"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

        "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

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",

    assertThat(stringIdentityConverter.toString()).isEqualTo("Converter.identity()");

    assertThat(Converter.identity()).isSameInstanceAs(Converter.identity());
  }

  public void testFrom() {
    Function<String, Integer> forward = Integer::parseInt;
    Function<Object, String> backward = toStringFunction();

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

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;

/**

### Sem anotação { #no-annotation }

Você não precisa declarar a anotação de tipo de retorno para transmitir dados binários.

Como o FastAPI não tentará converter os dados para JSON com Pydantic nem serializá-los de nenhuma forma, neste caso a anotação de tipo serve apenas para seu editor e ferramentas; ela não será usada pelo FastAPI.

  public void testStringConverter_convert() {
    Converter<String, TestEnum> converter = Enums.stringConverter(TestEnum.class);
    assertEquals(TestEnum.CHEETO, converter.convert("CHEETO"));
    assertEquals(TestEnum.HONDA, converter.convert("HONDA"));
    assertEquals(TestEnum.POODLE, converter.convert("POODLE"));
    assertThat(converter.convert(null)).isNull();
    assertThat(converter.reverse().convert(null)).isNull();
  }

 * </p>
 * <p>
 * If the source object is a {@link Date}, {@link Calendar}, or {@link Timestamp}, the converted object is created using their millisecond value.
 * For other types, the converted object is created from the string representation of the source object.
 * </p>
 * <p>
 * If no pattern is specified, the pattern used for conversion depends on the locale as follows:

    @Test
    public void testGetAsObjectAndGetAsString() throws Exception {
        final TimestampConverter converter = new TimestampConverter("yyyy/MM/dd HH:mm:ss");
        final java.sql.Timestamp result = (java.sql.Timestamp) converter.getAsObject("2008/12/31 12:34:56");
        System.out.println(result);
        assertThat(converter.getAsString(result), is("2008/12/31 12:34:56"));
    }

    /**
     * @throws Exception
     */