backend/time-series-forecasting/training_methods/automl_training_method.py [64:113]:
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    def dataset_time_series_identifier_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:

        """The column representing the time series identifier variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TIME_SERIES_IDENTIFIER_COLUMN_PARAMETER]

    def dataset_time_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:
        """The column representing the time variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TIME_COLUMN_PARAMETER]

    def dataset_target_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:
        """The column representing the target variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TARGET_COLUMN_PARAMETER]

    def train(
        self,
        dataset: dataset.Dataset,
        model_parameters: Dict[str, Any],
        prediction_parameters: Dict[str, Any],
    ) -> str:
        """Train a job and return the model URI.

        Args:
            dataset (dataset.Dataset): Input dataset.
            model_parameters (Dict[str, Any]): The model training parameters.
            prediction_parameters (Dict[str, Any]): The prediction parameters.

        Returns:
            str: The model resource name
        """

        time_column = model_parameters.get(TIME_COLUMN_PARAMETER)
        target_column = model_parameters.get(TARGET_COLUMN_PARAMETER)
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backend/time-series-forecasting/training_methods/bqml_training_method.py [52:100]:
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    def dataset_time_series_identifier_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:
        """The column representing the time series identifier variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TIME_SERIES_IDENTIFIER_COLUMN_PARAMETER]

    def dataset_time_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:
        """The column representing the time variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TIME_COLUMN_PARAMETER]

    def dataset_target_column(
        self, job_request: forecast_job_request.ForecastJobRequest
    ) -> str:
        """The column representing the target variable in the dataset dataframe.

        Returns:
            str: The column name
        """
        return job_request.model_parameters[TARGET_COLUMN_PARAMETER]

    def train(
        self,
        dataset: dataset.Dataset,
        model_parameters: Dict[str, Any],
        prediction_parameters: Dict[str, Any],
    ) -> str:
        """Train a job and return the model URI.

        Args:
            dataset (dataset.Dataset): Input dataset.
            model_parameters (Dict[str, Any]): The model training parameters.
            prediction_parameters (Dict[str, Any]): The prediction parameters.

        Returns:
            str: The model URI
        """

        time_column = model_parameters.get(TIME_COLUMN_PARAMETER)
        target_column = model_parameters.get(TARGET_COLUMN_PARAMETER)
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