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");

    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");

    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)

		// 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")
			}
		}

* **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.

labels.supported_search_none=Not Available
labels.purge_search_log_day=Purge Search Log Before
labels.purge_job_log_day=Purge Job Log Before
labels.purge_user_info_day=Purge User Before
labels.purge_by_bots=Bots Name For Purge
labels.csv_file_encoding=Encoding for CSV
labels.notification_to=Notification Email
labels.pathmap_configuration=Path Mapping
labels.pathmap_title_details=Path Mapping
labels.disabled=Disabled

          sortedElements[uniques] = sortedElements[i];
          uniques++;
        }
      }
      Arrays.fill(sortedElements, uniques, length, null);
      if (maybeExpand && uniques * 4 > length * 3) {
        // lots of nonduplicated elements, expand the array by 50%
        sortedElements =
            Arrays.copyOf(sortedElements, IntMath.saturatedAdd(length, length / 2 + 1));
      }

   */
  private InternetDomainName ancestor(int levels) {
    ImmutableList<String> ancestorParts = parts.subList(levels, parts.size());

    // levels equals the number of dots that are getting clipped away, then add the length of each
    // clipped part to get the length of the leading substring that is being removed.
    int substringFrom = levels;
    for (int i = 0; i < levels; i++) {

      list.add(i);
    }

    Ordering<Object> arbitrary =
        new ArbitraryOrdering() {
          @Override
          int identityHashCode(Object object) {
            return ((Integer) object) % 5; // fake tons of collisions!
          }
        };

    // Don't let the elements be in such a predictable order
    list = shuffledCopy(list, new Random(1));

    sort(list, arbitrary);

      list.add(i);
    }

    Ordering<Object> arbitrary =
        new ArbitraryOrdering() {
          @Override
          int identityHashCode(Object object) {
            return ((Integer) object) % 5; // fake tons of collisions!
          }
        };

    // Don't let the elements be in such a predictable order
    list = shuffledCopy(list, new Random(1));

    sort(list, arbitrary);