* o.equals(e)}.
   *
   * @param o object to be checked for containment in this queue
   * @return {@code true} if this queue contains the specified element
   */
  @CanIgnoreReturnValue // pushed down from class to method
  @Override
  public boolean contains(@Nullable Object o) {
    final Monitor monitor = this.monitor;
    monitor.enter();
    try {

import java.util.Map;
import java.util.Map.Entry;
import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * Simple helper class to build a "sparse" array of objects based on the indexes that were added to
 * it. The array will be from 0 to the maximum index given. All non-set indexes will contain null
 * (so it's not really a sparse array, just a pseudo sparse array). The builder can also return a

    TypeVariableKey(TypeVariable<?> var) {
      this.var = checkNotNull(var);
    }

    @Override
    public int hashCode() {
      return Objects.hashCode(var.getGenericDeclaration(), var.getName());
    }

    @Override
    public boolean equals(@CheckForNull Object obj) {
      if (obj instanceof TypeVariableKey) {
        TypeVariableKey that = (TypeVariableKey) obj;
        return equalsTypeVariable(that.var);

Por exemplo, o modelo acima declara um JSON "`object`" (ou `dict` no Python) como esse:

```JSON
{
    "name": "Foo",
    "description": "Uma descrição opcional",
    "price": 45.2,
    "tax": 3.5
}
```

...como `description` e `tax` são opcionais (Com um valor padrão de `None`), esse JSON "`object`" também é válido:

```JSON
{
    "name": "Foo",
    "price": 45.2
}
```

So, if we create a Pydantic object `user_in` like:

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

and then we call:

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

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

	globalNetPerfRX              netPerfRX
	globalSiteNetPerfRX          netPerfRX
	globalObjectPerfBucket       = "minio-perf-test-tmp-bucket"
	globalObjectPerfUserMetadata = "X-Amz-Meta-Minio-Object-Perf" // Clients can set this to bypass S3 API service freeze. Used by object pref tests.

	// MinIO version unix timestamp
	globalVersionUnix uint64

	// MinIO client
	globalMinioClient *minio.Client

import static com.google.common.collect.CollectPreconditions.checkNonnegative;
import static com.google.common.collect.CollectPreconditions.checkRemove;
import static com.google.common.collect.Maps.safeGet;
import static java.util.Objects.requireNonNull;

import com.google.common.annotations.GwtCompatible;
import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import com.google.common.primitives.Ints;

   * This method behaves equivalently to {@linkplain #zip zipping} the stream elements into
   * temporary pair objects and then using {@link Stream#forEach} on that stream.
   *
   * @since 22.0 (but only since 33.4.0 in the Android flavor)
   */
  @Beta
  public static <A extends @Nullable Object, B extends @Nullable Object> void forEachPair(
      Stream<A> streamA, Stream<B> streamB, BiConsumer<? super A, ? super B> consumer) {

		},
		{
			testName:          "fail to encode secret",
			objs:              []runtime.Object{kubeSystemNamespace, sa, saSecret},
			outputWriterError: errors.New("injected encode error"),
			wantErrStr:        "injected encode error",
		},
		{
			testName: "success",
			objs:     []runtime.Object{kubeSystemNamespace, sa, saSecret},
			name:     "cluster-foo",
			want:     "cal-want",
		},

      dummy += Objects.hashCode(S0, S1, S2, S3);
    }
    return dummy;
  }

  @Benchmark
  int hashString_5(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1, S2, S3, S4);
    }
    return dummy;
  }

  @Benchmark
  int hashMixed_5(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(I2, S1, D1, S2, I0);