public void testConverterToForward() {
    assertEquals("FooBar", UPPER_UNDERSCORE.converterTo(UPPER_CAMEL).convert("FOO_BAR"));
    assertEquals("fooBar", UPPER_UNDERSCORE.converterTo(LOWER_CAMEL).convert("FOO_BAR"));
    assertEquals("FOO_BAR", UPPER_CAMEL.converterTo(UPPER_UNDERSCORE).convert("FooBar"));
    assertEquals("FOO_BAR", LOWER_CAMEL.converterTo(UPPER_UNDERSCORE).convert("fooBar"));
  }

  public void testConverterToBackward() {

  }

  /**
   * 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 {

  }

  /**
   * 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 {

            String s;
            while ((s = queue.poll()) != null && convertedTexts.size() <= getMaxReadingNum()) {
                final List<String> results = converter.convert(s, field, lang);
                convertedTexts.addAll(results);
            }
            queue.addAll(convertedTexts);
        }

        return convertedTexts;
    }

    public void addConverter(final ReadingConverter converter) {

---
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

import org.codehaus.plexus.component.configurator.converters.ConfigurationConverter;
import org.codehaus.plexus.component.configurator.converters.composite.ObjectWithFieldsConverter;
import org.codehaus.plexus.component.configurator.converters.lookup.ConverterLookup;
import org.codehaus.plexus.component.configurator.converters.lookup.DefaultConverterLookup;

class EnhancedConverterLookup implements ConverterLookup {

        };
    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));
  }

import org.codehaus.plexus.component.configurator.converters.AbstractConfigurationConverter;
import org.codehaus.plexus.component.configurator.converters.basic.AbstractBasicConverter;
import org.codehaus.plexus.component.configurator.converters.composite.ObjectWithFieldsConverter;
import org.codehaus.plexus.component.configurator.converters.lookup.ConverterLookup;

{!> ../../docs_src/encoder/tutorial001_py310.py!}
```

////

//// tab | Python 3.8+

```Python hl_lines="5  22"
{!> ../../docs_src/encoder/tutorial001.py!}
```

////

Neste exemplo, ele converteria o modelo Pydantic em um `dict`, e o `datetime` em um `str`.

## Retornando uma `Response`

O exemplo acima mostra todas as partes que você precisa, mas ainda não é muito útil, já que você poderia ter retornado o `item` diretamente, e o **FastAPI** colocaria em uma `JSONResponse` para você, convertendo em um `dict`, etc. Tudo isso por padrão.

Agora, vamos ver como você pode usar isso para retornar uma resposta personalizada.