...but continue reading below to see how to overcome that.

## Return Type and Data Filtering

Let's continue from the previous example. We wanted to **annotate the function with one type**, but we wanted to be able to return from the function something that actually includes **more data**.

import org.apache.maven.wagon.events.TransferListener;
import org.apache.maven.wagon.providers.file.FileWagon;
import org.apache.maven.wagon.resource.Resource;

/**
 * Wagon used for test cases that annotate some methods. Note that this is not a thread-safe implementation.
 */
public class TestFileWagon extends FileWagon {
    private TestTransferListener testTransferListener;
    private boolean insideGet;

    @Deprecated

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Use this to meta-annotate {@code XxxFeature.Require} annotations, so that those annotations can
 * be used to decide whether to apply a test to a given class-under-test.
 *

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Use this to meta-annotate {@code XxxFeature.Require} annotations, so that those annotations can
 * be used to decide whether to apply a test to a given class-under-test.
 *

* 100% کد بر اساس <abbr title="حاشیه نویسی تایپ های پایتون (Python type annotations)، با استفاده از آن ویرایشگر و ابزارهای خارجی شما می توانند پشتیبانی بهتری از شما ارائه دهند">type annotate ها</abbr>.

* استفاده شده در اپلیکیشن های تولید

## ویژگی های Starlette

Nesse caso, você pode simplesmente trocar as `dataclasses` padrão por `pydantic.dataclasses`, que é um substituto direto:

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../docs_src/dataclasses/tutorial003.py!}
```

1. Ainda importamos `field` das `dataclasses` padrão.

2. `pydantic.dataclasses` é um substituto direto para `dataclasses`.

👆 💪 🌀 `dataclasses` ⏮️ 🎏 🆎 ✍ ⚒ 🐦 📊 📊.

💼, 👆 💪 ✔️ ⚙️ Pydantic ⏬ `dataclasses`. 🖼, 🚥 👆 ✔️ ❌ ⏮️ 🔁 🏗 🛠️ 🧾.

👈 💼, 👆 💪 🎯 💱 🐩 `dataclasses` ⏮️ `pydantic.dataclasses`, ❔ 💧-♻:

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../docs_src/dataclasses/tutorial003.py!}
```

1️⃣. 👥 🗄 `field` ⚪️➡️ 🐩 `dataclasses`.

2️⃣. `pydantic.dataclasses` 💧-♻ `dataclasses`.

## 在嵌套数据结构中使用数据类

您还可以把 `dataclasses` 与其它类型注解组合在一起，创建嵌套数据结构。

还有一些情况也可以使用 Pydantic 的 `dataclasses`。例如，在 API 文档中显示错误。

本例把标准的 `dataclasses` 直接替换为 `pydantic.dataclasses`：

```{ .python .annotate hl_lines="1  5  8-11  14-17  23-25  28" }
{!../../docs_src/dataclasses/tutorial003.py!}
```

1. 本例依然要从标准的 `dataclasses` 中导入 `field`；

2. 使用 `pydantic.dataclasses` 直接替换 `dataclasses`；

 * a little different from those of other Collection-related features such as {@link
 * CollectionFeature} or {@link SetFeature}.
 *
 * <p>However, when {@link CollectionSize.Require} is used to annotate a test it behaves normally
 * (i.e. it requires the collection instance under test to be a certain size for the test to run).
 * Note that this means a test should not require more than one CollectionSize, since a particular

@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### 📁

🔜 🎏 🏗 📁 ✍ 📁 `Dockerfile` ⏮️:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9

# (2)
WORKDIR /code

# (3)
COPY ./requirements.txt /code/requirements.txt

# (4)
RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

# (5)