# Sub-dependencies { #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" { #first-dependency-dependable }

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

{* ../../docs_src/dependencies/tutorial005_an_py310.py hl[8:9] *}

        }
      }
    }
  ]
}
```

### 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
| ----------------|-----------------------------|----------------------------------------

        assertFalse(a.equals("not a blob"), "equals(other type) should be false");
    }

    // Ensures clone() returns a deep copy; mutations are independent across instances
    @Test
    @DisplayName("clone: returns deep copy and independent state")
    void clone_returnsDeepCopy() {
        // Arrange
        byte[] data = new byte[] { 10, 20, 30 };

{* ../../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 }

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

/// tip | Dica
Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de <abbr title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</abbr> de Aprendizado de Máquina.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

///

/// tip | Порада

Якщо вам цікаво, "AlexNet", "ResNet" та "LeNet" — це просто назви ML моделей <abbr title="Технічно, архітектури Deep Learning моделей">Machine Learning</abbr>.

///


### Оголосіть *параметр шляху*

Потім створіть *параметр шляху* з анотацією типу, використовуючи створений вами клас enum (`ModelName`):

designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate...

    assertThat(request.requestLine).isEqualTo(
      "GET /a/deep/path?key=foo%20bar HTTP/1.1",
    )
    val requestUrl = request.url
    assertThat(requestUrl.scheme).isEqualTo("http")
    assertThat(requestUrl.host).isEqualTo(server.hostName)
    assertThat(requestUrl.port).isEqualTo(server.port)
    assertThat(requestUrl.encodedPath).isEqualTo("/a/deep/path")
    assertThat(requestUrl.queryParameter("key")).isEqualTo("foo bar")

* <abbr title="Um método de aprendizado de máquina que usa redes neurais artificiais com numerosas camadas ocultas entre as camadas de entrada e saída, desenvolvendo assim uma estrutura interna abrangente">Deep Learning</abbr>

### O abbr fornece uma frase completa e uma explicação { #the-abbr-gives-a-full-phrase-and-an-explanation }