if (expected.equals(in)) {
      assertSame(in, out);
    } else {
      assertEquals(expected, out);
    }
  }

  // Test collapse() a little differently than the rest, as we really want to
  // cover lots of different configurations of input text
  public void testCollapse() {
    // collapsing groups of '-' into '_' or '-'
    doTestCollapse("-", "_");
    doTestCollapse("x-", "x_");
    doTestCollapse("-x", "_x");

    public static final String PURGE_JOB_LOG_DAY_PROPERTY = "purge.joblog.day";

    /** Property key for bot-based purge configuration. */
    public static final String PURGE_BY_BOTS_PROPERTY = "purge.by.bots";

    /** Property key for search file proxy configuration. */
    public static final String SEARCH_FILE_PROXY_PROPERTY = "search.file.proxy";

    // ============================================================

    if (expected.equals(in)) {
      assertSame(in, out);
    } else {
      assertEquals(expected, out);
    }
  }

  // Test collapse() a little differently than the rest, as we really want to
  // cover lots of different configurations of input text
  public void testCollapse() {
    // collapsing groups of '-' into '_' or '-'
    doTestCollapse("-", "_");
    doTestCollapse("x-", "x_");
    doTestCollapse("-x", "_x");

labels.ignoreFailureType=Genegeerde fouttypen
labels.lastAccessTime=Laatste toegangstijd
labels.notificationTo=Melding aan
labels.num=Aantal
labels.pn=Paginanummer
labels.protocolScheme=Schema
labels.purgeByBots=Verwijderen door Bots
labels.logLevel=Logboekniveau
labels.purgeSearchLogDay=Zoeklogboeken verwijderen
labels.query=Query
labels.queryId=Query-ID
labels.rt=rt
labels.searchLog=Zoeklogboek
labels.sort=Sorteren
labels.start=Startpositie

* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.
* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

		// do an additional verification whether object exists when object is deletemarker and request
		// is from replication
		if opts.CheckDMReplicationReady {
			topts := opts
			topts.VersionID = ""
			goi, gerr := getObjectInfo(ctx, bucket, object, topts)
			if gerr == nil || goi.VersionID != "" { // object layer returned more info because object is deleted
				w.Header().Set(xhttp.MinIOTargetReplicationReady, "true")
			}
		}

      char c = ipString.charAt(i);
      if (c == '.') {
        hasDot = true;
      } else if (c == ':') {
        if (hasDot) {
          return null; // Colons must not appear after dots.
        }
        hasColon = true;
      } else if (c == '%') {
        percentIndex = i;
        break;
      } else if (Character.digit(c, 16) == -1) {

    assertThat(hpackReader!!.headerCount).isEqualTo(1)
  }

  /** Header table backing array is initially 8 long, let's ensure it grows.  */
  @Test
  fun dynamicallyGrowsBeyond64Entries() {
    // Lots of headers need more room!
    hpackReader = Hpack.Reader(bytesIn, 16384, 4096)
    bytesIn.writeByte(0x3F) // Dynamic table size update (size = 16384).
    bytesIn.writeByte(0xE1)
    bytesIn.writeByte(0x7F)

labels.ignoreFailureType=Ignorierter Fehlertyp
labels.lastAccessTime=Letzte Zugriffszeit
labels.notificationTo=Benachrichtigung an
labels.num=Anzahl
labels.pn=Seitennummer
labels.protocolScheme=Schema
labels.purgeByBots=Bots löschen
labels.logLevel=Protokollebene
labels.purgeSearchLogDay=Suchprotokoll löschen
labels.query=Abfrage
labels.queryId=Abfrage-ID
labels.rt=rt
labels.searchLog=Suchprotokoll
labels.sort=Sortieren

* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.