## Execução de dependências com `yield`

A sequência de execução é mais ou menos como esse diagrama. O tempo passa do topo para baixo. E cada coluna é uma das partes interagindo ou executando código.

```mermaid
sequenceDiagram

participant client as Cliente
participant handler as Manipulador de exceções

      throws InterruptedException {
    Preconditions.checkNotNull(buffer);
    /*
     * This code performs one System.nanoTime() more than necessary, and in return, the time to
     * execute Queue#drainTo is not added *on top* of waiting for the timeout (which could make
     * the timeout arbitrarily inaccurate, given a queue that is slow to drain).
     */
    long deadline = System.nanoTime() + unit.toNanos(timeout);
    int added = 0;

'.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-toggle=\"modal\"]'\nconst SELECTOR_DATA_DISMISS = '[data-dismiss=\"modal\"]'\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  focus: true,\n  show: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  focus: 'boolean',\n  show: ...

* go to the parent package (the directory `app/`)...
* then go to the parent of that package (there's no parent package, `app` is the top level 😱)...
* and in there, find the module `dependencies` (the file at `app/dependencies.py`)...
* and from it, import the function `get_token_header`.

* the path `/`
* using a <abbr title="an HTTP GET method"><code>get</code> operation</abbr>

/// info | `@decorator` Info

That `@something` syntax in Python is called a "decorator".

You put it on top of a function. Like a pretty decorative hat (I guess that's where the term came from).

A "decorator" takes the function below and does something with it.

## Execution of dependencies with `yield` { #execution-of-dependencies-with-yield }

The sequence of execution is more or less like this diagram. Time flows from top to bottom. And each column is one of the parts interacting or executing code.

```mermaid
sequenceDiagram

participant client as Client
participant handler as Exception handler
participant dep as Dep with yield

```Python
def get_settings():
    return Settings()
```

we would create that object for each request, and we would be reading the `.env` file for each request. ⚠️

But as we are using the `@lru_cache` decorator on top, the `Settings` object will be created only once, the first time it's called. ✔️

{* ../../docs_src/settings/app03_an_py39/main.py hl[1,11] *}

  // array. The top bits of this value are the remaining bits of the hash value that were not used
  // in the short hash. We saw that a mask of 0x7f would keep the 7-bit value 0x6f from a full
  // hashcode of 0x89abcdef. The imaginary `hash` value would then be the remaining top 25 bits,
  // 0x89abcd80. To this is added (or'd) the `next` value, which is an index within `entries`

applications with <a href="https://anyio.readthedocs.io/en/stable/" class="external-link" target="_blank">AnyIO</a> to be highly compatible and get its benefits (e.g. *structured concurrency*).

I created another library on top of AnyIO, as a thin layer on top, to improve a bit the type annotations and get better **autocompletion**, **inline errors**, etc. It also has a friendly introduction and tutorial to help you **understand** and write **your own async code**: <a href="https://asyncer.tiangolo.com/"...

      throws InterruptedException {
    Preconditions.checkNotNull(buffer);
    /*
     * This code performs one System.nanoTime() more than necessary, and in return, the time to
     * execute Queue#drainTo is not added *on top* of waiting for the timeout (which could make
     * the timeout arbitrarily inaccurate, given a queue that is slow to drain).
     */
    long deadline = System.nanoTime() + unit.toNanos(timeout);
    int added = 0;