Después de activar el entorno virtual, puedes ejecutar tu programa, y usará el Python dentro de tu entorno virtual con los paquetes que instalaste allí.

<div class="termy">

```console
$ python main.py

Hello World
```

</div>

## Configura Tu Editor

Depois de ativar o ambiente virtual, você pode executar seu programa, e ele usará o Python dentro do seu ambiente virtual com os pacotes que você instalou lá.

<div class="termy">

```console
$ python main.py

Hello World
```

</div>

## Configure seu editor

    }

    /*
     * We need the top SIGNIFICAND_BITS + 1 bits, including the "implicit" one bit. To make rounding
     * easier, we pick out the top SIGNIFICAND_BITS + 2 bits, so we have one to help us round up or
     * down. twiceSignifFloor will contain the top SIGNIFICAND_BITS + 2 bits, and signifFloor the
     * top SIGNIFICAND_BITS + 1.
     *

+ [Apache Maven JIRA project page](https://issues.apache.org/jira/projects/MNG/)
+ [Contributor License Agreement][cla]
+ [General GitHub documentation](https://help.github.com/)
+ [GitHub pull request documentation](https://docs.github.com/en/github/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork)

### Créalo

* Crea un archivo `main.py` con:

```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

<details markdown="1">

### Crie

* Crie um arquivo `main.py` com:

```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

<details markdown="1">

{* ../../docs_src/security/tutorial002_an_py310.py hl[31] *}

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

/// tip

You might remember that request bodies are also declared with Pydantic models.

	dataUsageBloomName     = ".bloomcycle.bin"
	dataUsageBloomNamePath = bucketMetaPrefix + SlashSeparator + dataUsageBloomName

	backgroundHealInfoPath = bucketMetaPrefix + SlashSeparator + ".background-heal.json"

	dataUsageCacheName = ".usage-cache.bin"
)

// storeDataUsageInBackend will store all objects sent on the dui channel until closed.

```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

<details markdown="1">

{* ../../docs_src/custom_response/tutorial009c.py hl[9:14,17] *}

Ahora en lugar de devolver:

```json
{"message": "Hello World"}
```

...este response devolverá:

```json
{
  "message": "Hello World"
}
```

Por supuesto, probablemente encontrarás formas mucho mejores de aprovechar esto que formatear JSON. 😉

## Clase de response predeterminada