}

    /**
     * Converts a phrase query to a query builder.
     *
     * @param context the query context
     * @param phraseQuery the phrase query
     * @param boost the boost factor
     * @return the query builder
     */
    protected QueryBuilder convertPhraseQuery(final QueryContext context, final PhraseQuery phraseQuery, final float boost) {
        final Term[] terms = phraseQuery.getTerms();

    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);
    // Check to make sure that we will not exceed the maximum load factor.
    if (expectedEntries > (int) (loadFactor * tableSize)) {
      tableSize <<= 1;
      return (tableSize > 0) ? tableSize : MAX_TABLE_SIZE;
    }
    return tableSize;
  }

    }

    /**
     * Converts a prefix query to a query builder.
     *
     * @param context the query context
     * @param prefixQuery the prefix query
     * @param boost the boost factor
     * @return the query builder
     */
    protected QueryBuilder convertPrefixQuery(final QueryContext context, final PrefixQuery prefixQuery, final float boost) {

     *
     * @param context the query context containing search configuration
     * @param query the Lucene query to convert (must be a FuzzyQuery)
     * @param boost the boost factor to apply to the query
     * @return OpenSearch QueryBuilder for fuzzy matching
     * @throws InvalidQueryException if the query is not a FuzzyQuery
     */
    @Override

- Already compressed objects are not fit for compression since they do not have compressible patterns.
Such objects do not produce efficient [`LZ compression`](https://en.wikipedia.org/wiki/LZ77_and_LZ78)
which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.

         * negative so it with NT_STATUS_SUCCESS (0) the binary search will not be
         * performed properly. The effect is that the code at index 1 is never found
         * (NT_STATUS_UNSUCCESSFUL). So here we factor out NT_STATUS_SUCCESS
         * as a special case (which it is).
         */
        if (errcode == 0) {
            return "NT_STATUS_SUCCESS";
        }
        if ((errcode & 0xC0000000) == 0xC0000000) {

    }

    /**
     * Converts a Lucene BooleanQuery to an OpenSearch BoolQueryBuilder.
     * @param context The query context.
     * @param booleanQuery The boolean query to convert.
     * @param boost The boost factor to apply.
     * @return The converted BoolQueryBuilder.
     */
    protected QueryBuilder convertBooleanQuery(final QueryContext context, final BooleanQuery booleanQuery, final float boost) {

$ python main.py

Hello World from Python
```

</div>

/// tip

Подробнее об этом можно прочитать на сайте <a href="https://12factor.net/config" class="external-link" target="_blank">The Twelve-Factor App: Config</a>.

///

## Типизация и Валидация

		<pre>content_length:[1000 TO 10000]</pre>
		If you want to exclude the upper and lower bounds, use "{}".
	</dd>
	<dt>Boost</dt>
	<dd>
		To boost a term use the "^" symbol with a boost factor (a number) at
		the end of the term you are searching.
		<pre>Fess^100</pre>
	</dd>
	<dt>Fuzzy</dt>
	<dd>
		To do a fuzzy search use the "~" symbol at the end of a single word

            for (i in 2..997) {
              sink.writeUtf8(String.format(" * $i = ${factor(i)}\n"))
            }
          }

          private fun factor(n: Int): String {
            for (i in 2 until n) {
              val x = n / i
              if (x * i == n) return "${factor(x)} × $i"
            }
            return n.toString()
          }
        }