field, model_name_map=model_name_map, ref_prefix=REF_PREFIX
        )[0]

    def get_compat_model_name_map(fields: List[ModelField]) -> ModelNameMap:
        models = get_flat_models_from_fields(fields, known_models=set())
        return get_model_name_map(models)  # type: ignore[no-any-return]

    def get_definitions(
        *,
        fields: List[ModelField],
        schema_generator: GenerateJsonSchema,

```Python hl_lines="1  10-13  21-24"
{!../../docs_src/sql_databases/sql_app/models.py!}
```

### 创建关系

现在创建关系。

为此，我们使用SQLAlchemy  ORM提供的`relationship`。

这将或多或少会成为一种“神奇”属性，其中表示该表与其他相关的表中的值。

```Python hl_lines="2  15  26"
{!../../docs_src/sql_databases/sql_app/models.py!}
```

当访问 user 中的属性`items`时，如 中`my_user.items`，它将有一个`Item`SQLAlchemy 模型列表（来自`items`表），这些模型具有指向`users`表中此记录的外键。

```

////

/// tip

Pydantic, which is what powers all the data validation and serialization in FastAPI, has a special behavior when you use `Optional` or `Union[Something, None]` without a default value, you can read more about it in the Pydantic docs about <a href="https://docs.pydantic.dev/2.3/usage/models/#required-optional-fields" class="external-link" target="_blank">Required fields</a>.

///

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output

Let's say you have a Pydantic model with default values, like this one:

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

### Model for Input

# Declare Request Example Data

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

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

//// tab | Python 3.10+ Pydantic v2

```Python hl_lines="13-24"
{!> ../../docs_src/schema_extra_example/tutorial001_py310.py!}
```

////

}

func BenchmarkMutexWorkSlack(b *testing.B) {
	benchmarkMutex(b, true, true)
}

func BenchmarkMutexNoSpin(b *testing.B) {
	b.ResetTimer()
	b.ReportAllocs()

	// This benchmark models a situation where spinning in the mutex should be
	// non-profitable and allows to confirm that spinning does not do harm.
	// To achieve this we create excess of goroutines most of which do local work.

	// apply scopes to migrator
	for len(tx.Statement.scopes) > 0 {
		tx = tx.executeScopes()
	}

	return tx.Dialector.Migrator(tx.Session(&Session{}))
}

// AutoMigrate run auto migration for given models
func (db *DB) AutoMigrate(dst ...interface{}) error {
	return db.Migrator().AutoMigrate(dst...)
}

// ViewOption view option
type ViewOption struct {

Você pode utilizá-lo enquanto desenvolve seu app para conectar o *body* e debugá-lo, e assim retorná-lo ao usuário, etc.

Tente enviar um item inválido como este:

```JSON
{
  "title": "towel",
  "size": "XL"
}
```

Você receberá uma *response* informando-o de que a data é inválida, e contendo o *body* recebido:

```JSON hl_lines="12-15"
{
  "detail": [
    {
      "loc": [

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.

Um zu unterscheiden zwischen Modellen für **Aktualisierungen**, mit lauter optionalen Werten, und solchen für die **Erzeugung**, mit benötigten Werten, können Sie die Techniken verwenden, die in [Extramodelle](extra-models.md){.internal-link target=_blank} beschrieben wurden.