# Dependencies in path operation decorators { #dependencies-in-path-operation-decorators }

In some cases you don't really need the return value of a dependency inside your *path operation function*.

Or the dependency doesn't return a value.

But you still need it to be executed/solved.

For those cases, instead of declaring a *path operation function* parameter with `Depends`, you can add a `list` of `dependencies` to the *path operation decorator*.

     * is given by {@link #getSource()}.
     *
     * @return The one-based index of the line containing the problem or a non-positive value if unknown.
     */
    int getLineNumber();

    /**
     * Gets the one-based index of the column containing the problem. The column number should refer to some text file
     * that is given by {@link #getSource()}.
     *

</div>

你可以发送如下请求：

```json
{
    "description": "Some data",
    "data": "aGVsbG8="
}
```

/// tip | 提示

`aGVsbG8=` 是 `hello` 的 base64 编码。

///

随后 Pydantic 会解码该 base64 字符串，并在模型的 `data` 字段中提供原始字节。

你将会收到类似的响应：

```json
{
  "description": "Some data",
  "content": "hello"
}
```

        email_file.write(content)


@app.post("/send-notification/{email}")
async def send_notification(email: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(write_notification, email, message="some notification")

./gradlew precommit
---------------------------------------------------------------------------

Some of these checks will require `docker-compose` installed for bringing up
test fixtures. If it's not present those checks will be skipped automatically.
The host running Docker (or VM if you're using Docker Desktop) needs 4GB of
memory or some of the containers will fail to start. You can tell that you

looking at Alice for some time with great curiosity, and this was
his first speech.

  `You should learn not to make personal remarks,' Alice said
with some severity; `it's very rude.'

  The Hatter opened his eyes very wide on hearing this; but all
he SAID was, `Why is a raven like a writing-desk?'

  `Come, we shall have some fun now!' thought Alice.  `I'm glad

    entries.add(entry(null, v3()));

    resetContainer(getSubjectGenerator().create(entries.toArray()));
    assertTrue(
        "A Map should equal any other Map containing the same entries,"
            + " even if some keys are null.",
        getMap().equals(newHashMap(entries)));
  }

  @CollectionSize.Require(absent = CollectionSize.ZERO)
  public void testEquals_otherContainsNullKey() {

</div>

你可以發送如下的請求：

```json
{
    "description": "Some data",
    "data": "aGVsbG8="
}
```

/// tip

`aGVsbG8=` 是 `hello` 的 base64 編碼。

///

接著 Pydantic 會將該 base64 字串解碼，並在模型的 `data` 欄位中提供原始位元組。

你會收到類似以下的回應：

```json
{
  "description": "Some data",
  "content": "hello"
}
```

## Pydantic `bytes` 用於輸出資料 { #pydantic-bytes-for-output-data }

        assertTrue(options.directory().isPresent(), "--directory option should be present");
        assertEquals("/some/path", options.directory().get(), "--directory should be /some/path");
    }

    @Test
    void testGoalsParsing() throws ParseException {
        String[] args = {"apply", "--plugins"};
        CommonsCliUpgradeOptions options = CommonsCliUpgradeOptions.parse(args);

In this example, we didn't declare any Pydantic model. In fact, the request body is not even <dfn title="converted from some plain format, like bytes, into Python objects">parsed</dfn> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.

Nevertheless, we can declare the expected schema for the request body.