* the discussion in the {@link #addListener} documentation. All its warnings about heavyweight
   * listeners are also applicable to heavyweight functions passed to this method.
   *
   * <p>This method is similar to {@link java.util.concurrent.CompletableFuture#exceptionally}. It
   * can also serve some of the use cases of {@link java.util.concurrent.CompletableFuture#handle}

## Forbid Extra Query Parameters { #forbid-extra-query-parameters }

In some special use cases (probably not very common), you might want to **restrict** the query parameters that you want to receive.

You can use Pydantic's model configuration to `forbid` any `extra` fields:

{* ../../docs_src/query_param_models/tutorial002_an_py310.py hl[10] *}

    response = client.put(
        "/items/123",
        json={"name": "Foo", "price": 50.1, "description": "Some Foo", "tax": 0.3},
    )
    assert response.status_code == 200
    assert response.json() == {
        "item_id": 123,
        "name": "Foo",
        "price": 50.1,
        "description": "Some Foo",
        "tax": 0.3,
    }


def test_put_only_required(client: TestClient):
    response = client.put(

 * under the License.
 */
package org.apache.maven.configuration;

/**
 * Unmarshals some textual configuration from the POM or similar into the properties of a bean. This component works
 * similar to the way Maven configures plugins from the POM, i.e. some configuration like {@code <param>value</param>}

## Code includes with unknown attributes { #code-includes-with-unknown-attributes }

{* ../../docs_src/python_types/tutorial001_py310.py unknown[123] *}

## Some more code includes to test fixing { #some-more-code-includes-to-test-fixing }

{* ../../docs_src/dependencies/tutorial013_an_py310.py ln[19:21] *}

            }
            // Simulate some encoding activity on the buffer
            dst.enc_ndr_long(123);
        }

        @Override
        public void decode(NdrBuffer src) throws NdrException {
            if (throwOnDecode) {
                throw new NdrException("Failed to decode");
            }
            // Simulate some decoding activity on the buffer
            src.dec_ndr_long();

```json
{
    "description": "Some data",
    "data": "aGVsbG8="
}
```

/// tip | Tipp

`aGVsbG8=` ist die base64-Kodierung von `hello`.

///

Anschließend dekodiert Pydantic den base64-String und stellt Ihnen die ursprünglichen Bytes im Feld `data` des Modells bereit.

Sie erhalten eine Response wie:

```json
{
  "description": "Some data",
  "content": "hello"
}
```

       *
       * TODO(b/112550045): Allocating a single, cheaper listener is (I think) only an optimization.
       * If we make some other optimizations, this one will no longer be necessary. The optimization
       * could actually hurt in some cases, as it forces us to keep all inputs in memory until the
       * final input completes.
       */
      @RetainedLocalRef

### `typing` module { #typing-module }

For some additional use cases, you might need to import some things from the standard library `typing` module, for example when you want to declare that something has "any type", you can use `Any` from `typing`:

```python
from typing import Any


def some_function(data: Any):
    print(data)
```

### Generic types { #generic-types }

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting