private final ImmutableSet<TestOption> options;
  private boolean closed;

  public TestOutputStream(OutputStream out, TestOption... options) throws IOException {
    this(out, Arrays.asList(options));
  }

  public TestOutputStream(OutputStream out, Iterable<TestOption> options) throws IOException {
    super(checkNotNull(out));
    this.options = ImmutableSet.copyOf(options);
    throwIf(OPEN_THROWS);

Pydantic-Modelle haben eine `.dict()`-Methode, die ein `dict` mit den Daten des Modells zurückgibt.

Wenn wir also ein Pydantic-Objekt `user_in` erstellen, etwa so:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

und wir rufen seine `.dict()`-Methode auf:

```Python
user_dict = user_in.dict()
```

  public static Collection<Object[]> parameters() {
    ElementOrder<?> naturalElementOrder = ElementOrder.sorted(Ordering.natural());

    return Arrays.asList(
        new Object[][] {
          {false, false, ElementOrder.insertion(), ElementOrder.insertion()},
          {true, false, ElementOrder.insertion(), ElementOrder.insertion()},

#### Pydantic-модель из содержимого другой модели

Как в примере выше мы получили `user_dict` из `user_in.dict()`, этот код:

```Python
user_dict = user_in.dict()
UserInDB(**user_dict)
```

будет равнозначен такому:

```Python
UserInDB(**user_in.dict())
```

...потому что `user_in.dict()` - это `dict`, и затем мы указываем, чтобы Python его "распаковал", когда передаём его в `UserInDB` и ставим перед ним `**`.

Como no exemplo acima, obtivemos o `user_dict` a partir do `user_in.dict()`, este código:

```Python
user_dict = user_in.dict()
UserInDB(**user_dict)
```

seria equivalente a:

```Python
UserInDB(**user_in.dict())
```

...porque `user_in.dict()` é um `dict`, e depois fazemos o Python "desembrulhá-lo" passando-o para UserInDB precedido por `**`.

#### 用其它模型中的内容生成 Pydantic 模型

上例中 ，从 `user_in.dict()` 中得到了 `user_dict`，下面的代码：

```Python
user_dict = user_in.dict()
UserInDB(**user_dict)
```

等效于：

```Python
UserInDB(**user_in.dict())
```

……因为 `user_in.dict()` 是字典，在传递给 `UserInDB` 时，把 `**` 加在  `user_in.dict()` 前，可以让 Python 进行**解包**。

这样，就可以用其它 Pydantic 模型中的数据生成 Pydantic 模型。

#### 解包 `dict` 和更多关键字

import static com.google.common.collect.testing.features.CollectionFeature.SERIALIZABLE;
import static com.google.common.collect.testing.features.CollectionFeature.SERIALIZABLE_INCLUDING_VIEWS;
import static java.util.Arrays.asList;
import static java.util.Collections.emptySet;
import static java.util.Collections.sort;

import com.google.common.annotations.GwtIncompatible;
import com.google.common.collect.BoundType;

import static com.google.common.collect.testing.Helpers.getMethod;
import static com.google.common.collect.testing.features.FeatureUtil.addImpliedFeatures;
import static java.util.Arrays.asList;
import static java.util.Collections.disjoint;
import static java.util.Collections.unmodifiableSet;
import static java.util.logging.Level.FINER;

import com.google.common.annotations.GwtIncompatible;

  }

  @Override
  Cell<R, C, V> getCell(int index) {
    int rowIndex = cellRowIndices[index];
    int columnIndex = cellColumnIndices[index];
    R rowKey = rowKeySet().asList().get(rowIndex);
    C columnKey = columnKeySet().asList().get(columnIndex);
    // requireNonNull is safe because we use indexes that were populated by the constructor.
    V value = requireNonNull(values[rowIndex][columnIndex]);

/**
 */
public class ExclusionSetFilter implements ArtifactFilter {
    private Set<String> excludes;

    public ExclusionSetFilter(String[] excludes) {
        this.excludes = new LinkedHashSet<>(Arrays.asList(excludes));
    }

    public ExclusionSetFilter(Set<String> excludes) {
        this.excludes = excludes;
    }

    public boolean include(Artifact artifact) {
        String id = artifact.getArtifactId();