* under the License.
 */
package org.apache.maven.model.building;

import java.util.EnumSet;
import java.util.List;
import java.util.Set;

import org.apache.maven.model.Model;
import org.apache.maven.model.io.ModelParseException;

/**
 * Collects problems that are encountered during model building. The primary purpose of this component is to account for

 * The maven super model

 * how parent poms are processed

 * the order in which elements in the POM are overridden

 * minimum element-set for a valid project pom


Maven super model

 Inheritance is recursive in Maven but there is a special model which is the implicit super parent in the lineage
 of models you may specify:

* Documentation automatique des modèles de données avec <a href="http://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (comme OpenAPI est aussi basée sur JSON Schema).

    disabled = user_dict["disabled"],
    hashed_password = user_dict["hashed_password"],
)
```

/// info

For a more complete explanation of `**user_dict` check back in [the documentation for **Extra Models**](../extra-models.md#about-user-in-dict){.internal-link target=_blank}.

///

## Return the token { #return-the-token }

The response of the `token` endpoint must be a JSON object.

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    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):

specific language governing permissions and limitations
under the License.
-->

<model xmlns="http://codehaus-plexus.github.io/MODELLO/2.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://codehaus-plexus.github.io/MODELLO/2.0.0 https://codehaus-plexus.github.io/modello/xsd/modello-2.0.0.xsd"
  xml.namespace="http://maven.apache.org/LIFECYCLE/${version}"

    page.get_by_role("button", name="Try it out").click()
    page.get_by_role("heading", name="Servers").click()
    # Manually add the screenshot
    page.screenshot(path="docs/en/docs/img/tutorial/query-param-models/image01.png")

    # ---------------------
    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/query_param_models/tutorial001.py"]
)
try:

/**
 * Strategy for applying Maven inference optimizations.
 * For 4.0.0 models: applies limited inference (parent-related only).
 * For 4.1.0+ models: applies full inference optimizations.
 * Removes redundant information that can be inferred by Maven during model building.
 */
@Named
@Singleton
@Priority(30)
public class InferenceStrategy extends AbstractUpgradeStrategy {

## ドキュメントを見る

自動ドキュメントを見ると、入力モデルと出力モデルがそれぞれ独自のJSON Schemaを持っていることが確認できます。

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image01.png">

そして、両方のモデルは、対話型のAPIドキュメントに使用されます:

<img src="https://fastapi.tiangolo.com/img/tutorial/response-model/image02.png">

## レスポンスモデルのエンコーディングパラメータ

レスポンスモデルにはデフォルト値を設定することができます:

{* ../../docs_src/response_model/tutorial004.py hl[11,13,14] *}

Bedenken Sie, dass Datenklassen nicht alles können, was Pydantic-Modelle können.

Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.

Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

///

## Datenklassen in `response_model` { #dataclasses-in-response-model }