*/
    @Nonnull
    List<Profile> getActivePomProfiles();

    /**
     * Gets the profiles that were active during model building for a specific model in the hierarchy.
     * This allows tracking which profiles came from which model (parent vs child).
     *
     * @param modelId The identifier of the model (groupId:artifactId:version) or empty string for the super POM.

Regression test: Ensure app-level responses with Union models and content/examples
don't accumulate duplicate $ref entries in anyOf arrays.
See https://github.com/fastapi/fastapi/pull/14463
"""

from fastapi import FastAPI
from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel


class ModelA(BaseModel):
    a: str


class ModelB(BaseModel):
    b: str


app = FastAPI(

 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.maven.model.building;

/**
 * Caches auxiliary data used during model building like already processed raw/effective models. The data in the cache
 * is meant for exclusive consumption by the model builder and is opaque to the cache implementation. The cache key is

```

**Important**: Return types or response models are what filter data ensuring no sensitive information is exposed. And they are used to serialize data with Pydantic (in Rust), this is the main idea that can increase response performance.

The return type doesn't have to be a Pydantic model, it could be a different type, like a list of integers, or a dict, etc.

@app.get("/items/")
async def read_items(filter_query: Annotated[FilterParams, Query()]):
    return filter_query
```

Read the new docs: [Query Parameter Models](https://fastapi.tiangolo.com/tutorial/query-param-models/).

#### `Header` Parameter Models

Use Pydantic models for `Header` parameters:

```python
from typing import Annotated

from fastapi import FastAPI, Header
from pydantic import BaseModel

    return {"message": result}
```

## SQLModel for SQL databases

When working with SQL databases, prefer using SQLModel as it is integrated with Pydantic and will allow declaring data validation with the same models.

Prefer it over SQLAlchemy.

## HTTPX

Use HTTPX for handling HTTP communication (e.g. with other APIs). It support sync and async usage.

Aqui estamos simulando a operação de *inicialização* custosa de carregar o modelo, colocando a (falsa) função do modelo no dicionário com modelos de Aprendizado de Máquina antes do `yield`. Esse código será executado **antes** de a aplicação **começar a receber requisições**, durante a *inicialização*.

 */
package org.apache.maven.model.composition;

import javax.inject.Named;
import javax.inject.Singleton;

import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

import org.apache.maven.model.Dependency;
import org.apache.maven.model.DependencyManagement;
import org.apache.maven.model.Model;
import org.apache.maven.model.building.ModelBuildingRequest;

  script.setAttribute("data-modal-title-color", "#FFFFFF");
  script.setAttribute("data-modal-title-font-family", "Roboto, sans-serif");
  script.setAttribute("data-modal-example-questions", "How to define a route?,How to validate models?,How to handle responses?,How to deploy FastAPI?");

이것이 어떻게 동작하는지, 그리고 필요하다면 어떻게 변경할 수 있는지 살펴보겠습니다.

## 입력과 출력을 위한 Pydantic 모델 { #pydantic-models-for-input-and-output }

예를 들어, 다음처럼 기본값이 있는 Pydantic 모델이 있다고 해보겠습니다:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### 입력용 모델 { #model-for-input }

이 모델을 다음처럼 입력으로 사용하면:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}