def __init__(
        self,
        method: Optional[OptaxOptimizer] = optax.adam(1e-2),
        n_epochs: int = 100,
        freeze_fun: Optional[Callable[[Tuple[AnyKey, ...], Array], str]] = None,
    ):
        """
        An object to configure the optimization in the calibration process.

        Parameters
        ----------
        method: OptaxOptimizer
            An Optax optimizer.
        n_epochs: int
            Maximum number of epochs to run the calibration for.
        freeze_fun: Optional[Callable[[Tuple[AnyKey, ...], Array], str]]
            A callable taking in input a path in the nested dictionary of parameters, as well as the corresponding
            array of parameters, and returns "trainable" or "freeze", according to whether the corresponding parameter
            should be optimized or not.

            Examples
            --------
            .. code-block:: python
                def freeze_fun(path: Tuple[str], v: Array) -> str:
                    path = [p[:6] for p in path]  # take only the first 6 characters of each key"
                    return 'trainable' if "Dense" in path else 'frozen'`
        """
        self.method = method
        self.n_epochs = n_epochs
        self.freeze_fun = freeze_fun

        An object to configure the optimization in the calibration process.

        Parameters
        ----------
        method: OptaxOptimizer
            An Optax optimizer.
        n_epochs: int
            Maximum number of epochs to run the calibration for.
        freeze_fun: Optional[Callable[[Tuple[AnyKey, ...], Array], str]]
            A callable taking in input a path in the nested dictionary of parameters, as well as the corresponding
            array of parameters, and returns "trainable" or "freeze", according to whether the corresponding parameter
            should be optimized or not.

            Examples
            --------
            .. code-block:: python
                def freeze_fun(path: Tuple[str], v: Array) -> str:
                    path = [p[:6] for p in path]  # take only the first 6 characters of each key"
                    return 'trainable' if "Dense" in path else 'frozen'`
        
fortuna/calib_model/config/optimizer.py