return resultList.subList(0, maxReadingNum);
        }
        return resultList;
    }

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

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 {

---
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

  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() {

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;

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 org.codehaus.plexus.component.configurator.ConfigurationListener;
import org.codehaus.plexus.component.configurator.converters.ConfigurationConverter;
import org.codehaus.plexus.component.configurator.converters.ParameterizedConfigurationConverter;
import org.codehaus.plexus.component.configurator.converters.lookup.ConverterLookup;
import org.codehaus.plexus.component.configurator.expression.ExpressionEvaluator;

A função recebe um objeto, como um modelo Pydantic e retorna uma versão compatível com JSON:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

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

O resultado de chamar a função é algo que pode ser codificado com o padrão do Python [`json.dumps()`](https://docs.python.org/3/library/json.html#json.dumps).

     * @param langFieldName The name of the field that contains language information.
     * @param readingConverter The converter to use for reading fields.
     * @param contentsReadingConverter The converter to use for reading content fields.
     * @param normalizer The normalizer to use for normalizing field values.
     * @param analyzer The analyzer to use for analyzing field values.

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;