return b1 << 24 | (b2 & 0xFF) << 16 | (b3 & 0xFF) << 8 | (b4 & 0xFF);
  }

  private static final class IntConverter extends Converter<String, Integer>
      implements Serializable {
    static final Converter<String, Integer> INSTANCE = new IntConverter();

    @Override
    protected Integer doForward(String value) {
      return Integer.decode(value);
    }

        "the total number of elements (%s) in the arrays must fit in an int",
        result);
    return (int) result;
  }

  private static final class FloatConverter extends Converter<String, Float>
      implements Serializable {
    static final Converter<String, Float> INSTANCE = new FloatConverter();

    @Override
    protected Float doForward(String value) {
      return Float.valueOf(value);
    }

    @Override

* Criar uma cópia do modelo armazenado, atualizando seus atributos com as atualizações parciais recebidas (usando o parâmetro `update`).
* Converter o modelo copiado em algo que possa ser armazenado no seu banco de dados (por exemplo, usando o `jsonable_encoder`).
    * Isso é comparável ao uso do método `.model_dump()`, mas garante (e converte) os valores para tipos de dados que possam ser convertidos em JSON, por exemplo, `datetime` para `str`.

        "the total number of elements (%s) in the arrays must fit in an int",
        result);
    return (int) result;
  }

  private static final class DoubleConverter extends Converter<String, Double>
      implements Serializable {
    static final Converter<String, Double> INSTANCE = new DoubleConverter();

    @Override
    protected Double doForward(String value) {
      return Double.valueOf(value);
    }

    } else if (accum == Long.MIN_VALUE) {
      return null;
    } else {
      return -accum;
    }
  }

  private static final class LongConverter extends Converter<String, Long> implements Serializable {
    static final Converter<String, Long> INSTANCE = new LongConverter();

    @Override
    protected Long doForward(String value) {
      return Long.decode(value);
    }

    @Override

import org.apache.lucene.search.TermQuery;
import org.codelibs.core.CoreLibConstants;
import org.codelibs.fess.suggest.converter.AnalyzerConverter;
import org.codelibs.fess.suggest.converter.KatakanaToAlphabetConverter;
import org.codelibs.fess.suggest.converter.ReadingConverter;
import org.codelibs.fess.suggest.converter.ReadingConverterChain;
import org.codelibs.fess.suggest.entity.SuggestItem;

            // Use the outputs of this converter as inputs for the next converter
            currentInputs = nextInputs;
        }

        return new ArrayList<>(resultSet);
    }

    /**
     * Adds a converter to the chain.
     * @param converter The converter to add.
     */
    public void addConverter(final ReadingConverter converter) {
        converters.add(converter);
    }

    private ReadingConverter converter;
    private TestReadingConverter testConverter;

    @Before
    public void setUp() {
        testConverter = new TestReadingConverter();
        converter = testConverter;
    }

    @Test
    public void testGetMaxReadingNum() {
        // Test default max reading num
        assertEquals(10, converter.getMaxReadingNum());
    }

    @Test

import java.util.Map;

import org.codelibs.core.beans.Converter;
import org.codelibs.core.beans.converter.DateConverter;
import org.codelibs.core.beans.converter.NumberConverter;
import org.codelibs.core.beans.converter.SqlDateConverter;
import org.codelibs.core.beans.converter.TimeConverter;
import org.codelibs.core.beans.converter.TimestampConverter;

/**

Você também pode usar modelos Pydantic como subtipos de `list`, `set`, etc:

{* ../../docs_src/body_nested_models/tutorial006_py310.py hl[18] *}

Isso vai esperar(converter, validar, documentar, etc) um corpo JSON tal qual:

```JSON hl_lines="11"
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2,
    "tags": [
        "rock",