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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 _solve(self, x0, A, l, u, xmin, xmax):
 """ Solves using the Interior Point OPTimizer. """

        # Indexes of constrained lines.
        il = [i for i,ln in enumerate(self._ln) if 0.0 < ln.rate_a < 1e10]
        nl2 = len(il)

        neqnln = 2 * self._nb # no. of non-linear equality constraints
        niqnln = 2 * len(il)  # no. of lines with constraints

        user_data = {"A": A, "neqnln": neqnln, "niqnln": niqnln}

        self._f(x0)
        Jdata = self._dg(x0, False, user_data)
#        Hdata = self._h(x0, ones(neqnln + niqnln), None, False, user_data)

        lmbda = {"eqnonlin": ones(neqnln),
                 "ineqnonlin": ones(niqnln)}
        H = tril(self._hessfcn(x0, lmbda), format="coo")
        self._Hrow, self._Hcol = H.row, H.col

        n = len(x0) # the number of variables
        xl = xmin
        xu = xmax
        gl = r_[zeros(2 * self._nb), -Inf * ones(2 * nl2), l]
        gu = r_[zeros(2 * self._nb),       zeros(2 * nl2), u]
        m = len(gl) # the number of constraints
        nnzj = len(Jdata) # the number of nonzeros in Jacobian matrix
        nnzh = 0#len(H.data) # the number of non-zeros in Hessian matrix

        f_fcn, df_fcn, g_fcn, dg_fcn, h_fcn = \
            self._f, self._df, self._g, self._dg, self._h

        nlp = pyipopt.create(n, xl, xu, m, gl, gu, nnzj, nnzh,
                             f_fcn, df_fcn, g_fcn, dg_fcn)#, h_fcn)

#        print dir(nlp)
#        nlp.str_option("print_options_documentation", "yes")
#        nlp.int_option("max_iter", 10)

#        x, zl, zu, obj = nlp.solve(x0)
        success = nlp.solve(x0, user_data)
        nlp.close()