from pydantic import BaseModel

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: Union[str, None] = None


@app.get("/items/{item_id}", response_model=Item)

from pydantic import BaseModel

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: str | None = None


@app.get("/items/{item_id}", response_model=Item)

from pydantic import BaseModel

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: Union[str, None] = None


@app.get("/items/{item_id}", response_model=Item)

from pydantic import BaseModel

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: str | None = None


@app.get("/items/{item_id}", response_model=Item)

fake_secret_token = "coneofsilence"

fake_db = {
    "foo": {"id": "foo", "title": "Foo", "description": "There goes my hero"},
    "bar": {"id": "bar", "title": "Bar", "description": "The bartenders"},
}

app = FastAPI()


class Item(BaseModel):
    id: str
    title: str
    description: Union[str, None] = None


@app.get("/items/{item_id}", response_model=Item)

@needs_py39
@pytest.mark.parametrize(
    "url,data",
    [
        ("/items/foo", {"name": "Foo", "price": 50.2}),
        (
            "/items/bar",
            {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
        ),
        (
            "/items/baz",
            {
                "name": "Baz",
                "description": None,
                "price": 50.2,

client = TestClient(app)


@pytest.mark.parametrize(
    "url,data",
    [
        ("/items/foo", {"name": "Foo", "price": 50.2}),
        (
            "/items/bar",
            {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
        ),
        (
            "/items/baz",
            {
                "name": "Baz",
                "description": None,
                "price": 50.2,

@needs_py310
@pytest.mark.parametrize(
    "url,data",
    [
        ("/items/foo", {"name": "Foo", "price": 50.2}),
        (
            "/items/bar",
            {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},
        ),
        (
            "/items/baz",
            {
                "name": "Baz",
                "description": None,
                "price": 50.2,

///

#### 기본값이 있는 필드를 갖는 값의 데이터

하지만 모델의 필드가 기본값이 있어도 ID가 `bar`인 항목(items)처럼 데이터가 값을 갖는다면:

```Python hl_lines="3  5"
{
    "name": "Bar",
    "description": "The bartenders",
    "price": 62,
    "tax": 20.2
}
```

응답에 해당 값들이 포함됩니다.

#### 기본값과 동일한 값을 갖는 데이터

If the data has the same values as the default ones, like the item with ID `baz`:

///

#### 默认值字段有实际值的数据

但是，如果你的数据在具有默认值的模型字段中有实际的值，例如 ID 为 `bar` 的项：

```Python hl_lines="3  5"
{
    "name": "Bar",
    "description": "The bartenders",
    "price": 62,
    "tax": 20.2
}
```

这些值将包含在响应中。

#### 具有与默认值相同值的数据

如果数据具有与默认值相同的值，例如 ID 为 `baz` 的项：

```Python hl_lines="3  5-6"
{
    "name": "Baz",