return createItem(key, System.getProperty(key));
    }

    /**
     * Creates a key-value item map for display.
     *
     * @param label the item label
     * @param value the item value
     * @return map containing the formatted key-value pair
     */
    protected static Map<String, String> createItem(final Object label, final Object value) {

from fastapi import FastAPI

app = FastAPI()


@app.post("/items/", status_code=201)
async def create_item(name: str):

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI(strict_content_type=False)


class Item(BaseModel):
    name: str
    price: float


@app.post("/items/")
async def create_item(item: Item):

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: list[str] = []


@app.post("/items/")
async def create_item(item: Item) -> Item:
    return item


@app.get("/items/")
async def read_items() -> list[Item]:
    return [
        Item(name="Portal Gun", price=42.0),
        Item(name="Plumbus", price=32.0),

                            "description": {"type": "string"},
                        },
                    }
                }
            },
            "required": True,
        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    data = magic_data_reader(raw_body)

        status_code=422,
        content=jsonable_encoder({"detail": exc.errors(), "body": exc.body}),
    )


class Item(BaseModel):
    title: str
    size: int


@app.post("/items/")
async def create_item(item: Item):

@app.post(
    "/items/",
    openapi_extra={
        "requestBody": {
            "content": {"application/x-yaml": {"schema": Item.model_json_schema()}},
            "required": True,
        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:

        SuggestItem item1 = createItem("test text", 10L, 5L, 1.0f);
        SuggestItem item2 = createItem("test text", 20L, 15L, 2.0f);

        SuggestItem merged = SuggestItemMerger.merge(item1, item2);

        assertEquals("test text", merged.getText());
    }

    @Test
    public void testMerge_preservesId() {
        SuggestItem item1 = createItem("text", 10L, 5L, 1.0f);

from pydantic.v1 import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    size: float


app = FastAPI()


@app.post("/items/")
async def create_item(item: Annotated[Item, Body(embed=True)]) -> Item:

    size: float


class ItemV2(BaseModelV2):
    name: str
    description: str | None = None
    size: float


app = FastAPI()


@app.post("/items/", response_model=ItemV2)
async def create_item(item: Item):