def load(
        path: Union[str, os.PathLike, BinaryIO],
        pl_trainer_kwargs: Optional[dict] = None,
        **kwargs,
    ) -> "ConformalModel":
        """
        Loads a model from a given path or file handle.

        Parameters
        ----------
        path
            Path or file handle from which to load the model.
        pl_trainer_kwargs
            Only effective if the underlying forecasting model is a `TorchForecastingModel`.
            Optionally, a set of kwargs to create a new Lightning Trainer used to configure the model for downstream
            tasks (e.g. prediction).
            Some examples include specifying the batch size or moving the model to CPU/GPU(s). Check the
            `Lightning Trainer documentation <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`_
            for more information about the supported kwargs.
        **kwargs
            Only effective if the underlying forecasting model is a `TorchForecastingModel`.
            Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method,
            For more information, read the `official documentation <https://pytorch-lightning.readthedocs.io/en/stable/
            common/lightning_module.html#load-from-checkpoint>`_.
        """
        model: ConformalModel = GlobalForecastingModel.load(path)

        if TORCH_AVAILABLE and issubclass(type(model.model), TorchForecastingModel):
            path_tfm = f"{path}.{type(model.model).__name__}.pt"
            model.model = TorchForecastingModel.load(
                path_tfm,
                pl_trainer_kwargs=pl_trainer_kwargs,
                **kwargs,
            )
        return model

        Loads a model from a given path or file handle.

        Parameters
        ----------
        path
            Path or file handle from which to load the model.
        pl_trainer_kwargs
            Only effective if the underlying forecasting model is a `TorchForecastingModel`.
            Optionally, a set of kwargs to create a new Lightning Trainer used to configure the model for downstream
            tasks (e.g. prediction).
            Some examples include specifying the batch size or moving the model to CPU/GPU(s). Check the
            `Lightning Trainer documentation <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`_
            for more information about the supported kwargs.
        **kwargs
            Only effective if the underlying forecasting model is a `TorchForecastingModel`.
            Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method,
            For more information, read the `official documentation <https://pytorch-lightning.readthedocs.io/en/stable/
            common/lightning_module.html#load-from-checkpoint>`_.
        
darts/models/forecasting/conformal_models.py