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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def _outlierDetection_threaded(inputs):
    '''
    Detect the outlier
    '''
    [samples_idx, samples_x, samples_y_aggregation] = inputs
    sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
                        % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
    outlier = None

    # Create a diagnostic regression model which removes the sample that we want to evaluate
    diagnostic_regressor_gp = gp_create_model.create_model(\
                                    samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
                                    samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
    mu, sigma = gp_prediction.predict(samples_x[samples_idx], diagnostic_regressor_gp['model'])

    # 2.33 is the z-score for 98% confidence level
    if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
        outlier = {"samples_idx": samples_idx,
                   "expected_mu": mu,
                   "expected_sigma": sigma,
                   "difference": abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)}
    return outlier