* specific tags by re-computing them:
   *
   * ```kotlin
   * val copy = original.clone()
   *
   * val myTag = original.tag(MyTag::class)
   * if (myTag != null) {
   *   copy.tag(MyTag::class) { myTag }
   * }
   * ```
   *
   * ```java
   * Call copy = original.clone();
   *
   * MyTag myTag = original.tag(MyTag.class);
   * if (myTag != null) {
   *   copy.tag(MyTag.class, () -> myTag);

   * the bridge method in addition to the original method, which causes both the original and bridge
   * methods to be subscribed (since both are annotated @Subscribe) without specifically checking
   * for bridge methods.
   */
  // We use an anonymous class to be sure that we generate two methods (bridge and original).
  @SuppressWarnings("AnonymousToLambda")

   * the bridge method in addition to the original method, which causes both the original and bridge
   * methods to be subscribed (since both are annotated @Subscribe) without specifically checking
   * for bridge methods.
   */
  // We use an anonymous class to be sure that we generate two methods (bridge and original).
  @SuppressWarnings("AnonymousToLambda")

- 12 * 12 = 144 drives pool2

> Notice the requirement of common SLA here original cluster had 1024 drives with 16 drives per erasure set with default parity of '4', second pool is expected to have a minimum of 8 drives per erasure set to match the original cluster SLA (parity count) of '4'. '12' drives stripe per erasure set in the second pool satisfies the original pool's parity count.

  public TablesTransposeColumnTest() {
    super(false, true, true, true, true);
  }

  @Override
  Table<String, Character, Integer> makeTable() {
    Table<Character, String, Integer> original = TreeBasedTable.create();
    return transpose(original);
  }

  public TablesTransposeColumnTest() {
    super(false, true, true, true, true);
  }

  @Override
  Table<String, Character, Integer> makeTable() {
    Table<Character, String, Integer> original = TreeBasedTable.create();
    return transpose(original);
  }

  public void testCreateCopy() {
    TreeBasedTable<String, Integer, Character> original =
        TreeBasedTable.create(Collections.reverseOrder(), Ordering.usingToString());
    original.put("foo", 3, 'a');
    original.put("foo", 12, 'b');
    original.put("bar", 5, 'c');
    original.put("cat", 8, 'd');
    table = TreeBasedTable.create(original);
    assertThat(table.rowKeySet()).containsExactly("foo", "cat", "bar").inOrder();

JSON só pode conter strings codificadas em UTF-8, portanto não pode conter bytes puros.

Base64 pode codificar dados binários em strings, mas, para isso, precisa usar mais caracteres do que os dados binários originais; assim, normalmente é menos eficiente do que arquivos comuns.

Use base64 apenas se realmente precisar incluir dados binários em JSON e não puder usar arquivos para isso.

## Pydantic `bytes` { #pydantic-bytes }

    super(false, false, false, false);
  }

  @Override
  Table<Integer, String, Character> makeTable() {
    RowSortedTable<Integer, String, Character> original = TreeBasedTable.create();
    return unmodifiableRowSortedTable(original);
  }

  @Override
  protected Map<String, Map<Integer, Character>> makePopulatedMap() {
    RowSortedTable<Integer, String, Character> table = TreeBasedTable.create();

  public UnmodifiableTableRowMapTest() {
    super(false, false, false, false);
  }

  @Override
  Table<String, Integer, Character> makeTable() {
    Table<String, Integer, Character> original = HashBasedTable.create();
    return unmodifiableTable(original);
  }

  @Override
  protected Map<String, Map<Integer, Character>> makePopulatedMap() {
    Table<String, Integer, Character> table = HashBasedTable.create();