"lang_sv": {
          "match": "*_sv",
          "mapping": {
            "type": "text",
            "analyzer": "swedish_analyzer"
          }
        }
      },
      {
        "lang_ta": {
          "match": "*_ta",
          "mapping": {
            "type": "text",
            "analyzer": "empty_analyzer"
          }
        }
      },
      {
        "lang_te": {

        "lang_sv": {
          "match": "*_sv",
          "mapping": {
            "type": "text",
            "analyzer": "swedish_analyzer"
          }
        }
      },
      {
        "lang_ta": {
          "match": "*_ta",
          "mapping": {
            "type": "text",
            "analyzer": "empty_analyzer"
          }
        }
      },
      {
        "lang_te": {

        "lang_sv": {
          "match": "*_sv",
          "mapping": {
            "type": "text",
            "analyzer": "swedish_analyzer"
          }
        }
      },
      {
        "lang_ta": {
          "match": "*_ta",
          "mapping": {
            "type": "text",
            "analyzer": "empty_analyzer"
          }
        }
      },
      {
        "lang_te": {

# TensorFlow C API

- See [www.tensorflow.org/install/lang_c](https://www.tensorflow.org/install/lang_c)
- Nightly builds:
  - [Linux CPU-only](https://storage.googleapis.com/tensorflow-nightly/github/tensorflow/lib_package/libtensorflow-cpu-linux-x86_64.tar.gz)
  - [Linux GPU](https://storage.googleapis.com/tensorflow-nightly/github/tensorflow/lib_package/libtensorflow-gpu-linux-x86_64.tar.gz)

      - name: Export Language Codes
        id: show-langs
        run: |
          echo "langs=$(python ./scripts/docs.py langs-json)" >> $GITHUB_OUTPUT

  build-docs:
    needs:
      - changes
      - langs
    if: ${{ needs.changes.outputs.docs == 'true' }}
    runs-on: ubuntu-latest
    strategy:
      matrix:
        lang: ${{ fromJson(needs.langs.outputs.langs) }}
    steps:
      - name: Dump GitHub context

        final ReadingConverter converter;
        if (langs == null || langs.length == 0) {
            converter = new LangAnalyzerConverter(null);
        } else {
            final ReadingConverterChain chain = new ReadingConverterChain();
            for (final String lang : langs) {
                chain.addConverter(new LangAnalyzerConverter(lang));

    public String normalize(final String text, final String field, final String... langs) {
        final Normalizer normalizer;
        if (langs == null || langs.length == 0) {
            normalizer = new LangAnalyzerNormalizer(null);
        } else {
            final NormalizerChain chain = new NormalizerChain();
            for (final String lang : langs) {
                chain.add(new LangAnalyzerNormalizer(lang));
            }

        // Test convert with null languages
        String text = "test text";
        String field = "content";
        String[] langs = null;

        List<String> results = converter.convert(text, field, langs);

        assertNotNull(results);
        // Results may vary based on analyzer configuration
    }

    @Test

     */
    public FullWidthToHalfWidthAlphabetNormalizer() {
        // nothing
    }

    @Override
    public String normalize(final String text, final String field, final String... langs) {
        final char[] chars = new char[text.length()];
        for (int i = 0; i < chars.length; i++) {
            final char c = text.charAt(i);
            if (c >= 'ａ' && c <= 'ｚ') {

     * @param normalizer the normalizer to use for normalizing the search words
     * @param analyzer the analyzer to use for analyzing the search words
     * @param langs the array of languages associated with the search words
     * @return a SuggestItem created from the given search words and associated data
     */