<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def compute_aic(model_object):
 """ Compute the Akaike Information Criteria for an estimated model. Parameters model_object : an MNDC_Model (multinomial discrete choice model) instance. The model should have already been estimated. `model_object.log_likelihood` should be a number, and `model_object.params` should be a pandas Series. Returns ------- aic : float. The AIC for the estimated model. Notes ----- aic = -2 * log_likelihood + 2 * num_estimated_parameters References Akaike, H. (1974). 'A new look at the statistical identification model', IEEE Transactions on Automatic Control 19, 6: 716-723. """

    assert isinstance(model_object.params, pd.Series)
    assert isinstance(model_object.log_likelihood, Number)

    return -2 * model_object.log_likelihood + 2 * model_object.params.size