But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.

# Segurança - Primeiros Passos

Vamos imaginar que você tem a sua API **backend** em algum domínio.

E você tem um **frontend** em outro domínio ou em um path diferente no mesmo domínio (ou em uma aplicação mobile).

E você quer uma maneira de o frontend autenticar o backend, usando um **username** e **senha**.

Nós podemos usar o **OAuth2** junto com o **FastAPI**.

## Conect-se com o autor

Você pode se conectar <a href="https://tiangolo.com" class="external-link" target="_blank">comigo (Sebastián Ramírez / `tiangolo`)</a>, o autor.

Você pode:

* <a href="https://github.com/tiangolo" class="external-link" target="_blank">Me siga no **GitHub**</a>.

3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function.

    So, it is a generator function that transfers the "generating" work to something else internally.

    By doing it this way, we can put it in a `with` block, and that way, ensure that it is closed after finishing.

!!! tip

		"-j", "CONNMARK",
		"--set-xmark", inpodTproxyMark)

	// Handle healthcheck probes from the host node. In the host netns, before the packet enters the pod, we SNAT
	// the healthcheck packet to a fixed IP if the packet is coming from a node-local process with a socket.
	//
	// We do this so we can exempt this traffic from ztunnel capture/proxy - otherwise both kube-proxy (legit)

## Pydantic `Settings`

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

### Install `pydantic-settings`

* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc.
* **Convert data**: from the request to the required type.
* **Validate data**: coming from each request:
    * Generating **automatic errors** returned to the client when the data is invalid.
* **Document** the API using OpenAPI:
    * which is then used by the automatic interactive documentation user interfaces.

                               "' has a reference type ", DataTypeString(dt))
             : absl::OkStatus();
}

// Converts `ninputs` and `inputs` into `inputs_tensors` and `input_nodes` and
// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(

Flask is a "microframework", it doesn't include database integrations nor many of the things that come by default in Django.

This simplicity and flexibility allow doing things like using NoSQL databases as the main data storage system.

As it is very simple, it's relatively intuitive to learn, although the documentation gets somewhat technical at some points.

    * Automatic and clear errors when the data is invalid.
    * Validation even for deeply nested JSON objects.
* <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from:
    * JSON.
    * Path parameters.
    * Query parameters.
    * Cookies.
    * Headers.
    * Forms.
    * Files.