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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 rank(matrix, atol=1e-13, rtol=0):
 """ Estimate the rank, i.e., the dimension of the column space, of a matrix. The algorithm used by this function is based on the singular value decomposition of `stoichiometry_matrix`. Parameters matrix : ndarray The matrix should be at most 2-D. A 1-D array with length k will be treated as a 2-D with shape (1, k) atol : float The absolute tolerance for a zero singular value. Singular values smaller than ``atol`` are considered to be zero. rtol : float The relative tolerance for a zero singular value. Singular values less than the relative tolerance times the largest singular value are considered to be zero. Notes ----- If both `atol` and `rtol` are positive, the combined tolerance is the maximum of the two; that is:: tol = max(atol, rtol * smax) Singular values smaller than ``tol`` are considered to be zero. Returns ------- int The estimated rank of the matrix. See Also -------- numpy.linalg.matrix_rank matrix_rank is basically the same as this function, but it does not provide the option of the absolute tolerance. """

    matrix = np.atleast_2d(matrix)
    sigma = svd(matrix, compute_uv=False)
    tol = max(atol, rtol * sigma[0])
    return int((sigma >= tol).sum())