3. Dataclass `Author` содержит список dataclass `Item`.

4. Dataclass `Author` используется в параметре `response_model`.

5. Вы можете использовать и другие стандартные аннотации типов вместе с dataclasses в качестве тела запроса.

    В этом случае это список dataclass `Item`.

6. Здесь мы возвращаем словарь, содержащий `items`, который является списком dataclass.

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private final double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }

  private double[] dataset(int i) {

     * @return The data class.
     */
    public Class<?> getDataClass() {
        return dataClass;
    }

    /**
     * Sets the data class.
     * @param dataClass The data class to set.
     */
    public void setDataClass(final Class<?> dataClass) {
        this.dataClass = dataClass;
    }

    }

    /**
     * Returns the dataClass.
     *
     * @return the dataClass
     */
    public Class<?> getDataClass() {
        return dataClass;
    }

    /**
     * Sets the dataClass.
     *
     * @param dataClass the dataClass to set
     */
    public void setDataClass(final Class<?> dataClass) {
        this.dataClass = dataClass;
    }

    /**

from dataclasses import field  # (1)

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: str | None = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

from dataclasses import field  # (1)
from typing import Union

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: Union[str, None] = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

     */
    private static byte[] createBuffer(int dataLen, byte[] dataBytes, String params) throws UnsupportedEncodingException {
        final byte[] paramBytes = params.getBytes(SmbConstants.DEFAULT_OEM_ENCODING);
        byte[] buffer = new byte[10 + paramBytes.length + dataLen];
        // copy params
        System.arraycopy(paramBytes, 0, buffer, 10, paramBytes.length);

from dataclasses import dataclass, field
from typing import Union

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    tags: list[str] = field(default_factory=list)
    description: Union[str, None] = None
    tax: Union[float, None] = None


app = FastAPI()


@app.get("/items/next", response_model=Item)
async def read_next_item():
    return {
        "name": "Island In The Moon",

from dataclasses import dataclass

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    description: str | None = None
    tax: float | None = None


app = FastAPI()


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