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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 transition_matrix_non_reversible(C):
 r""" Estimates a non-reversible transition matrix from count matrix C T_ij = c_ij / c_i where c_i = sum_j c_ij Parameters C: ndarray, shape (n,n) count matrix Returns ------- T: Estimated transition matrix """

    # multiply by 1.0 to make sure we're not doing integer division
    rowsums = 1.0 * np.sum(C, axis=1)
    if np.min(rowsums) <= 0:
        raise ValueError(
            "Transition matrix has row sum of " + str(np.min(rowsums)) + ". Must have strictly positive row sums.")
    return np.divide(C, rowsums[:, np.newaxis])