How to use the adaptive.learner.learner1D.loss_manager function in adaptive

# Find the intervals for which the losses should be calculated.
        intervals, intervals_combined = [
            [(x_m, x_r) for x_m, (x_l, x_r) in neighbors.items()][:-1]
            for neighbors in (self.neighbors, self.neighbors_combined)
        ]

        # The the losses for the "real" intervals.
        self.losses = loss_manager(self._scale[0])
        for ival in intervals:
            self.losses[ival] = self._get_loss_in_interval(*ival)

        # List with "real" intervals that have interpolated intervals inside
        to_interpolate = []

        self.losses_combined = loss_manager(self._scale[0])
        for ival in intervals_combined:
            # If this interval exists in 'losses' then copy it otherwise
            # calculate it.
            if ival in reversed(self.losses):
                self.losses_combined[ival] = self.losses[ival]
            else:
                # Set all losses to inf now, later they might be udpdated if the
                # interval appears to be inside a real interval.
                self.losses_combined[ival] = np.inf
                x_left, x_right = ival
                a, b = to_interpolate[-1] if to_interpolate else (None, None)
                if b == x_left and (a, b) not in self.losses:
                    # join (a, b) and (x_left, x_right) → (a, x_right)
                    to_interpolate[-1] = (a, x_right)
                else:
                    to_interpolate.append((x_left, x_right))

# Update scale
        self._bbox[0] = [points_combined.min(), points_combined.max()]
        self._bbox[1] = [values.min(axis=0), values.max(axis=0)]
        self._scale[0] = self._bbox[0][1] - self._bbox[0][0]
        self._scale[1] = np.max(self._bbox[1][1] - self._bbox[1][0])
        self._oldscale = deepcopy(self._scale)

        # Find the intervals for which the losses should be calculated.
        intervals, intervals_combined = [
            [(x_m, x_r) for x_m, (x_l, x_r) in neighbors.items()][:-1]
            for neighbors in (self.neighbors, self.neighbors_combined)
        ]

        # The the losses for the "real" intervals.
        self.losses = loss_manager(self._scale[0])
        for ival in intervals:
            self.losses[ival] = self._get_loss_in_interval(*ival)

        # List with "real" intervals that have interpolated intervals inside
        to_interpolate = []

        self.losses_combined = loss_manager(self._scale[0])
        for ival in intervals_combined:
            # If this interval exists in 'losses' then copy it otherwise
            # calculate it.
            if ival in reversed(self.losses):
                self.losses_combined[ival] = self.losses[ival]
            else:
                # Set all losses to inf now, later they might be udpdated if the
                # interval appears to be inside a real interval.
                self.losses_combined[ival] = np.inf