// 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

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 JSON.

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

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}

  public void testSerialization_andThen() {
    Converter<String, Long> converterA = Longs.stringConverter();
    Converter<Long, String> reverseConverter = Longs.stringConverter().reverse();
    Converter<String, String> composedConverter = converterA.andThen(reverseConverter);
    SerializableTester.reserializeAndAssert(composedConverter);
  }

  public void testSerialization_from() {

 */
package org.codelibs.fess.suggest.converter;

import java.util.List;

import junit.framework.TestCase;

public class KatakanaToAlphabetConverterTest extends TestCase {

    private KatakanaToAlphabetConverter converter;

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        converter = new KatakanaToAlphabetConverter();
        converter.init();
    }

  }

  /**
   * Returns a serializable {@code Converter} that converts strings from this format to {@code
   * targetFormat}.
   *
   * @since 16.0
   */
  public Converter<String, String> converterTo(CaseFormat targetFormat) {
    return new StringConverter(this, targetFormat);
  }

  private static final class StringConverter extends Converter<String, String>
      implements Serializable {

Por exemplo, você não pode colocar um modelo do Pydantic em uma `JSONResponse` sem antes convertê-lo em um `dict` com todos os tipos de dados (como `datetime`, `UUID`, etc) convertidos para tipos compatíveis com JSON.

Para esses casos, você pode usar o `jsonable_encoder` para converter seus dados antes de repassá-los para a resposta:

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}

        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.