Se estivesse em uma anotação de tipo, poderíamos ter usado a barra vertical, como:

```Python
some_variable: PlaneItem | CarItem
```

Mas se colocarmos isso em `response_model=PlaneItem | CarItem` teríamos um erro, pois o Python tentaria executar uma **operação inválida** entre `PlaneItem` e `CarItem` em vez de interpretar isso como uma anotação de tipo.

## Lista de modelos

                }
            }).start();
        }

        latch.await();

        assertEquals("All threads should succeed", threadCount, successCount.get());
        assertEquals("No threads should have errors", 0, errorCount.get());
    }

    public void test_writeThreadDump_withSpecialCharacters() throws IOException {
        // Test with file path containing special characters

        assertDoesNotThrow(() -> filter.init(filterConfig));
    }

    @Test
    void testDestroy() {
        // Test that destroy method executes without errors
        assertDoesNotThrow(() -> filter.destroy());
    }

    @Test
    void testDoFilter_noAuthorizationHeader_shouldChallengeClient() throws Exception {
        // Initialize filter first

    }

    @BeforeEach
    public void setUp() throws UnknownHostException {
        ntlmSsp = new NtlmSsp();

        // Use lenient stubbing to avoid UnnecessaryStubbing errors for tests that don't need all mocks
        lenient().when(mockCifsContext.getConfig()).thenReturn(mockConfig);
        lenient().when(mockConfig.getDefaultDomain()).thenReturn("DOMAIN");

## Crie um Pull Request

É possível [contribuir](contributing.md){.internal-link target=_blank} no código fonte fazendo Pull Requests, por exemplo:

* Para corrigir um erro de digitação que você encontrou na documentação.

        assertNotNull(result);

        // Note: Actual log output verification would require a log appender mock
        // but we can at least ensure the method executes without errors
        logger.info("Execute method called with query: {} and boost: {}", query, boost);
    }

    public void test_execute_withHighBoost() {
        // Test execute with high boost value

    * If you know Python types you know how to use Pydantic.
* Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**:
    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.

 * dynamicProperties.store();
 * </pre>
 *
 * <p>Exceptions:
 * <ul>
 *   <li>{@link FileAccessException} - Thrown if there are issues accessing the file.</li>
 *   <li>{@link IORuntimeException} - Thrown if there are I/O errors during file operations.</li>
 * </ul>
 *
 * <p>Thread Safety:
 * This class uses synchronization to ensure thread-safe access to the properties.
 *
 * @see Properties
 */

#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/base64.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/util/debug_data_dumper.h"

using tensorflow::errors::InvalidArgument;

namespace tensorflow {
namespace {

absl::Status ValidateNonRefOutput(const Node* node, int idx) {
  const DataType& dt = node->output_type(idx);
  return IsRefType(dt)

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including but not limited to the risks and costs of program errors,
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