syne_tune/optimizer/schedulers/searchers/bayesopt/gpautograd/kernel/exponential_decay.py [261:278]:
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        result = {k: anp.reshape(v, (1,))[0] for k, v in zip(keys, values)}
        for pref, func in [('kernelx_', self.kernel_x), ('meanx_', self.mean_x)]:
            result.update({
                (pref + k): v for k, v in func.get_params().items()})
                
        return result

    def set_params(self, param_dict):
        for pref, func in [('kernelx_', self.kernel_x), ('meanx_', self.mean_x)]:
            len_pref = len(pref)
            stripped_dict = {
                k[len_pref:]: v for k, v in param_dict.items()
                if k.startswith(pref)}
            func.set_params(stripped_dict)
        self.encoding_alpha.set(self.alpha_internal, param_dict['alpha'])
        self.encoding_mean_lam.set(
            self.mean_lam_internal, param_dict['mean_lam'])
        self.encoding_gamma.set(self.gamma_internal, param_dict['gamma'])
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syne_tune/optimizer/schedulers/searchers/bayesopt/gpautograd/kernel/freeze_thaw.py [202:218]:
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        result = {k: anp.reshape(v, (1,))[0] for k, v in zip(keys, values)}
        for pref, func in [('kernelx_', self.kernel_x), ('meanx_', self.mean_x)]:
            result.update({
                (pref + k): v for k, v in func.get_params().items()})
        return result

    def set_params(self, param_dict):
        for pref, func in [('kernelx_', self.kernel_x), ('meanx_', self.mean_x)]:
            len_pref = len(pref)
            stripped_dict = {
                k[len_pref:]: v for k, v in param_dict.items()
                if k.startswith(pref)}
            func.set_params(stripped_dict)
        self.encoding_alpha.set(self.alpha_internal, param_dict['alpha'])
        self.encoding_mean_lam.set(
            self.mean_lam_internal, param_dict['mean_lam'])
        self.encoding_gamma.set(self.gamma_internal, param_dict['gamma'])
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