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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_table( self, table, table_meta, transformer_dict=None, transformer_list=None, missing=None):
 """Create, apply and store the specified transformers for `table`. Args: table(pandas.DataFrame):
 Contents of the table to be transformed. table_meta(dict):
 Metadata for the given table. transformer_dict(dict):
 Mapping `tuple(str, str)` -> `str` where the tuple in the keys represent the (table_name, column_name) and the value the name of the assigned transformer. 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: pandas.DataFrame: Transformed table. """


        if missing is None:
            missing = self.missing

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

        result = pd.DataFrame()
        table_name = table_meta['name']

        for field in table_meta['fields']:
            col_name = field['name']

            if transformer_list:
                for transformer_name in transformer_list:
                    if field['type'] == self.get_class(transformer_name).type:
                        transformed = self._fit_transform_column(
                            table, field, transformer_name, table_name)

                        result = pd.concat([result, transformed], axis=1)

            elif (table_name, col_name) in transformer_dict:
                transformer_name = TRANSFORMERS[transformer_dict[(table_name, col_name)]]
                transformed = self._fit_transform_column(
                    table, field, transformer_name, table_name)

                result = pd.concat([result, transformed], axis=1)

        return result