def _score_core_from_prediction(
        self,
        vals: np.ndarray,
        pred_vals: np.ndarray,
    ) -> np.ndarray:
        """For each timestamp of the inputs:

        - the parameters of the considered distribution are fitted on the samples of the probabilistic time series
        - the negative log-likelihood of the deterministic time series values are computed

        If the series is multivariate, the score will be computed on each component independently.

        Parameters
        ----------
        vals
            The values of a deterministic time series (number of samples per timestamp must be equal to 1)
        pred_vals
            The values of a probabilistic time series (number of samples per timestamp must be higher than 1)
        time_index
            The time index intersection between `series` and `pred_series`.

        Returns
        -------
        TimeSeries
        """
        vals = self._extract_deterministic_values(vals, "series")
        self._assert_stochastic(pred_vals, "pred_series")

        np_anomaly_scores = []
        for component_idx in range(pred_vals.shape[1]):
            np_anomaly_scores.append(
                self._score_core_nllikelihood(
                    vals[:, component_idx].squeeze(-1),
                    pred_vals[:, component_idx],
                )
            )
        return np.array(np_anomaly_scores).T
For each timestamp of the inputs:

        - the parameters of the considered distribution are fitted on the samples of the probabilistic time series
        - the negative log-likelihood of the deterministic time series values are computed

        If the series is multivariate, the score will be computed on each component independently.

        Parameters
        ----------
        vals
            The values of a deterministic time series (number of samples per timestamp must be equal to 1)
        pred_vals
            The values of a probabilistic time series (number of samples per timestamp must be higher than 1)
        time_index
            The time index intersection between `series` and `pred_series`.

        Returns
        -------
        TimeSeries
        
darts/ad/scorers/scorers.py