// Check if the given error corresponds to the ENAMETOOLONG (name too long).
func isSysErrTooLong(err error) bool {
	return errors.Is(err, syscall.ENAMETOOLONG)
}

// Check if the given error corresponds to the ELOOP (too many symlinks).
func isSysErrTooManySymlinks(err error) bool {
	return errors.Is(err, syscall.ELOOP)
}

// Check if the given error corresponds to ENOTEMPTY for unix,
// EEXIST for solaris variants,

		}
		for _, fi := range fis {
			if fi.Mode()&os.ModeSymlink == os.ModeSymlink {
				fi, err = Stat(pathJoin(dirPath, fi.Name()))
				if err != nil {
					// It got deleted in the meantime, not found
					// or returns too many symlinks ignore this
					// file/directory.
					if osIsNotExist(err) || isSysErrPathNotFound(err) ||
						isSysErrTooManySymlinks(err) {
						continue
					}
					return err
				}

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

 *
 * <ul>
 *   <li>An annotation named just "Suppress" might someday be treated by a non-Android tool as a
 *       suppression. This would follow the precedent of many of our annotation processors, which
 *       look for any annotation named, e.g., "GwtIncompatible," regardless of package.
 *   <li>An annotation named just "Suppress" might suggest to users that the test is suppressed

   * non-nullable {@code cause}, as many users expect to find one.
   */
  public UncheckedExecutionException(@CheckForNull String message, @CheckForNull Throwable cause) {
    super(message, cause);
  }

  /**
   * Creates a new instance with {@code null} as its detail message and the given cause. Prefer to
   * provide a non-nullable {@code cause}, as many users expect to find one.
   */

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

You can import it directly from `fastapi`:

```python
from fastapi import Depends
```

::: fastapi.Depends

## `Security()`

For many scenarios, you can handle security (authorization, authentication, etc.) with dependencies, using `Depends()`.

But when you want to also declare OAuth2 scopes, you can use `Security()` instead of `Depends()`.

You can import `Security()` directly from `fastapi`:

```python

```

Dans cet exemple, nous n'avons déclaré aucun modèle Pydantic. En fait, le corps de la requête n'est même pas <abbr title="converti d'un format simple, comme des octets, en objets Python">parsé</abbr> en tant que JSON, il est lu directement en tant que `bytes`, et la fonction `magic_data_reader()` serait chargé de l'analyser d'une manière ou d'une autre.

Néanmoins, nous pouvons déclarer le schéma attendu pour le corps de la requête.

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like: