Notice that the variable `item` is one of the elements in the list `items`.

And still, the editor knows it is a `str`, and provides support for that.

#### Tuple and Set { #tuple-and-set }

You would do the same to declare `tuple`s and `set`s:

//// tab | Python 3.9+

```Python hl_lines="1"
{!> ../../docs_src/python_types/tutorial007_py39.py!}
```

////

//// tab | Python 3.8+

#### String with `value.startswith()` { #string-with-value-startswith }

Did you notice? a string using `value.startswith()` can take a tuple, and it will check each value in the tuple:

{* ../../docs_src/query_params_str_validations/tutorial015_an_py310.py ln[16:19] hl[17] *}

#### A Random Item { #a-random-item }

                return null;
            }

            Object mimeType = docMap.get("mimetype");
            if (mimeType == null) {
                return null;
            }

            // Create a task tuple
            return new Tuple3<>("task-id", path, mimeType.toString());
        }
    }

{* ../../docs_src/body_nested_models/tutorial002.py hl[1] *}

### Declare a `list` with a type parameter { #declare-a-list-with-a-type-parameter }

To declare types that have type parameters (internal types), like `list`, `dict`, `tuple`:

* If you are in a Python version lower than 3.9, import their equivalent version from the `typing` module
* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

In Python 3.9 it would be:

It is equivalent to `set(["name", "description"])`.

///

#### Using `list`s instead of `set`s { #using-lists-instead-of-sets }

If you forget to use a `set` and use a `list` or `tuple` instead, FastAPI will still convert it to a `set` and it will work correctly:

{* ../../docs_src/response_model/tutorial006_py310.py hl[29,35] *}

## Recap { #recap }

        and `audio_dataset_from_directory`, to be used with the
        `validation_split` argument, for returning both dataset splits at once,
        as a tuple.
    *   Added `tf.keras.utils.split_dataset` utility to split a `Dataset` object
        or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).
    *   Added step granularity to `BackupAndRestore` callback for handling

* ♻️ Refactor and simplify internal `analyze_param()` to structure data with dataclasses instead of tuple. PR [#12099](https://github.com/fastapi/fastapi/pull/12099) by [@tiangolo](https://github.com/tiangolo).