syne_tune/optimizer/schedulers/searchers/gp_searcher_factory.py [475:485]:
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        num_samples=1)
    exponent_cost = kwargs.get('exponent_cost', 1.0)
    acquisition_class = (
        EIpuAcquisitionFunction, dict(exponent_cost=exponent_cost))
    # The same skip_optimization strategy applies to both models
    skip_optimization_cost = skip_optimization

    output_model_factory = {INTERNAL_METRIC_NAME: model_factory,
                            INTERNAL_COST_NAME: model_factory_cost}
    output_skip_optimization = {INTERNAL_METRIC_NAME: skip_optimization,
                                INTERNAL_COST_NAME: skip_optimization_cost}
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syne_tune/optimizer/schedulers/searchers/gp_searcher_factory.py [524:534]:
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        num_samples=1)
    exponent_cost = kwargs.get('exponent_cost', 1.0)
    acquisition_class = (
        EIpuAcquisitionFunction, dict(exponent_cost=exponent_cost))
    # The same skip_optimization strategy applies to both models
    skip_optimization_cost = skip_optimization

    output_model_factory = {INTERNAL_METRIC_NAME: model_factory,
                            INTERNAL_COST_NAME: model_factory_cost}
    output_skip_optimization = {INTERNAL_METRIC_NAME: skip_optimization,
                                INTERNAL_COST_NAME: skip_optimization_cost}
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