assertEquivalent(stats.mean(), stream(array).average().getAsDouble());
      assertEquivalent(stats.sum(), stream(array).sum());
      assertEquivalent(stats.max(), stream(array).max().getAsDouble());
      assertEquivalent(stats.min(), stream(array).min().getAsDouble());
      // static method on Stats vs on instance method on DoubleStream
      assertEquivalent(Stats.meanOf(array), stream(array).average().getAsDouble());

	Rotational bool
	Metrics    DiskMetrics
	Error      string // carries the error over the network
}

// DiskMetrics has the information about XL Storage APIs
// the number of calls of each API and the moving average of
// the duration of each API.
type DiskMetrics struct {
	LastMinute              map[string]AccElem `json:"apiLatencies,omitempty"`
	APICalls                map[string]uint64  `json:"apiCalls,omitempty"`

    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;

import java.util.stream.DoubleStream;
import java.util.stream.IntStream;
import java.util.stream.LongStream;
import javax.annotation.CheckForNull;

/**
 * A bundle of statistical summary values -- sum, count, mean/average, min and max, and several
 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>

    bounds when loading a specially crafted `SavedModel`
    ([CVE-2020-26271](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-26271))
*   Solves an OOM issue on TPUs when XLA contexts use fused average updates
*   Updates `libjpeg-turbo` to `2.0.5` to handle
    [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
*   Updates `junit` to `4.13.1` to handle

]

[tool.coverage.run]
parallel = true
data_file = "coverage/.coverage"
source = [
    "docs_src",
    "tests",
    "fastapi"
]
context = '${CONTEXT}'
dynamic_context = "test_function"
omit = [
    "docs_src/response_model/tutorial003_04.py",
    "docs_src/response_model/tutorial003_04_py310.py",
]

[tool.coverage.report]
show_missing = true
sort = "-Cover"

-e .[all]
-r requirements-docs-tests.txt
pytest >=7.1.3,<9.0.0
coverage[toml] >= 6.5.0,< 8.0
mypy ==1.8.0
dirty-equals ==0.6.0
sqlmodel==0.0.22
flask >=1.1.2,<4.0.0
anyio[trio] >=3.2.1,<4.0.0
PyJWT==2.8.0
pyyaml >=5.3.1,<7.0.0
passlib[bcrypt] >=1.7.2,<2.0.0
inline-snapshot==0.13.0
# types
types-ujson ==5.7.0.1

  `sync_partial_proxy_rules_duration_seconds` (in addition to the existing\n`sync_proxy_rules_duration_seconds`),
  giving better information about the duration of each \nsync type, rather than
  only giving a weighted average of the two sync types together. ([#117787](https://github.com/kubernetes/kubernetes/pull/117787), [@danwinship](https://github.com/danwinship))
- `kubeadm`: added a new "kubeadm config validate" command that can be used to

import org.codehaus.plexus.component.configurator.expression.ExpressionEvaluator;
import org.codehaus.plexus.configuration.PlexusConfiguration;

/**
 * A component configurator which can leverage the {@link EnhancedConfigurationConverter}
 * and {@link EnhancedConverterLookup}.
 */
@Singleton
@Named("enhanced")
public class EnhancedComponentConfigurator extends BasicComponentConfigurator {

### Timing-Angriffe

Aber was ist ein „Timing-Angriff“?

Stellen wir uns vor, dass einige Angreifer versuchen, den Benutzernamen und das Passwort zu erraten.

Und sie senden eine Anfrage mit dem Benutzernamen `johndoe` und dem Passwort `love123`.

Dann würde der Python-Code in Ihrer Anwendung etwa so aussehen:

```Python
if "johndoe" == "stanleyjobson" and "love123" == "swordfish":
    ...
```