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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 levelize_strength_or_aggregation(to_levelize, max_levels, max_coarse):
 """Turn parameter into a list per level. Helper function to preprocess the strength and aggregation parameters passed to smoothed_aggregation_solver and rootnode_solver. Parameters to_levelize : {string, tuple, list} Parameter to preprocess, i.e., levelize and convert to a level-by-level list such that entry i specifies the parameter at level i max_levels : int Defines the maximum number of levels considered max_coarse : int Defines the maximum coarse grid size allowed Returns ------- (max_levels, max_coarse, to_levelize) : tuple New max_levels and max_coarse values and then the parameter list to_levelize, such that entry i specifies the parameter choice at level i. max_levels and max_coarse are returned, because they may be updated if strength or aggregation set a predefined coarsening and possibly change these values. Notes -------- This routine is needed because the user will pass in a parameter option such as smooth='jacobi', or smooth=['jacobi', None], and this option must be "levelized", or converted to a list of length max_levels such that entry [i] in that list is the parameter choice for level i. The parameter choice in to_levelize can be a string, tuple or list. If it is a string or tuple, then that option is assumed to be the parameter setting at every level. If to_levelize is inititally a list, if the length of the list is less than max_levels, the last entry in the list defines that parameter for all subsequent levels. Examples -------- (4, 10, ['evolution', 'classical', 'classical']) """

    if isinstance(to_levelize, tuple):
        if to_levelize[0] == 'predefined':
            to_levelize = [to_levelize]
            max_levels = 2
            max_coarse = 0
        else:
            to_levelize = [to_levelize for i in range(max_levels-1)]

    elif isinstance(to_levelize, str):
        if to_levelize == 'predefined':
            raise ValueError('predefined to_levelize requires a user-provided\
                              CSR matrix representing strength or aggregation\
                              i.e., (\'predefined\', {\'C\' : CSR_MAT}).')
        else:
            to_levelize = [to_levelize for i in range(max_levels-1)]

    elif isinstance(to_levelize, list):
        if isinstance(to_levelize[-1], tuple) and\
           (to_levelize[-1][0] == 'predefined'):
            # to_levelize is a list that ends with a predefined operator
            max_levels = len(to_levelize) + 1
            max_coarse = 0
        else:
            # to_levelize a list that __doesn't__ end with 'predefined'
            if len(to_levelize) < max_levels-1:
                mlz = max_levels - 1 - len(to_levelize)
                toext = [to_levelize[-1] for i in range(mlz)]
                to_levelize.extend(toext)

    elif to_levelize is None:
        to_levelize = [(None, {}) for i in range(max_levels-1)]
    else:
        raise ValueError('invalid to_levelize')

    return max_levels, max_coarse, to_levelize