///

/// 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`):

```Python hl_lines="16"

pkg log/syslog (freebsd-386-cgo), method (*Writer) Info(string) error
pkg log/syslog (freebsd-386-cgo), method (*Writer) Notice(string) error
pkg log/syslog (freebsd-386-cgo), method (*Writer) Warning(string) error
pkg log/syslog (freebsd-386-cgo), method (*Writer) Write([]uint8) (int, error)
pkg log/syslog (freebsd-386-cgo), type Priority int
pkg log/syslog (freebsd-386-cgo), type Writer struct

///

/// tip | "Dica"



///

	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

Logo, crie um *parâmetro de rota* com anotações de tipo usando a classe enum que você criou (`ModelName`):

```Python hl_lines="16"

Ceci vous permettra d'obtenir des fonctionnalités de l'éditeur dans votre fonction, telles
que des vérifications d'erreur, de l'auto-complétion, etc.

///

## <abbr title="aussi appelé sérialisation, ou parfois parsing ou marshalling en anglais">Conversion</abbr> de données

    * !!! [1215 15:42:56] Timed out waiting for apiserver:  to answer at https://localhost:6443/version; tried 10 waiting 1 between each

   The `read_vs_write_current_requests` metric histogram is adjusted, in the case of using API Priority and Fairness instead of max-in-flight, to divide by the relevant limit: sum of queue capacities for waiting requests, sum of seat limits for executing requests. ([#110164](https://github.com/kubernetes/k...

{!../../docs_src/path_params/tutorial002.py!}
```

👉 💼, `item_id` 📣 `int`.

/// check

👉 🔜 🤝 👆 👨‍🎨 🐕‍🦺 🔘 👆 🔢, ⏮️ ❌ ✅, 🛠️, ♒️.

///

## 💽 <abbr title="also known as: serialization, parsing, marshalling">🛠️</abbr>

🚥 👆 🏃 👉 🖼 &amp; 📂 👆 🖥 <a href="http://127.0.0.1:8000/items/3" class="external-link" target="_blank">http://127.0.0.1:8000/items/3</a>, 👆 🔜 👀 📨:

```JSON
{"item_id":3}
```

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

as he could go.

  Alice took up the fan and gloves, and, as the hall was very
hot, she kept fanning herself all the time she went on talking:
`Dear, dear!  How queer everything is to-day!  And yesterday
things went on just as usual.  I wonder if I've been changed in
the night?  Let me think:  was I the same when I got up this
morning?  I almost think I can remember feeling a little