source/glue/jobs/forecast_etl.py [991:1020]:
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    def __init__(self, etl: ETL):
        """
        :param etl: the ETL object containing the data
        """
        super().__init__(etl)
        self.output_dimensions = []

    def apply(self):
        """Apply all transformations"""
        self.drop_duplicates()
        self.apply_input_mappings()

    @updates_df_as_dynamicframe
    def apply_input_mappings(self):
        """
        Map all fields to those supported in consolidating
        :return: The DynamicFame
        """
        logger.info(
            "%s applying schema mappings"  # NOSONAR (python:S1192) - string for clarity
            % self.etl.name
        )

        fields = list(self.df.schema().field_map.keys())
        mappings = list()
        for field in fields:
            if field == self.etl.identifier:
                mappings.append((field, "string", "identifier", "string"))
            elif field == "timestamp" or field == "date":
                mappings.append((field, "string", "timestamp", "timestamp"))
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source/glue/jobs/forecast_etl.py [1044:1070]:
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    def __init__(self, etl: ETL):
        super().__init__(etl)
        self.output_dimensions = []

    def apply(self):
        """Apply all transformations"""
        self.drop_duplicates()
        self.apply_input_mappings()

    @updates_df_as_dynamicframe
    def apply_input_mappings(self):
        """
        Map all fields to those supported in consolidating
        :return: The DynamicFame
        """
        logger.info(
            "%s applying schema mappings"  # NOSONAR (python:S1192) - string for clarity
            % self.etl.name
        )

        fields = list(self.df.schema().field_map.keys())
        mappings = list()
        for field in fields:
            if field == self.etl.identifier:
                mappings.append((field, "string", "identifier", "string"))
            elif field == "timestamp" or field == "date":
                mappings.append((field, "string", "timestamp", "timestamp"))
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