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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 minimize(self, theta_init, max_iter=50, callback=None, disp=0, tau=(10., 2., 2.), tol=1e-3):
 """ Minimize a list of objectives using a proximal consensus algorithm Parameters theta_init : ndarray Initial parameter vector (numpy array) max_iter : int, optional Maximum number of iterations to run (default: 50) callback : function, optional a function that gets called on each iteration with the following arguments: the current parameter value (ndarray), and a dictionary that contains a information about the status of the algorithm disp : int, optional determines how much information to display when running. Ranges from 0 (nothing) to 3 (lots of information) Returns ------- theta : ndarray The parameters found after running the optimization procedure Other Parameters tau : (float, float, float), optional initial, increment and decrement parameters for the momentum scheduler (default: (10, 2, 2)) tol : float, optional residual tolerance for assessing convergence. if both the primal and dual residuals are less than this value, then the algorithm has converged (default: 1e-3) """


        # get list of objectives for this parameter
        num_obj = len(self.objectives)
        assert num_obj >= 1, "There must be at least one objective!"

        # initialize lists of primal and dual variable copies, one for each objective
        orig_shape = theta_init.shape
        primals = [theta_init.flatten() for _ in range(num_obj)]
        duals = [np.zeros(theta_init.size) for _ in range(num_obj)]
        theta_avg = np.mean(primals, axis=0).ravel()

        # initialize penalty parameter
        tau = namedtuple('tau', ('init', 'inc', 'dec'))(*tau)
        rho = tau.init

        # store cumulative runtimes of each iteration, starting now
        tstart = time.time()

        # clear metadata
        self.metadata = defaultdict(list)

        # run ADMM iterations
        self.converged = False
        for cur_iter in range(max_iter):

            # store the parameters from the previous iteration
            theta_prev = theta_avg

            # update each primal variable copy by taking a proximal step via each objective
            for varidx, dual in enumerate(duals):
                primals[varidx] = self.objectives[varidx]((theta_prev - dual).reshape(orig_shape), rho).ravel()

            # average primal copies
            theta_avg = np.mean(primals, axis=0)

            # update the dual variables (after primal update has finished)
            for varidx, primal in enumerate(primals):
                duals[varidx] += primal - theta_avg

            # compute primal and dual residuals
            primal_resid = float(np.sum([np.linalg.norm(primal - theta_avg) for primal in primals]))
            dual_resid = num_obj * rho ** 2 * np.linalg.norm(theta_avg - theta_prev)

            # update penalty parameter according to primal and dual residuals
            # (see sect. 3.4.1 of the Boyd and Parikh ADMM paper)
            if primal_resid > tau.init * dual_resid:
                rho *= float(tau.inc)
            elif dual_resid > tau.init * primal_resid:
                rho /= float(tau.dec)

            # update metadata for this iteration
            self.metadata['Primal resid'].append(primal_resid)
            self.metadata['Dual resid'].append(dual_resid)
            self.metadata['Time (s)'].append(time.time() - tstart)
            self.metadata['rho'].append(rho)

            # invoke the callback function with the current parameters and
            # history
            if callback is not None:

                # get the metadata from this iteration
                data = valmap(last, self.metadata)

                callback(theta_avg.reshape(orig_shape), data)

            # update the display
            self.update_display(cur_iter + 1, disp)

            # check for convergence
            if (primal_resid <= tol) & (dual_resid <= tol):
                self.converged = True
                break

        # clean up display
        self.update_display(-1, disp)

        # store and return final parameters
        self.theta = theta_avg.reshape(orig_shape)
        return self.theta