<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def optimize_with_repeates(self,fast=None,verbose=None,n_times=10,lambd=None,lambd_g=None,lambd_n=None):
 """ Train the model repeadly up to a number specified by the users with random restarts and return a list of all relative minima that have been found. This list is sorted according to least likelihood. Each list term is a dictionary with keys "counter", "LML", and "scales". After running this function, the vc object will be set at the last iteration. Thus, if you wish to get the vc object of one of the repeats, then set the scales. For example: vc.setScales(scales=optimize_with_repeates_output[0]["scales"]) Args: fast: Boolean. if set to True initalize kronSumGP verbose: Boolean. If set to True, verbose output is produced. (default True) n_times: number of re-starts of the optimization. (default 10) """

        verbose = dlimix.getVerbose(verbose)

        if not self.init:       self._initGP(fast)

        opt_list = []

        fixed0 = sp.zeros_like(self.gp.getParams()['dataTerm'])

        # minimize n_times
        for i in range(n_times):

            scales1 = self._getScalesRand()
            fixed1  = 1e-1*sp.randn(fixed0.shape[0],fixed0.shape[1])
            conv = self.trainGP(fast=fast,scales0=scales1,fixed0=fixed1,lambd=lambd,lambd_g=lambd_g,lambd_n=lambd_n)

            if conv:
                # compare with previous minima
                temp=1
                for j in range(len(opt_list)):
                    if sp.allclose(abs(self.getScales()),abs(opt_list[j]['scales'])):
                        temp=0
                        opt_list[j]['counter']+=1
                        break
                if temp==1:
                    opt = {}
                    opt['counter'] = 1
                    opt['LML'] = self.getLML()
                    opt['scales'] = self.getScales()
                    opt_list.append(opt)


        # sort by LML
        LML = sp.array([opt_list[i]['LML'] for i in range(len(opt_list))])
        index   = LML.argsort()[::-1]
        out = []
        if verbose:
            print("\nLocal mimima\n")
            print("n_times\t\tLML")
            print("------------------------------------")

        for i in range(len(opt_list)):
            out.append(opt_list[index[i]])
            if verbose:
                print(("%d\t\t%f" % (opt_list[index[i]]['counter'], opt_list[index[i]]['LML'])))
                print("")

        return out