*/
@Deprecated(since = "4.0.0")
public interface ProfileInjector {

    /**
     * Merges values from the specified profile into the given model. Implementations are expected to keep the profile
     * and model completely decoupled by injecting deep copies rather than the original objects from the profile.
     *
     * @param model The model into which to merge the values defined by the profile, must not be <code>null</code>.

    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":
        return {"model_name": model_name, "message": "LeCNN all the images"}

<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">🔢 (⚖️ 🔢) 💪 🐍</a> ↩️ ⏬ 3️⃣.4️⃣.

///

/// tip

🚥 👆 💭, "📊", "🎓", &amp; "🍏" 📛 🎰 🏫 <abbr title="Technically, Deep Learning model architectures">🏷</abbr>.

///

### 📣 *➡ 🔢*

⤴️ ✍ *➡ 🔢* ⏮️ 🆎 ✍ ⚙️ 🔢 🎓 👆 ✍ (`ModelName`):

```Python hl_lines="16"
{!../../docs_src/path_params/tutorial005.py!}
```

		Recursive: true,                  // recursively heal all objects at 'prefix'
		Remove:    true,                  // remove content that has lost quorum and not recoverable
		ScanMode:  madmin.HealNormalScan, // by default do not do 'deep' scanning
	}

	start, _, err := madmClnt.Heal(context.Background(), "healing-rewrite-bucket", "", opts, "", false, false)
	if err != nil {
		log.Fatalln(err)
	}
	fmt.Println("Healstart sequence ===")

@Deprecated(since = "4.0.0")
public interface InheritanceAssembler {

    /**
     * Merges values from the specified parent model into the given child model. Implementations are expected to keep
     * parent and child completely decoupled by injecting deep copies of objects into the child rather than the original
     * objects from the parent.
     *

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"}

///

/// 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`):

///

/// tip | Consejo

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

///

### Declara un *parámetro de path*

Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`):

        }
      }
    }
  ]
}
```

### v1.3+ versions

Version 1.3 introduces changes to help with [faster metadata reads and updates](https://blog.min.io/minio-versioning-metadata-deep-dive/)

| Entry           | Encoding                    | Content
| ----------------|-----------------------------|----------------------------------------

# Sub-dependencies

You can create dependencies that have **sub-dependencies**.

They can be as **deep** as you need them to be.

**FastAPI** will take care of solving them.

## First dependency "dependable"

You could create a first dependency ("dependable") like:

//// tab | Python 3.10+

```Python hl_lines="8-9"
{!> ../../docs_src/dependencies/tutorial005_an_py310.py!}
```

////

//// tab | Python 3.9+