# Classes as Dependencies { #classes-as-dependencies }

Before diving deeper into the **Dependency Injection** system, let's upgrade the previous example.

## A `dict` from the previous example { #a-dict-from-the-previous-example }

In the previous example, we were returning a `dict` from our dependency ("dependable"):

{* ../../docs_src/dependencies/tutorial001_an_py310.py hl[9] *}

  - The list of drives participating in this operation belong to the set.

## Explore Further

- [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html)

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"}

</div>

That would work for most of the cases. 😎

You could use that command for example to start your **FastAPI** app in a container, in a server, etc.

## ASGI Servers { #asgi-servers }

Let's go a little deeper into the details.

FastAPI uses a standard for building Python web frameworks and servers called <abbr title="Asynchronous Server Gateway Interface">ASGI</abbr>. FastAPI is an ASGI web framework.

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// 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* { #declare-a-path-parameter }

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

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter* { #declare-a-path-parameter }

## 1. Install MinIO Server

Install MinIO Server using the instructions in the [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html).

## 2. Use an Existing Key and Certificate with MinIO

      } catch (e: IOException) {
        failConnection(e)
      }
    }
  }

  fun writePing(
    reply: Boolean,
    payload1: Int,
    payload2: Int,
  ) {
    try {
      writer.ping(reply, payload1, payload2)
    } catch (e: IOException) {
      failConnection(e)
    }
  }

  /** For testing: sends a ping and waits for a pong. */

// CallWithHTTPMethod - make a REST call with context, using a custom HTTP method.
func (c *Client) CallWithHTTPMethod(ctx context.Context, httpMethod, rpcMethod string, values url.Values, body io.Reader, length int64) (reply io.ReadCloser, err error) {
	switch atomic.LoadInt32(&c.connected) {
	case closed:
		// client closed, this is usually a manual process
		// so return a local error as client is closed

                        "compile",
                        "test",
                        "package",
                        "verify",
                        "install",
                        "deploy",
                        "wrapper:wrapper");
            }
            return List.of(new ArgumentCompleter(new StringsCompleter(names)));
        }

        private void mvnenc(CommandInput input) {