requirements**.txt
            pyproject.toml
      - name: Install Dependencies
        run: uv pip install -r requirements-tests.txt
      - run: mkdir coverage
      - name: Test
        if: matrix.codspeed != 'codspeed'
        run: bash scripts/test.sh
        env:
          COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}

 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package com.foo.bar;

public class Test {
    public String test="テスト";

Shinsuke Sugaya <******@****.***> 1763869613 +0900

Shinsuke Sugaya <******@****.***> 1763869613 +0900

Matt Lloyd <******@****.***> 1745728257 +0100

# Introduction

This is a sample Markdown document for testing the MarkdownExtractor.

## Features

The extractor should handle:

- YAML front matter extraction
- Heading structure
- **Bold text** and *italic text*
- Lists and other formatting

### Code Examples

Here is some inline `code` and a code block:

```java
public class Example {
    public static void main(String[] args) {

## Tabs and colored boxes { #tabs-and-colored-boxes }

//// tab | Test

/// info
Some text
///

/// note
Some text
///

/// note | Technical details
Some text
///

/// check
Some text
///

/// tip
Some text
///

/// warning
Some text
///

/// danger
Some text
///

////

//// tab | Info

//// tab | Test

/// info | Info
Etwas Text
///

/// note | Hinweis
Etwas Text
///

/// note | Technische Details
Etwas Text
///

/// check | Testen
Etwas Text
///

/// tip | Tipp
Etwas Text
///

/// warning | Achtung
Etwas Text
///

/// danger | Gefahr
Etwas Text
///

////

//// tab | Info

type objAPITestType func(obj ObjectLayer, instanceType string, bucketName string,
	apiRouter http.Handler, credentials auth.Credentials, t *testing.T)

// Regular object test type.
type objTestType func(obj ObjectLayer, instanceType string, t TestErrHandler)

// Special test type for test with directories

# Тестовый файл LLM { #llm-test-file }

Этот документ проверяет, понимает ли <abbr title="Large Language Model – Большая языковая модель">LLM</abbr>, переводящая документацию, `general_prompt` в `scripts/translate.py` и языковой специфичный промпт в `docs/{language code}/llm-prompt.md`. Языковой специфичный промпт добавляется к `general_prompt`.

Тесты, добавленные здесь, увидят все создатели языковых промптов.

Использование: