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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 fill_A(A, right_eigenvectors):
 """Construct feasible initial guess for transformation matrix A. Parameters A : ndarray Possibly non-feasible transformation matrix. right_eigenvectors : ndarray Right eigenvectors of transition matrix Returns ------- A : ndarray Feasible transformation matrix. """

    num_micro, num_eigen = right_eigenvectors.shape

    A = A.copy()

    # compute 1st column of A by row sum condition
    A[1:, 0] = -1 * A[1:, 1:].sum(1)

    # compute 1st row of A by maximum condition
    A[0] = -1 * dot(right_eigenvectors[:, 1:].real, A[1:]).min(0)

    # rescale A to be in the feasible set
    A /= A[0].sum()

    return A