## Server Machine and Server Program

There's a small detail about names to keep in mind. 💡

The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).

Just keep in mind that when you read "server" in general, it could refer to one of those two things.

    return operation_id


def deep_dict_update(main_dict: Dict[Any, Any], update_dict: Dict[Any, Any]) -> None:
    for key, value in update_dict.items():
        if (
            key in main_dict
            and isinstance(main_dict[key], dict)
            and isinstance(value, dict)
        ):
            deep_dict_update(main_dict[key], value)
        elif (
            key in main_dict

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

* You can then modify further the `response` before returning it.

```Python hl_lines="8-9  11  14"
{!../../../docs_src/middleware/tutorial001.py!}
```

!!! tip
    Keep in mind that custom proprietary headers can be added <a href="https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers" class="external-link" target="_blank">using the 'X-' prefix</a>.

!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

```Python hl_lines="16"

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

Just use any kind of model, any kind of class, any kind of database that you need for your application. **FastAPI** has you covered with the dependency injection system.

## Code size

This example might seem verbose. Keep in mind that we are mixing security, data models, utility functions and *path operations* in the same file.

But here's the key point.

The security and dependency injection stuff is written once.

返回给客户端之前，要把枚举元素转换为对应的值（本例中为字符串）：

```Python hl_lines="18  21  23"
{!../../../docs_src/path_params/tutorial005.py!}
```

客户端中的 JSON 响应如下：

```JSON
{
  "model_name": "alexnet",
  "message": "Deep Learning FTW!"
}
```

## 包含路径的路径参数

假设*路径操作*的路径为 `/files/{file_path}`。

但需要 `file_path` 中也包含*路径*，比如，`home/johndoe/myfile.txt`。

此时，该文件的 URL 是这样的：`/files/home/johndoe/myfile.txt`。

client = TestClient(app)


def test_get_enums_alexnet():
    response = client.get("/models/alexnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"}


def test_get_enums_lenet():
    response = client.get("/models/lenet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}

[tool.ruff.lint.isort]
known-third-party = ["fastapi", "pydantic", "starlette"]

[tool.ruff.lint.pyupgrade]
# Preserve types, even if a file imports `from __future__ import annotations`.