## Documenting the callback { #documenting-the-callback }

The actual callback code will depend heavily on your own API app.

And it will probably vary a lot from one app to the next.

It could be just one or two lines of code, like:

```Python
callback_url = "https://example.com/api/v1/invoices/events/"

    for (int i = 0; i < map.segments.length; i++) {
      assertThat(map.segments[i].table.length()).isEqualTo(segmentSize);
    }
  }

  public void testSetMaximumSize() {
    // vary maximumSize wrt concurrencyLevel

    for (int maxSize = 1; maxSize < 100; maxSize++) {
      checkMaximumSize(1, 8, maxSize);
      checkMaximumSize(2, 8, maxSize);
      checkMaximumSize(4, 8, maxSize);

    for (int i = 0; i < map.segments.length; i++) {
      assertThat(map.segments[i].table.length()).isEqualTo(segmentSize);
    }
  }

  public void testSetMaximumSize() {
    // vary maximumSize wrt concurrencyLevel

    for (int maxSize = 1; maxSize < 100; maxSize++) {
      checkMaximumSize(1, 8, maxSize);
      checkMaximumSize(2, 8, maxSize);
      checkMaximumSize(4, 8, maxSize);

</p>

<pre class="ebnf">
VarDecl = "var" ( VarSpec | "(" { VarSpec ";" } ")" ) .
VarSpec = IdentifierList ( Type [ "=" ExpressionList ] | "=" ExpressionList ) .
</pre>

<pre>
var i int
var U, V, W float64
var k = 0
var x, y float32 = -1, -2
var (
	i       int
	u, v, s = 2.0, 3.0, "bar"
)
var re, im = complexSqrt(-1)

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### Create the `Settings` object { #create-the-settings-object }

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

    response = client.put(
        "/items/5",
        json={"description": "A very nice Item"},
    )
    assert response.status_code == 422, response.text
    assert response.json() == {
        "detail": [
            {
                "loc": ["body", "name"],
                "input": {"description": "A very nice Item"},
                "msg": "Field required",
                "type": "missing",

        map sigbuild-r2.12-python3.9 2.12-python3.9
        map sigbuild-r2.12-python3.10 2.12-python3.10
        map sigbuild-r2.12-python3.11 2.12-python3.11
        # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different)
        map sigbuild-r2.12-clang 2.12-python3.9
        map sigbuild-r2.12-clang-python3.8 2.12-python3.8
        map sigbuild-r2.12-clang-python3.9 2.12-python3.9

Una variable de entorno (también conocida como "**env var**") es una variable que vive **fuera** del código de Python, en el **sistema operativo**, y podría ser leída por tu código de Python (o por otros programas también).

Las variables de entorno pueden ser útiles para manejar **configuraciones** de aplicaciones, como parte de la **instalación** de Python, etc.

## Crear y Usar Variables de Entorno { #create-and-use-env-vars }

    c = TestClient(mod.app)
    return c


def test_foo_needy_very(client: TestClient):
    response = client.get("/items/foo?needy=very")
    assert response.status_code == 200
    assert response.json() == {
        "item_id": "foo",
        "needy": "very",
        "skip": 0,
        "limit": None,
    }


def test_foo_no_needy(client: TestClient):
    response = client.get("/items/foo?skip=a&limit=b")

    return client


def test_post_body_example(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


def test_openapi_schema(client: TestClient) -> None: