<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def correct_transition_matrix(T, reversible=None):
 r"""Normalize transition matrix Fixes a the row normalization of a transition matrix. To be used with the reversible estimators to fix an almost coverged transition matrix. Parameters T : (M, M) ndarray matrix to correct reversible : boolean for future use Returns ------- (M, M) ndarray corrected transition matrix """

    row_sums = T.sum(axis=1).A1
    max_sum = np.max(row_sums)
    if max_sum == 0.0:
         max_sum = 1.0
    return (T + scipy.sparse.diags(-row_sums+max_sum, 0)) / max_sum