* Composite bodies' IDs are now based on path, not only on route name, as the auto-generated name uses the function names, that can be duplicated in different modules.
    * The same new ID generation applies to response models.
    * This also changes the generated title for those models.

///

So, you should be able to pin to a version like:

```txt
fastapi>=0.45.0,<0.46.0
```

Breaking changes and new features are added in "MINOR" versions.

/// tip

The "MINOR" is the number in the middle, for example, in `0.2.3`, the MINOR version is `2`.

///

## Upgrading the FastAPI versions

You should add tests for your app.

                <mxCell id="3" value="&lt;font face=&quot;Roboto&quot;&gt;&lt;span style=&quot;font-size: 24px&quot;&gt;Server(s)&lt;/span&gt;&lt;/font&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;strokeWidth=3;fontFamily=Roboto Mono, mono;FType=g;" parent="1" vertex="1">
                    <mxGeometry x="710" y="-50" width="300" height="80" as="geometry"/>
                </mxCell>

  }

  @ListFeature.Require(SUPPORTS_ADD_WITH_INDEX)
  @CollectionSize.Require(absent = {ZERO, ONE})
  public void testAddAllAtIndex_middle() {
    assertTrue(
        "addAll(middle, disjoint) should return true",
        getList().addAll(getNumElements() / 2, createDisjointCollection()));
    expectAdded(getNumElements() / 2, createDisjointCollection());
  }

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

Como esse arquivo está no mesmo pacote, você pode usar importações relativas para importar o objeto `app` do módulo `main` (`main.py`):

```Python hl_lines="3"
{!../../docs_src/app_testing/test_main.py!}
```

...e ter o código para os testes como antes.

## Testando: exemplo estendido

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

Using this same trick, you could use a Pydantic model to define the JSON Schema that is then included in the custom OpenAPI schema section for the *path operation*.

And you could do this even if the data type in the request is not JSON.

    // Test that listeners added both before and after the value is available
    // get called correctly.
    for (int i = 0; i < 20; i++) {

      // Right in the middle start up a thread to close the latch.
      if (i == 10) {
        new Thread(() -> latch.countDown()).start();
      }

      future.addListener(listenerLatch::countDown, exec);
    }

```

////

/// info

In Pydantic Version 1 hieß die Methode zum Abrufen des JSON-Schemas für ein Modell `Item.schema()`, in Pydantic Version 2 heißt die Methode `Item.model_json_schema()`.

///

Obwohl wir nicht die standardmäßig integrierte Funktionalität verwenden, verwenden wir dennoch ein Pydantic-Modell, um das JSON-Schema für die Daten, die wir in YAML empfangen möchten, manuell zu generieren.

- github.com/xeipuuv/gojsonschema

# Apache 2.0 (but missing appendix): https://github.com/garyburd/redigo/blob/master/LICENSE
- github.com/garyburd/redigo
- github.com/gomodule/redigo

# Apache 2.0
#   github.com/ghodss/yaml: MIT / BSD-3
#   github.com/gogo/protobuf: BSD-3
#   github.com/jmespath/go-jmespath: Apache 2.0
#   sigs.k8s.io/yaml: MIT / BSD-3
- github.com/tektoncd/pipeline