification performances.__add__(

        Actual dataset target vs. estimated target by the module.
        The graph of a good classfier module should a have no red dots visible:
        - Red Dots: Target (only visible if the black dot doesn't cover it).
        - Green Dots: Estimated classes confidences (size = outputs means).
        - Black Dots: Single winnter-takes-all estimated target.

        :param module: An object that has at least reset() and activate() methods.
        :param dataset: A classification dataset. It should, for any given sequence, have a constant target.
        :type dataset: ClassificationDataSet
        " 
    def calculate_module_output_mean(module, inputs) : 
        "Returns the mean of the module's outputs for a given input list." 
        outputs = np.zeros (module.outdim) 
        module.reset () 
        for inpt in inputs : 
            outputs += module.activate (inpt) 
return (outputs / len (inputs)) 
num_sequences = dataset.getNumSequences () 
    actual = [] 
    expected = [] 
    confidence_x = [] 
    confidence_s = [] 
    correct = 0 
    for seq_i in range (num_sequences) : 
        seq = dataset.getSequence (seq_i) 
        outputs_mean = calculate_module_output_mean (module, seq [0]) 
        actual.append (np.argmax (outputs_mean)) 
        confidence_s.append (np.array (outputs_mean)) 
        confidence_x.append ((np.ones (module.outdim) * expected)) 
        expected.append (np.argmax (seq [1])) 
        if (actual [(- 1)] == expected [(- 1)]) : 
            correct += 1 
plt.title ('{}% Correct Classification (red dots mean bad classification)'.format (((correct * 100) / num_sequences))) 
    plt.xlabel ('Sequence') 
    plt.ylabel ('Class') 
    plt.scatter (list (range (num_sequences)), expected, s = s, c = 'r', linewidths = 0) 
    plt.scatter (list (range (num_sequences)), actual, s = s, c = 'k') 
    plt.scatter (confidence_x, (list...

    
    

    Fix the buggy line: confidence_x.append ((np.ones (module.outdim) * expected))
assistant
#Here is Python program to fix the buggy line: confidence_x.

confidence_x.append ((np.ones (module.outdim) * seq_i))