from starlette.testclient import TestClient

app = FastAPI()


class Item(BaseModel):
    name: str = Field(alias="aliased_name")
    price: Optional[float] = None
    owner_ids: Optional[List[int]] = None


@app.get("/items/valid", response_model=Item)
def get_valid():
    return Item(aliased_name="valid", price=1.0)


@app.get("/items/coerce", response_model=Item)
def get_coerce():

    }

    @Override
    public void writeTo(final StreamOutput out) throws IOException {
        queryBuilder.writeTo(out);
    }

    @Override
    public int hashCode() {
        return queryBuilder.hashCode();
    }

    @Override
    public boolean equals(final Object obj) {
        if (this == obj) {
            return true;
        }

    private static Class<?>[] toClassArray(final Object... methodArgs) {
        if (methodArgs == null) {
            return null;
        }
        final Class<?>[] result = new Class[methodArgs.length];
        for (int i = 0; i < methodArgs.length; ++i) {
            if (methodArgs[i] != null) {
                result[i] = methodArgs[i].getClass();
            }
        }
        return result;
    }

# Дополнительные типы данных

До сих пор вы использовали простые типы данных, такие как:

* `int`
* `float`
* `str`
* `bool`

Но вы также можете использовать и более сложные типы.

При этом у вас останутся те же возможности , что и до сих пор:

* Отличная поддержка редактора.
* Преобразование данных из входящих запросов.
* Преобразование данных для ответа.
* Валидация данных.
* Автоматическая аннотация и документация.

// This is mainly implemented for speed.
func (c *StandardClaims) UnmarshalJSON(b []byte) (err error) {
	return jsonparser.ObjectEach(b, func(key []byte, value []byte, dataType jsonparser.ValueType, _ int) error {
		if len(key) == 0 {
			return nil
		}
		switch key[0] {
		case 'a':
			if string(key) == "accessKey" {
				if dataType != jsonparser.String {
					return errors.New("accessKey: Expected string")

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

که سپس میتوان به این شکل از آن استفاده کرد:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,

	FTP  []string
	SFTP []string

	MemLimit uint64

	UserTimeout         time.Duration
	IdleTimeout         time.Duration
	ReadHeaderTimeout   time.Duration
	MaxIdleConnsPerHost int

	SendBufSize, RecvBufSize int
	CrossDomainXML           string
	// The layout of disks as interpreted
	Layout disksLayout
}

var (
	// Global user opts context
	globalServerCtxt serverCtxt

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sau đó có thể được sử dụng:

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像这样来使用：

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",

      return resourceName;
    }

    /** Returns the file that includes this resource. */
    final File getFile() {
      return file;
    }

    @Override
    public int hashCode() {
      return resourceName.hashCode();
    }

    @Override
    public boolean equals(@CheckForNull Object obj) {
      if (obj instanceof ResourceInfo) {