{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

1. Wir importieren `field` weiterhin von Standard-`dataclasses`.

2. `pydantic.dataclasses` ist ein direkter Ersatz für `dataclasses`.

3. Die Datenklasse `Author` enthält eine Liste von `Item`-Datenklassen.

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

1. We still import `field` from standard `dataclasses`.

2. `pydantic.dataclasses` is a drop-in replacement for `dataclasses`.

3. The `Author` dataclass includes a list of `Item` dataclasses.

...без додавання обов’язкового параметра `needy`, Ви побачите помилку:

```JSON
{
  "detail": [
    {
      "type": "missing",
      "loc": [
        "query",
        "needy"
      ],
      "msg": "Field required",
      "input": null
    }
  ]
}
```

Оскільки `needy` є обов’язковим параметром, вам потрібно вказати його в URL:

```
http://127.0.0.1:8000/items/foo-item?needy=sooooneedy
```

  var bodyLimit: Long by delegate::bodyLimit

  var serverSocketFactory: ServerSocketFactory? by delegate::serverSocketFactory

  var dispatcher: Dispatcher = QueueDispatcher()
    set(value) {
      field = value
      delegate.dispatcher = value.wrap()
    }

  val port: Int
    get() {
      before() // This implicitly starts the delegate.
      return delegate.port
    }

  val hostName: String

    }

    if (userRequest.header("Connection") == null) {
      requestBuilder.header("Connection", "Keep-Alive")
    }

    // If we add an "Accept-Encoding: gzip" header field we're responsible for also decompressing
    // the transfer stream.
    var transparentGzip = false
    if (userRequest.header("Accept-Encoding") == null && userRequest.header("Range") == null) {
      transparentGzip = true

        }
        // Convert milliseconds to 100-nanosecond intervals
        // 1 ms = 10,000 * 100ns intervals
        long intervals = timeoutMs * 10000L;
        // MS-SMB2 timeout field is 4 bytes (uint32), so clamp to max value
        return Math.min(intervals, 0xFFFFFFFFL);
    }

    /**
     * Get the flags
     * @return the flags
     */
    public int getFlags() {

  /*
   * Per JDK: "the behavior of a map entry is undefined if the backing map has been modified after
   * the entry was returned by the iterator, except through the setValue operation on the map entry"
   * As such, this field must be cleared before every map mutation.
   *
   * Note about volatile: volatile doesn't make it safe to read from a mutable graph in one thread

///

### Pydantic v1 and v2 on the same app { #pydantic-v1-and-v2-on-the-same-app }

It's **not supported** by Pydantic to have a model of Pydantic v2 with its own fields defined as Pydantic v1 models or vice versa.

```mermaid
graph TB
    subgraph "❌ Not Supported"
        direction TB
        subgraph V2["Pydantic v2 Model"]
            V1Field["Pydantic v1 Model"]
        end

You can achieve that using <a href="https://docs.pydantic.dev/latest/concepts/validators/#field-after-validator" class="external-link" target="_blank">Pydantic's `AfterValidator`</a> inside of `Annotated`.

/// tip

Pydantic also has <a href="https://docs.pydantic.dev/latest/concepts/validators/#field-before-validator" class="external-link" target="_blank">`BeforeValidator`</a> and others. 🤓

///

* `table=True` informa ao SQLModel que este é um *modelo de tabela*, ele deve representar uma **tabela** no banco de dados SQL, não é apenas um *modelo de dados* (como seria qualquer outra classe Pydantic comum).

* `Field(primary_key=True)` informa ao SQLModel que o `id` é a **chave primária** no banco de dados SQL (você pode aprender mais sobre chaves primárias SQL na documentação do SQLModel).