How to use the pantab._types._pandas_types 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():
        if v == "date":
            dtypes[k] = "datetime64[ns]"