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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 get_sos_decomposition(sdp, y_mat=None, threshold=0.0):
 """Given a solution of the dual problem, it returns the SOS decomposition. :param sdp: The SDP relaxation to be solved. :type sdp: :class:`ncpol2sdpa.sdp`. :param y_mat: Optional parameter providing the dual solution of the moment matrix. If not provided, the solution is extracted from the sdp object. :type y_mat: :class:`numpy.array`. :param threshold: Optional parameter for specifying the threshold value below which the eigenvalues and entries of the eigenvectors are disregarded. :type threshold: float. :rtype: list of :class:`sympy.core.exp.Expr`. """

    if len(sdp.monomial_sets) != 1:
        raise Exception("Cannot automatically match primal and dual " +
                        "variables.")
    elif len(sdp.y_mat[1:]) != len(sdp.constraints):
        raise Exception("Cannot automatically match constraints with blocks " +
                        "in the dual solution.")
    elif sdp.status == "unsolved" and y_mat is None:
        raise Exception("The SDP relaxation is unsolved and dual solution " +
                        "is not provided!")
    elif sdp.status != "unsolved" and y_mat is None:
        y_mat = sdp.y_mat
    sos = []
    for y_mat_block in y_mat:
        term = 0
        vals, vecs = np.linalg.eigh(y_mat_block)
        for j, val in enumerate(vals):
            if val < -0.001:
                raise Exception("Large negative eigenvalue: " + val +
                                ". Matrix cannot be positive.")
            elif val > 0:
                sub_term = 0
                for i, entry in enumerate(vecs[:, j]):
                    sub_term += entry * sdp.monomial_sets[0][i]
                term += val * sub_term**2
        term = expand(term)
        new_term = 0
        if term.is_Mul:
            elements = [term]
        else:
            elements = term.as_coeff_mul()[1][0].as_coeff_add()[1]
        for element in elements:
            _, coeff = separate_scalar_factor(element)
            if abs(coeff) > threshold:
                new_term += element
        sos.append(new_term)
    return sos