How to use the pantab._types._ColumnType function in pantab

def _read_table(*, connection: tab_api.Connection, table: TableType) -> pd.DataFrame:
    if isinstance(table, str):
        table = tab_api.TableName(table)

    table_def = connection.catalog.get_table_definition(table)
    columns = table_def.columns

    dtypes: Dict[str, str] = {}
    for column in columns:
        column_type = pantab_types._ColumnType(column.type, column.nullability)
        try:
            dtypes[column.name.unescaped] = pantab_types._pandas_types[column_type]
        except KeyError as e:
            raise TypeError(
                f"Column {column.name} has unsupported datatype {column.type}"
            ) from e

    query = f"SELECT * from {table}"
    dtype_strs = tuple(dtypes.values())

    df = pd.DataFrame(libreader.read_hyper_query(connection._cdata, query, dtype_strs))

    df.columns = dtypes.keys()

    # TODO: remove this hackery...
    for k, v in dtypes.items():

def _insert_frame(
    df: pd.DataFrame,
    *,
    connection: tab_api.Connection,
    table: pantab_types.TableType,
    table_mode: str,
) -> None:
    _validate_table_mode(table_mode)

    if isinstance(table, str):
        table = tab_api.TableName(table)

    # Populate insertion mechanisms dependent on column types
    column_types: List[pantab_types._ColumnType] = []
    columns: List[tab_api.TableDefinition.Column] = []
    for col_name, dtype in df.dtypes.items():
        column_type = _pandas_to_tableau_type(dtype.name)
        column_types.append(column_type)
        columns.append(
            tab_api.TableDefinition.Column(
                name=col_name,
                type=column_type.type_,
                nullability=column_type.nullability,
            )
        )

    # Sanity check for existing table structures
    if table_mode == "a" and connection.catalog.has_table(table):
        table_def = connection.catalog.get_table_definition(table)
        _assert_columns_equal(columns, table_def.columns)