docs: 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