* default} methods. Instead, it inherits their default implementations. When those implementations
 * invoke methods, they invoke methods on the {@code ForwardingSortedSet}.
 *
 * <p>Each of the {@code standard} methods, where appropriate, uses the set's comparator (or the
 * natural ordering of the elements, if there is no comparator) to test element equality. As a

		close(r.exitReader)
		r.readerWg.Wait()
		r.exitReader = nil
		r.input = nil
	}
	return nil
}

// startReader will start a reader that accepts input from r.input.
// Input should be root -> object input. Each root indicates a record.
// If r.input is closed, it is assumed that no more input will come.
// When this function returns r.readerWg will be decremented and r.decoded will be closed.

 *   <li>orders {@link ValueGraph#nodes()} in the order in which the elements were added (insertion
 *       order)
 * </ul>
 *
 * <p>{@code ValueGraph}s built by this class also guarantee that each collection-returning accessor
 * returns a <b>(live) unmodifiable view</b>; see <a
 * href="https://github.com/google/guava/wiki/GraphsExplained#accessor-behavior">the external
 * documentation</a> for details.
 *

   * executor promptly, or else the returned {@code ListenableFuture} may fail to work. The task's
   * execution consists of blocking until the input future is {@linkplain Future#isDone() done}, so
   * each call to this method may claim and hold a thread for an arbitrary length of time. Use of
   * bounded executors or other executors that may fail to execute a task promptly may result in
   * deadlocks.
   *

    }

    @Override
    public Map<String, Collection<Integer>> create(Object... elements) {
      ImmutableMap.Builder<String, Integer> builder = ImmutableMap.builder();
      // assumes that each set is a singleton or less (as is done for the samples)
      for (Object elem : elements) {
        @SuppressWarnings("unchecked") // safe by generator contract

        multiset2.add("equalIn1");
        multiset2.add("fewerIn1", 3);
        multiset2.add("onlyIn2", 2);
        for (int i = 0; i < elements.length; i++) {
          // add 1 more copy of each element to multiset1 than multiset2
          multiset1.add(elements[i], i + 2);
          multiset2.add(elements[i], i + 1);
        }
        return difference(multiset1, multiset2);
      }
    };
  }

	// Split data into 3 parts
	partSize := len(dummyData) / 3
	part1Data := dummyData[0:partSize]
	part2Data := dummyData[partSize : 2*partSize]
	part3Data := dummyData[2*partSize:]

	// Calculate CRC32C checksum for each part using NewChecksumFromData
	checksumType := ChecksumCRC32C

	part1Checksum := NewChecksumFromData(checksumType, part1Data)
	part2Checksum := NewChecksumFromData(checksumType, part2Data)

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

	return -1
}

// getServerPoolsAvailableSpace will return the available space of each pool after storing the content.
// If there is not enough space the pool will return 0 bytes available.
// The size of each will be multiplied by the number of sets.
// Negative sizes are seen as 0 bytes.

            verify(mockNdrBuffer).enc_ndr_small((byte) 0x22);
            verify(mockNdrBuffer).advance(6);
            verify(mockNdrBuffer).derive(anyInt());
            // Verify each node byte is encoded
            verify(mockNdrBuffer).enc_ndr_small((byte) 0xAA);
            verify(mockNdrBuffer).enc_ndr_small((byte) 0xBB);
            verify(mockNdrBuffer).enc_ndr_small((byte) 0xCC);