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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit_transform( self, tables=None, transformer_dict=None, transformer_list=None, missing=None):
 """Create, apply and store the specified transformers for the given tables. Args: tables(dict):
 Mapping of table names to `tuple` where each tuple is on the form (`pandas.DataFrame`, `dict`). The `DataFrame` contains the table data and the `dict` the corresponding meta information. If not specified, the tables will be retrieved using the meta_file. transformer_dict(dict):
 Mapping `tuple(str, str)` -> `str` where the tuple is (table_name, column_name). transformer_list(list):
 List of transformers to use. Overrides the transformers in the meta_file. missing(bool):
 Wheter or not use NullTransformer to handle missing values. Returns: dict: Map from `str` (table_names) to `pandas.DataFrame` (transformed data). """


        if missing is None:
            missing = self.missing

        else:
            self.missing = missing
            warnings.warn(DEPRECATION_MESSAGE.format('fit_transform'), DeprecationWarning)

        transformed = {}

        if tables is None:
            tables = self.table_dict

        if transformer_dict is None and transformer_list is None:
            transformer_dict = self.transformer_dict

        for table_name in tables:
            table, table_meta = tables[table_name]
            transformed_table = self.fit_transform_table(
                table, table_meta, transformer_dict, transformer_list)

            transformed[table_name] = transformed_table

        return transformed