improve prediction speed

Improve prediction speed of DL model by indicating batch size in .predict call.

https://www.tensorflow.org/api_docs/python/tf/keras/Model#predict

    def make_prediction(self, subset):
        """
        Create predictions for NN, OLS, and persistence and add true observation as reference.

        Predictions are filled in an array with full index range. Therefore, predictions can have missing values. All
        predictions for a single station are stored locally under `<forecast/forecast_norm>_<station>_test.nc` and can
        be found inside `forecast_path`.
        """
        subset_type = subset.name
        logging.info(f"start make_prediction for {subset_type}")
        time_dimension = self.data_store.get("time_dim")
        window_dim = self.data_store.get("window_dim")

        for i, data in enumerate(subset):
            input_data = data.get_X()
            target_data = data.get_Y(as_numpy=False)
            observation_data = data.get_observation()

            # get scaling parameters
            transformation_func = data.apply_transformation

            nn_output = self.model.predict(input_data)