// Verify that the central time zone is returned
        assertNotNull(result);
        assertEquals(FessUserTimeZoneProcessProvider.centralTimeZone, result);
        assertEquals(TimeZone.getDefault(), result);
    }

    // Test getRequestedTimeZone() with different default time zone
    public void test_getRequestedTimeZone_withDifferentDefaultTimeZone() {
        // Store original time zone

    void testInitDefaultsSetsAllFields() throws CIFSException {
        BaseConfiguration testConfig = new BaseConfiguration(false);
        testConfig.initDefaults();

        // Check random and time zone
        assertNotNull(testConfig.getRandom());
        assertNotNull(testConfig.getLocalTimezone());

        // Check PID is set
        assertTrue(testConfig.getPid() >= 0);

	t.HealedBuckets = append(make([]string, 0, len(tracker.HealedBuckets)), tracker.HealedBuckets...)
	ahs.healStatus[tracker.ID] = t
	tracker.mu.RUnlock()
}

// Sort by zone, set and disk index
func sortDisks(disks []madmin.Disk) {
	sort.Slice(disks, func(i, j int) bool {
		a, b := &disks[i], &disks[j]
		if a.PoolIndex != b.PoolIndex {
			return a.PoolIndex < b.PoolIndex
		}

In this scenario, each one of the cleaners (including you) would be a processor, doing their part of the job.

Mais dans ce cas, si pouviez amener 8 ex-serveurs/cuisiniers/devenus-nettoyeurs 👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳👨‍🍳, et que chacun d'eux (plus vous) pouvait prendre une zone de la maison pour la nettoyer, vous pourriez faire tout le travail en parallèle, et finir plus tôt.

Dans ce scénario, chacun des nettoyeurs (vous y compris) serait un processeur, faisant sa partie du travail.

You would have **one single `engine` object** for all your code to connect to the same database.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[14:18] hl[14:15,17:18] *}

Using `check_same_thread=False` allows FastAPI to use the same SQLite database in different threads. This is necessary as **one single request** could use **more than one thread** (for example in dependencies).

        of built-in training loops such as `tf.keras` `compile` and `fit`, we
        expect reduction value to be `SUM` or `NONE`. Using `AUTO` in that case
        will raise an error.
    *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in

We had this type annotation:

//// tab | Python 3.10+

```Python
q: str | None = None
```

////

//// tab | Python 3.8+

```Python
q: Union[str, None] = None
```

////

What we will do is wrap that with `Annotated`, so it becomes:

//// tab | Python 3.10+

```Python
q: Annotated[str | None] = None
```

////

//// tab | Python 3.8+

```Python

```

/// tip

The second argument to <a href="https://docs.python.org/3.8/library/os.html#os.getenv" class="external-link" target="_blank">`os.getenv()`</a> is the default value to return.

If not provided, it's `None` by default, here we provide `"World"` as the default value to use.

///

Then you could call that Python program:

//// tab | Linux, macOS, Windows Bash

<div class="termy">

```console

					if kv.Match(BatchJobKV{Key: k, Value: v}) {
						return true
					}
				}
			}

			// None of the provided metadata filters match skip the object.
			return false
		}

		// if one of source or target is non MinIO, just replicate the top most version like `mc mirror`