* 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()}.
     *

    return optional.orElse(null);
  }

  /**
   * Returns the minimum of the two values. If the values compare as 0, the first is returned.
   *
   * <p>The recommended solution for finding the {@code minimum} of some values depends on the type
   * of your data and the number of elements you have. Read more in the Guava User Guide article on
   * <a href="https://github.com/google/guava/wiki/CollectionUtilitiesExplained#comparators">{@code

The Wrapper then downloads the Gradle distribution (if necessary), starts the Launcher and runs the build, spawning and connecting to other processes such as the Daemon as required.

Embedding a Java runtime in the distribution would provide some benefits, such as allowing the Launcher, Daemon and Workers to run on it, removing the prerequisite of an installed Java runtime.

        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);

        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")

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.

			input: []byte(`id,time,num,num2,text
1,2010-01-01T,7867786,4565.908123
2,2017-01-02T03:04Z,-5, 0.765111,Some some
`),
			// Since we are doing offset, no headers are used.
			wantResult: `{"id":"1","time":"2010-01-01T","num":"7867786","num2":"4565.908123"}
{"id":"2","time":"2017-01-02T03:04Z","num":"-5","num2":" 0.765111","text":"Some some"}`,
			wantErr: false,
			requestXML: []byte(`<?xml version="1.0" encoding="UTF-8"?>

</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"
}
```

</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 }

    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() {