2. **Thoughtful, honest feedback.** We will consider every contribution on its merits, regardless of whether AI was used. We may not respond quickly—our review queue is long—but we will tell you clearly what works, what needs to change, and why.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.model_dump()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

We verify that we get a `username`, and extract the scopes.

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

For that, we update the Pydantic model `TokenData` with a new property `scopes`.

Now that we have **multiple models**, we can update the parts of the app that use them.

We receive in the request a `HeroCreate` *data model*, and from it, we create a `Hero` *table model*.

This new *table model* `Hero` will have the fields sent by the client, and will also have an `id` generated by the database.

///

We are going to include this `APIRouter` in the main `FastAPI` app, but first, let's check the dependencies and another `APIRouter`.

## Dependencies { #dependencies }

We see that we are going to need some dependencies used in several places of the application.

So we put them in their own `dependencies` module (`app/dependencies.py`).

## Decision

We do not use Java serialization.
Instead, we use custom serialization where we explicitly describe how data objects should be serialized and deserialized.

For internal purposes, we use binary formats for their brevity.
We use the `Serializer` abstraction to separate the actual implementation of serialization from its uses.

When sharing data with external tools, we use JSON.

## Consequences

Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.

And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:

     * when we run AbstractFutureTest itself.
     */

    /*
     * We don't test UnsafeAtomicHelper here, since guava-android doesn't provide a way to use it
     * under the JVM. (We could arrange for one if we really wanted, but that will break once the
     * JDK further restricts access to Unsafe.) But we have coverage under an Android emulator,

        "PatternMatchingInstanceof", // We should enable this.
        "StatementSwitchToExpressionSwitch", // We should enable this.
        "StringConcatToTextBlock", // We should enable this.
        "ClassCanBeStatic", // We should enable this.

        // DISCUSS
        "EnumOrdinal", // This violation is ubiquitous, though most are benign.
        "EqualsGetClass", // Let's agree if we want to adopt Error Prone's idea of valid equals()

- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision

We do not introduce new public API methods that include Groovy types in their signatures.
Existing Groovy methods will not be removed immediately.

## Consequences

* If we would have used `Closure`, we must instead use `Action<T>`, `Spec<T>`, `Callable<T>`, or `Transformer<OUT, IN>`.