* etc.

In these cases, you would normally return an **HTTP status code** in the range of **400** (from 400 to 499).

This is similar to the 200 HTTP status codes (from 200 to 299). Those "200" status codes mean that somehow there was a "success" in the request.

The status codes in the 400 range mean that there was an error from the client.

Remember all those **"404 Not Found"** errors (and jokes)?

# Stop two nodes, one of each pool, to check that all S3 calls work while quorum is still there
[ "${MODE}" == "pools" ] && docker-compose -f minio-${MODE}.yaml stop minio2
[ "${MODE}" == "pools" ] && docker-compose -f minio-${MODE}.yaml stop minio6

# Pause one node, to check that all S3 calls work while one node goes wrong
[ "${MODE}" == "resiliency" ] && docker-compose -f minio-${MODE}.yaml pause minio4

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

        <artifactId>modello-maven-plugin</artifactId>
        <configuration>
          <version>1.0.0</version>
          <models>
            <model>src/main/mdo/profiles.mdo</model>
            <model>src/main/mdo/paramdoc.mdo</model>
          </models>
        </configuration>
        <executions>
          <execution>
            <id>modello</id>
            <goals>

It receives a `dict`: the keys are status codes for each response (like `200`), and the values are other `dict`s with the information for each of them.

Each of those response `dict`s can have a key `model`, containing a Pydantic model, just like `response_model`.

**FastAPI** will take that model, generate its JSON Schema and include it in the correct place in OpenAPI.

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

//// tab | Pydantic v2

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

////

    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.

				}

				if db.Statement.ReflectValue.CanAddr() && db.Statement.Dest != db.Statement.Model && db.Statement.Model != nil {
					_, queryValues = schema.GetIdentityFieldValuesMap(db.Statement.Context, reflect.ValueOf(db.Statement.Model), db.Statement.Schema.PrimaryFields)
					column, values = schema.ToQueryValues(db.Statement.Table, db.Statement.Schema.PrimaryFieldDBNames, queryValues)

## 0.30.0

* Add support for Pydantic's ORM mode:
    * Updated documentation about SQL with SQLAlchemy, using Pydantic models with ORM mode, SQLAlchemy models with relations, separation of files, simplification of code and other changes. New docs: [SQL (Relational) Databases](https://fastapi.tiangolo.com/tutorial/sql-databases/).

hould-try-7c0ac7eebb3e title: 5 Advanced Features of FastAPI You Should Try - author: Kaustubh Gupta author_link: https://medium.com/@kaustubhgupta1828/ link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/ title: Deploying ML Models as API Using FastAPI and Heroku - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/ title: Using GitHub Actions to Deploy a FastAPI Project to Heroku author_link: http...