const tensorflow::Scope& scope,
          const std::vector<tensorflow::Output>& inputs,
          std::vector<tensorflow::Output>* outputs) {
        DCHECK_EQ(scope.graph(), &parent->graph);
        TF_RETURN_IF_ERROR(
            CopyGraph(&params->body_graph->graph, &parent->graph,
                      &parent->refiner, params->body_inputs, inputs,
                      scope.impl()->name(), scope.impl()->control_deps(),

import static com.google.common.truth.Truth.assertThat;
import static java.util.Arrays.asList;
import static java.util.Collections.emptyList;
import static java.util.Collections.emptySet;
import static java.util.Collections.nCopies;
import static java.util.Collections.singleton;
import static java.util.Collections.singletonList;

import com.google.common.annotations.GwtCompatible;
import com.google.common.annotations.GwtIncompatible;

    Spliterator<Entry<E>> entrySpliterator = multiset.entrySet().spliterator();
    return CollectSpliterators.flatMap(
        entrySpliterator,
        (Entry<E> entry) -> Collections.nCopies(entry.getCount(), entry.getElement()).spliterator(),
        Spliterator.SIZED
            | (entrySpliterator.characteristics()
                & (Spliterator.ORDERED | Spliterator.NONNULL | Spliterator.IMMUTABLE)),

   *     copied
   */
  @GwtIncompatible // Array.newArray(Class, int)
  public final E[] toArray(Class<@NonNull E> type) {
    return Iterables.<E>toArray(getDelegate(), type);
  }

  /**
   * Copies all the elements from this fluent iterable to {@code collection}. This is equivalent to
   * calling {@code Iterables.addAll(collection, this)}.
   *

	// It must match the data used during encryption
	// or data key generation.
	AssociatedData Context
}

// MACRequest is a structure containing fields
// and options for generating message authentication
// codes (MAC).
type MACRequest struct {
	// Name is the name of the master key used decrypt
	// the ciphertext.
	Name string

	Version int

	Message []byte
}

            // Then: Should create sam instance
            assertNotNull(message.sam);
            assertEquals(10, message.resume_handle);
            assertEquals(0, message.num_entries); // num_entries comes from the 5th dec_ndr_long call
            assertEquals(0, message.retval);
        }

        @Test
        @DisplayName("Should handle zero sam pointer correctly")

<div class="termy">

```console
$ pip install pydantic-settings
---> 100%
```

</div>

It also comes included when you install the `all` extras with:

<div class="termy">

```console
$ pip install "fastapi[all]"
---> 100%
```

</div>

### Create the `Settings` object { #create-the-settings-object }

            <option value="$PROJECT_DIR$/platforms/core-execution/request-handler-worker" />
            <option value="$PROJECT_DIR$/platforms/core-execution/scoped-persistent-cache" />
            <option value="$PROJECT_DIR$/platforms/core-execution/snapshots" />
            <option value="$PROJECT_DIR$/platforms/core-execution/worker-main" />

 * <p>In order to allow proper garbage collection of unused locks, the edges of the lock graph are
 * weak references.
 *
 * <p><strong>Performance</strong>
 *
 * <p>The extra bookkeeping done by cycle detecting locks comes at some cost to performance.
 * Benchmarks (as of December 2011) show that:
 *
 * <ul>
 *   <li>for an unnested {@code lock()} and {@code unlock()}, a cycle detecting lock takes 38ns as

Por exemplo:

* **Processamento de áudio** ou **imagem**
* **Visão Computacional**: uma imagem é composta por milhões de pixels, cada pixel tem 3 valores / cores, processar isso normalmente exige alguma computação em todos esses pixels ao mesmo tempo