}

  /**
   * Returns a converter whose {@code convert} method applies {@code secondConverter} to the result
   * of this converter. Its {@code reverse} method applies the converters in reverse order.
   *
   * <p>The returned converter is serializable if {@code this} converter and {@code secondConverter}
   * are.
   */
  public final <C> Converter<A, C> andThen(Converter<B, C> secondConverter) {

---
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

        };
    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.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 {

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;

import java.util.List;
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;
import org.codelibs.core.convert.DateConversionUtil;

  }

  /**
   * 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 {

# Codificador Compatível com JSON

Existem alguns casos em que você pode precisar converter um tipo de dados (como um modelo Pydantic) para algo compatível com JSON (como um `dict`, `list`, etc).

Por exemplo, se você precisar armazená-lo em um banco de dados.

Para isso, **FastAPI** fornece uma função `jsonable_encoder()`.

## Usando a função `jsonable_encoder`

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:

```Python hl_lines="6-7  21-22"
{!../../docs_src/response_directly/tutorial001.py!}