FuzzyQuery fuzzyQuery = new FuzzyQuery(term);

        QueryBuilder result = fuzzyQueryCommand.convertFuzzyQuery(context, fuzzyQuery, 1.0f);
        assertNotNull(result);

        // Verify that the query was processed successfully
        assertTrue(result instanceof FuzzyQueryBuilder);
        FuzzyQueryBuilder fqb = (FuzzyQueryBuilder) result;
        assertEquals("title", fqb.fieldName());
        assertEquals(searchText, fqb.value());

        this.extractorMap = extractorMap;
    }

    /**
     * Creates a new ExtractorBuilder instance.
     *
     * @param in      The input stream to be processed by the extractor.
     * @param params  The parameters to be used by the extractor.
     * @return A new ExtractorBuilder instance.
     */

     * `ID <= lastGoodStreamId` can only be replayed on a new connection if they are idempotent.
     *
     * @param lastGoodStreamId the last stream ID the peer processed before sending this message. If
     *     [lastGoodStreamId] is zero, the peer processed no frames.
     * @param errorCode reason for closing the connection.
     * @param debugData only valid for HTTP/2; opaque debug data to send.
     */
    fun goAway(

     * - Any character between 0x20 and 0xD7FF
     * - Any character between 0xE000 and 0xFFFD
     * - Any character between 0x10000 and 0x10FFFF
     *
     * @param in the input string to be processed
     * @return a new string with invalid XML characters removed, or the original
     *         string if it is empty
     */
    public static String stripInvalidXMLCharacters(final String in) {

multiprecision library gmp's integer type mpz_t wrapped to look like
the Go package big's integer type Int.

This is a syntactically valid Go program—it can be parsed with the Go
parser and processed by godoc—but it is not compiled directly by gc.
Instead, a separate tool, cgo, processes it to produce three output
files.  The first two, 6g.go and 6c.c, are a Go source file for 6g and
a C source file for 6c; both compile as part of the named package

        }
    }

    /**
     * Processes and normalizes a document title.
     * Applies text normalization using configured space characters and returns
     * a clean title suitable for indexing.
     *
     * @param responseData the response data from crawling (not currently used)
     * @param title the raw title text to process
     * @param dataMap additional data map (not currently used)

    }

    limit = newLimit
    constructed = header.constructed
    if (name != null) path += name
    try {
      val result = block(header)

      // The object processed bytes beyond its range.
      if (newLimit != -1L && byteCount > newLimit) {
        throw ProtocolException("unexpected byte count at $this")
      }

      return result
    } finally {

ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database.

MinIO's Object Lambda, enables application developers to process data retrieved from MinIO before returning it to an application. You can register a Lambda Function target...

     *
     * @param params The search request parameters
     * @param cursor Function to process each document in the result set
     * @param userBean Optional user information for permission checking
     * @return Total number of documents processed
     */
    public long scrollSearch(final SearchRequestParams params, final BooleanFunction<Map<String, Object>> cursor,

        testData.add(createRelatedContent("test", "Test Content", ""));
        mockBhv.setTestData(testData);

        relatedContentHelper.init();

        // Verify that load was called and data is processed
        String[] results = relatedContentHelper.getRelatedContents("test");
        assertEquals(1, results.length);
        assertEquals("Test Content", results[0]);
    }