return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      // redeclare to help optimizers with b/310253115
      @SuppressWarnings("RedundantOverride")
      @J2ktIncompatible // serialization
      @Override
      @GwtIncompatible // serialization
      Object writeReplace() {

    return (E) requireNonNull(array[index]);
  }

  // TODO(lowasser): benchmark optimizations for equals() and see if they're worthwhile

  // redeclare to help optimizers with b/310253115
  @SuppressWarnings("RedundantOverride")
  @Override
  @J2ktIncompatible // serialization
  @GwtIncompatible // serialization
  Object writeReplace() {
    return super.writeReplace();

   *
   * @return this method does not return; it always throws
   * @throws IOException when the given throwable is an IOException
   * @throws X1 when the given throwable is of the declared type X1
   * @throws X2 when the given throwable is of the declared type X2
   */
  public <X1 extends Exception, X2 extends Exception> RuntimeException rethrow(
      Throwable e, Class<X1> declaredType1, Class<X2> declaredType2) throws IOException, X1, X2 {

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

/// check

This will give you editor support inside of your function, with error checks, completion, etc.

///

        return axes.get(axis).get(axisIndex);
      }

      @Override
      boolean isPartialView() {
        return true;
      }

      // redeclare to help optimizers with b/310253115
      @SuppressWarnings("RedundantOverride")
      @J2ktIncompatible // serialization
      @Override
      @GwtIncompatible // serialization
      Object writeReplace() {

/// tip

Prefer to use the `Annotated` version if possible.

///

```Python hl_lines="13"
{!> ../../docs_src/dependencies/tutorial005.py!}
```

////

Let's focus on the parameters declared:

* Even though this function is a dependency ("dependable") itself, it also declares another dependency (it "depends" on something else).

          // Since this is an artificially generated type variable, we don't bother checking
          // subtyping between declared type bound and actual type bound. So it's possible that we
          // may generate something like <capture#1-of ? extends Foo&SubFoo>.
          // Checking subtype between declared and actual type bounds
          // adds recursive isSubtypeOf() call and feels complicated.

If you added the return type annotation, tools and editors would complain with a (correct) error telling you that your function is returning a type (e.g. a dict) that is different from what you declared (e.g. a Pydantic model).

In those cases, you can use the *path operation decorator* parameter `response_model` instead of the return type.

You can use the `response_model` parameter in any of the *path operations*:

          // Since this is an artificially generated type variable, we don't bother checking
          // subtyping between declared type bound and actual type bound. So it's possible that we
          // may generate something like <capture#1-of ? extends Foo&SubFoo>.
          // Checking subtype between declared and actual type bounds
          // adds recursive isSubtypeOf() call and feels complicated.

```Python hl_lines="1  8-9"
{!../../docs_src/response_cookies/tutorial002.py!}
```

And then you can return any object you need, as you normally would (a `dict`, a database model, etc).

And if you declared a `response_model`, it will still be used to filter and convert the object you returned.