yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

        assert response.json() == {
            "openapi": "3.1.0",
            "info": {"title": "FastAPI", "version": "0.1.0"},
            "paths": {
                "/predict": {
                    "get": {
                        "summary": "Predict",
                        "operationId": "predict_predict_get",
                        "parameters": [
                            {
                                "required": True,

### Transition Status

        and `Model.predict()` to `"auto"`, which defaults to `verbose=1` for
        most cases and defaults to `verbose=2` when used with
        `ParameterServerStrategy` or with interactive logging disabled.
    *   Argument `jit_compile` in `Model.compile()` now applies to
        `Model.evaluate()` and `Model.predict()`. Setting `jit_compile=True` in

//     Those cannot be well described when embedded.
//  3. Inconsistent validation.  Because the usages are different, the validation rules are different by usage, which makes it hard for users to predict what will happen.
//  4. The fields are both imprecise and overly precise.  Kind is not a precise mapping to a URL. This can produce ambiguity

        this.asyncId = asyncId;
    }


    /**
     * @return the credit
     */
    public final int getCredit () {
        return this.credit;
    }


    /**
     * @param credit
     *            the credit to set
     */
    public final void setCredit ( int credit ) {
        this.credit = credit;
    }


    /**
     * @return the creditCharge
     */

the potential to leak sensitive information such as "Authorization" or "Cookie" headers and the
contents of request and response bodies. This data should only be logged in a controlled way or in
a non-production environment.

You can redact headers that may contain sensitive information by calling `redactHeader()`.
```java
logging.redactHeader("Authorization");
logging.redactHeader("Cookie");
```

Download
--------

```kotlin

    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)
    return {"result": result}
```

**Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.

tipus %s estan permesos",groupCheckedRangeStart:"Si us plau, triï entre ",groupCheckedTooFewStart:"Si us plau, triï almenys ",groupCheckedTooManyStart:"Si us plau, triï un màxim de ",groupCheckedEnd:" element(s)",badCreditCard:"El número de targeta de crèdit proporcionat no és vàlid",badCVV:"CVV proporcionat no és vàlid",wrongFileDim:"Les dimensions de la imatge no són vàlides,",imageTooTall:"l'alçada de la imatge no pot ser major a",imageTooWide:"l'amplada de la imatge no pot ser major a",imageTooSmall:"la...

autorisés",groupCheckedRangeStart:"Choisissez entre ",groupCheckedTooFewStart:"Vous devez faire au moins ",groupCheckedTooManyStart:"Vous ne pouvez pas faire plus de ",groupCheckedEnd:" sélection(s)",badCreditCard:"Vous avez saisi un numéro de carte de crédit invalide",badCVV:"Vous avez saisi un CVV incorrect",wrongFileDim:"Mauvaise taille de l'image, ",imageTooTall:"l'image ne peut pas être plus élevée que",imageTooWide:"l'image ne peut pas être plus large que",imageTooSmall:"l'image est trop petite"...