// Test that multiple converter instances work independently
        ReadingConverter converter1 = new TestReadingConverter();
        ReadingConverter converter2 = new TestReadingConverter();

        converter1.init();
        converter2.init();

        String text = "test";
        String field = "content";

        List<String> readings1 = converter1.convert(text, field, "en");

     * @return the value as an object
     */
    Object getAsObject(String value);

    /**
     * Returns {@literal true} if this converter can handle the specified type.
     *
     * @param clazz
     *            the type. Must not be {@literal null}
     * @return {@literal true} if this converter can handle the specified type
     */
    boolean isTarget(Class<?> clazz);

        List<String> results = converter.convert(text, field, "en");

        assertNotNull(results);
    }

    @Test
    public void testMultipleConverterInstances() throws IOException {
        // Test that multiple converter instances work independently
        AnalyzerConverter converter1 = new AnalyzerConverter(client, settings);
        AnalyzerConverter converter2 = new AnalyzerConverter(client, settings);

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

        return 10;
    }

    /**
     * Initializes the converter.
     *
     * @throws IOException if an I/O error occurs during initialization.
     */
    void init() throws IOException;

    /**
     * Converts the given text into a list of readings based on the specified field and languages.
     *
     * @param text the text to be converted.
     * @param field the field to be used for conversion.

    *   `parallel_for.pfor`: add converters for Softmax, LogSoftmax, IsNaN, All,
        Any, and MatrixSetDiag.
    *   `parallel_for`: add converters for LowerTriangularSolve and Cholesky.
    *   `parallel_for`: add converters for `LogMatrixDeterminant` and
        `MatrixBandPart`.
    *   `parallel_for`: Add converter for `MatrixDiag`.
    *   `parallel_for`: Add converters for `OneHot`, `LowerBound`, `UpperBound`.

Par exemple, vous ne pouvez pas mettre un modèle Pydantic dans une `JSONResponse` sans d'abord le convertir en un `dict` avec tous les types de données (comme `datetime`, `UUID`, etc.) convertis en types compatibles avec JSON.

Pour ces cas, vous pouvez spécifier un appel à `jsonable_encoder` pour convertir vos données avant de les passer à une réponse :

{* ../../docs_src/response_directly/tutorial001.py hl[6:7,21:22] *}

        };
    Function<Object, String> backward = toStringFunction();

    Converter<String, Number> converter = Converter.<String, Number>from(forward, backward);

    assertNull(converter.convert(null));
    assertNull(converter.reverse().convert(null));

    assertEquals((Integer) 5, converter.convert("5"));
    assertEquals("5", converter.reverse().convert(5));
  }

- `watch.Until` now works for long durations. ([#67350](https://github.com/kubernetes/kubernetes/pull/67350), [@tnozicka](https://github.com/tnozicka))
- Added duration metric for CRD webhook converters. ([#74376](https://github.com/kubernetes/kubernetes/pull/74376), [@mbohlool](https://github.com/mbohlool))

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