def __init__(self, method="ols"):
        """
        MinT Reconcilator.

        This implements the MinT reconciliation approach presented in [1]_ and
        summarised in [2]_.

        Parameters
        ----------
        method
            This parameter can take four different values, determining how the covariance
            matrix ``W`` of the forecast errors is estimated (corresponding to ``Wh`` in [2]_):

            * ``ols`` uses ``W = I``. This option looks only at the hierarchy but ignores the
              values of the series provided to ``fit()``.
            * ``wls_struct`` uses ``W = diag(S1)``, where ``S1`` is a vector of size `n` with values
              between 0 and `m`, representing the number of base components composing each
              of the `n` components. This options looks only at the hierarchy but ignores
              the values of the series provided to ``fit()``.
            * ``wls_var`` uses ``W = diag(W1)``, where ``W1`` is the temporal average of the
              variance of the forecasting residuals. This method assumes that the series
              provided to ``fit()`` contain the forecast residuals (deterministic series).
            * ``mint_cov`` computes ``W`` as the empirical covariance matrix of the residuals
              for each component, with residuals samples taken over time. This method assumes
              that the series provided to ``fit()`` contain the forecast residuals
              (deterministic series), and it requires the residuals to be linearly independent.
            * ``wls_val`` uses ``W = diag(V1)``, where ``V1`` is the temporal average of the
              component values. This method assumes that the series provided to ``fit()`` contains
              an example of the actual values (e.g., either the training series or the forecasts).
              This method is not presented in [2]_.

        References
        ----------
        .. [1] `Optimal forecast reconciliation for hierarchical and grouped time series through
                trace minimization <https://robjhyndman.com/papers/MinT.pdf>`_
        .. [2] https://otexts.com/fpp3/reconciliation.html#the-mint-optimal-reconciliation-approach
        """
        known_methods = ["ols", "wls", "wls_var", "wls_struct", "wls_val", "mint_cov"]
        raise_if_not(
            method in known_methods,
            f"The method must be one of {known_methods}",
        )
        # Define fixed params (i.e. attributes defined before calling `super().__init__`):
        self.method = method
        super().__init__()

        MinT Reconcilator.

        This implements the MinT reconciliation approach presented in [1]_ and
        summarised in [2]_.

        Parameters
        ----------
        method
            This parameter can take four different values, determining how the covariance
            matrix ``W`` of the forecast errors is estimated (corresponding to ``Wh`` in [2]_):

            * ``ols`` uses ``W = I``. This option looks only at the hierarchy but ignores the
              values of the series provided to ``fit()``.
            * ``wls_struct`` uses ``W = diag(S1)``, where ``S1`` is a vector of size `n` with values
              between 0 and `m`, representing the number of base components composing each
              of the `n` components. This options looks only at the hierarchy but ignores
              the values of the series provided to ``fit()``.
            * ``wls_var`` uses ``W = diag(W1)``, where ``W1`` is the temporal average of the
              variance of the forecasting residuals. This method assumes that the series
              provided to ``fit()`` contain the forecast residuals (deterministic series).
            * ``mint_cov`` computes ``W`` as the empirical covariance matrix of the residuals
              for each component, with residuals samples taken over time. This method assumes
              that the series provided to ``fit()`` contain the forecast residuals
              (deterministic series), and it requires the residuals to be linearly independent.
            * ``wls_val`` uses ``W = diag(V1)``, where ``V1`` is the temporal average of the
              component values. This method assumes that the series provided to ``fit()`` contains
              an example of the actual values (e.g., either the training series or the forecasts).
              This method is not presented in [2]_.

        References
        ----------
        .. [1] `Optimal forecast reconciliation for hierarchical and grouped time series through
                trace minimization <https://robjhyndman.com/papers/MinT.pdf>`_
        .. [2] https://otexts.com/fpp3/reconciliation.html#the-mint-optimal-reconciliation-approach
        
darts/dataprocessing/transformers/reconciliation.py