nevergrad/optimization/optimizerlib.py [714:726]:
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        self.children: tp.List[p.Parameter] = []

    def recommend(self) -> p.Parameter:
        if self.naive:
            return self.current_bests["optimistic"].parameter
        else:
            # This is NOT the naive version. We deal with noise.
            out = self.parametrization.spawn_child()
            with p.helpers.deterministic_sampling(out):
                out.set_standardized_data(self.current_center)
            return out

    def _internal_ask_candidate(self) -> p.Parameter:
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nevergrad/optimization/optimizerlib.py [2244:2256]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        self.children: tp.List[p.Parameter] = []

    def recommend(self) -> p.Parameter:
        if self.naive:
            return self.current_bests["optimistic"].parameter
        else:
            # This is NOT the naive version. We deal with noise.
            out = self.parametrization.spawn_child()
            with p.helpers.deterministic_sampling(out):
                out.set_standardized_data(self.current_center)
            return out

    def _internal_ask_candidate(self) -> p.Parameter:
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