DefaultToolchain toolchain = newDefaultToolchain(model);
        toolchain.addProvideToken("name", RequirementMatcherFactory.createExactMatcher("Jane Doe"));

        assertFalse(toolchain.matchesRequirements(Collections.singletonMap("name", "John Doe")));
        verify(logger).debug("Toolchain {} doesn't match required property: {}", toolchain, "name");
    }

    @Test
    void testEquals() {

    ],
)
def test_run_module(module_name: str):
    with patch("builtins.print") as mock_print:
        runpy.run_module(f"docs_src.python_types.{module_name}", run_name="__main__")

{
  "title": "Sample Document",
  "author": "John Doe",
  "version": "1.0",
  "published": "2025-01-15",
  "tags": ["crawler", "extractor", "json"],
  "content": {
    "summary": "This is a sample JSON document for testing",
    "body": "The extractor should handle nested objects and arrays properly"
  },
  "metadata": {
    "created_at": "2025-01-01T00:00:00Z",
    "updated_at": "2025-01-15T12:00:00Z"
  }

        assertTrue(content.contains("Email"));
        assertTrue(content.contains("Age"));
        assertTrue(content.contains("Department"));

        // Verify data extraction
        assertTrue(content.contains("John Doe"));
        assertTrue(content.contains("******@****.***"));
        assertTrue(content.contains("Engineering"));
    }

    public void test_columnMetadata() {

    assert mock_print.call_count == 2
    call_args = [str(arg.args[0]) for arg in mock_print.call_args_list]
    assert call_args == [
        "id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3]",
        "123",

---
title: Sample Markdown Document
author: John Doe
date: 2025-01-15
tags:
  - crawler
  - extractor
  - markdown
---

# 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

    full_name: Union[str, None] = None
    disabled: Union[bool, None] = None


def fake_decode_token(token):
    return User(
        username=token + "fakedecoded", email="******@****.***", full_name="John Doe"
    )


async def get_current_user(token: Annotated[str, Depends(oauth2_scheme)]):
    user = fake_decode_token(token)
    return user


@app.get("/users/me")

    full_name: Union[str, None] = None
    disabled: Union[bool, None] = None


def fake_decode_token(token):
    return User(
        username=token + "fakedecoded", email="******@****.***", full_name="John Doe"
    )


async def get_current_user(token: str = Depends(oauth2_scheme)):
    user = fake_decode_token(token)
    return user


@app.get("/users/me")

```

就能以如下方式调用：

```Python
user_dict = user_in.dict()
```

现在，变量 `user_dict`中的就是包含数据的**字典**（变量 `user_dict` 是字典，不是 Pydantic 模型对象）。

以如下方式调用：

```Python
print(user_dict)
```

输出的就是 Python **字典**：

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': 'john.doe@example.com',
    'full_name': None,
}
```

def get_full_name(first_name, last_name):
    full_name = first_name.title() + " " + last_name.title()
    return full_name