But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.
  // * We want to behave reasonably for timeouts of 0
  // * We are more responsive to completion than timeouts. This is because parkNanos depends on

      }
    }
    return utf8Length;
  }

  /**
   * Returns {@code true} if {@code bytes} is a <i>well-formed</i> UTF-8 byte sequence according to
   * Unicode 6.0. Note that this is a stronger criterion than simply whether the bytes can be
   * decoded. For example, some versions of the JDK decoder will accept "non-shortest form" byte
   * sequences, but encoding never reproduces these. Such byte sequences are <i>not</i> considered

    //    second thread can then dispatch to the subscriber it took before the first thread does.
    //
    // All this makes me really wonder if there's any value in queueing here at all. A dispatcher
    // that simply loops through the subscribers and dispatches the event to each would actually
    // probably provide a stronger order guarantee, though that order would obviously be different
    // in some cases.

### Autres formes de code asynchrone

L'utilisation d'`async` et `await` est relativement nouvelle dans ce langage.

Mais cela rend la programmation asynchrone bien plus simple.

Cette même syntaxe (ou presque) était aussi incluse dans les versions modernes de Javascript (dans les versions navigateur et NodeJS).

Mais avant ça, gérer du code asynchrone était bien plus complexe et difficile.

            return resource;
        }
    }

    @BeforeEach
    void setUp() {
        lenient().when(fileEntry.getName()).thenReturn("test");
    }

    @Test
    @DisplayName("Iterator without filter - simple case")
    void iteratorWithoutFilter() {
        // Setup: constructor calls advance() once
        when(delegate.hasNext()).thenReturn(true, false);
        when(delegate.next()).thenReturn(fileEntry);

import org.mockito.InOrder;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;

@ExtendWith(MockitoExtension.class)
class FileEntryTest {

    @Mock
    FileEntry mockEntry;

    // Simple fake implementation to exercise the interface without mocks
    private static final class TestFileEntry implements FileEntry {
        private final String name;
        private final int type;

### Use o sub-modelo como um tipo

E então podemos usa-lo como o tipo de um atributo:

{* ../../docs_src/body_nested_models/tutorial004.py hl[20] *}

Isso significa que o **FastAPI** vai esperar um corpo similar à:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": ["rock", "metal", "bar"],
    "image": {
        "url": "http://example.com/baz.jpg",

            throws Exception {
        File localRepoDir = new File(projectBuildingRequest.getLocalRepository().getBasedir());
        LocalRepository localRepo = new LocalRepository(localRepoDir, "simple");

        RepositorySystemSession session = new MavenSessionBuilderSupplier(repositorySystem)
                .get()
                .withLocalRepositories(localRepo)
                .build();

            throw new IOException("Connection not established");
        }

        try {
            // In real implementation, this would perform RDMA write:
            // Similar to rdmaRead but with IBV_WR_RDMA_WRITE opcode

            log.debug("DiSNI RDMA write completed, {} bytes", length);

        } catch (Exception e) {
            state = RdmaConnectionState.ERROR;