```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`

	// Check if backend is writable and supports O_DIRECT
	uuid := mustGetUUID()
	filePath := pathJoin(s.drivePath, minioMetaTmpDeletedBucket, ".writable-check-"+uuid+".tmp")

	// Create top level directories if they don't exist.
	// with mode 0o777 mkdir honors system umask.
	mkdirAll(pathutil.Dir(filePath), 0o777, s.drivePath) // don't need to fail here

* 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.

it, and on both sides of it, and behind it, it occurred to her
that she might as well look and see what was on the top of it.

  She stretched herself up on tiptoe, and peeped over the edge of
the mushroom, and her eyes immediately met those of a large
caterpillar, that was sitting on the top with its arms folded,
quietly smoking a long hookah, and taking not the smallest notice
of her or of anything else.

      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;

* 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`.

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

      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;

		config.DriveSubSys:          drive.HelpDrive,
		config.BrowserSubSys:        browser.Help,
		config.ILMSubSys:            ilm.Help,
	}

	config.RegisterHelpSubSys(helpMap)

	// save top-level help for deprecated sub-systems in a separate map.
	deprecatedHelpKVMap := map[string]config.HelpKV{
		config.RegionSubSys: {
			Key:         config.RegionSubSys,