* cross origin: cross origin (do not translate to "origem cruzada")
* Cross-Origin Resource Sharing: Cross-Origin Resource Sharing (do not translate to "Compartilhamento de Recursos de Origem Cruzada")
* Deep Learning: Deep Learning (do not translate to "Aprendizado Profundo")
* dependable: dependable
* dependencies: dependências
* deprecated: descontinuado
* docs: documentação
* FastAPI app: aplicação FastAPI

- Mozilla Developer Network: Мережа Розробників Mozilla
- tutorial: навчальний посібник
- advanced user guide: просунутий посібник користувача
- deep learning: глибоке навчання
- machine learning: машинне навчання
- dependency injection: впровадження залежностей
- digest (HTTP): дайджест
- basic authentication (HTTP): базова автентифікація
- JSON schema: Схема JSON
- password flow: потік паролю

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

* 100% test coverage: cobertura de tests del 100%
* back and forth: de un lado a otro
* I/O (as in "input and output"): I/O (do not translate to "E/S")
* Machine Learning: Machine Learning (do not translate to "Aprendizaje Automático")
* Deep Learning: Deep Learning (do not translate to "Aprendizaje Profundo")
* callback hell: callback hell (do not translate to "infierno de callbacks")
* tip: Consejo (do not translate to "tip")

* production (meaning production software or environment): продакшн (do not change the ending, for example, translate `in production` as `в продакшн` (not `в продакшене`))
* completion (meaning code auto-completion): автозавершение
* editor (meaning component of IDE): редактор кода
* adopt (meaning start to use): использовать (or `начать использовать`)
* headers (meaning HTTP-headers): HTTP-заголовки

## Use Case { #use-case }

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",

## 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

{* ../../docs_src/path_params/tutorial005_py310.py hl[1,6:9] *}

/// tip

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

///

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

You can declare extra information in `Field`, `Query`, `Body`, etc. And it will be included in the generated JSON Schema.

You will learn more about adding extra information later in the docs, when learning to declare examples.

/// warning

Extra keys passed to `Field` will also be present in the resulting OpenAPI schema for your application.