* - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

   * - median with compute taking a double-collection and with computeInPlace;
   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;

    // b/80241237

    Cache<String, String> c = CacheBuilder.newBuilder().maximumSize(1).build();

    assertThat(c.asMap().compute("hash-1", (k, v) -> "a")).isEqualTo("a");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "b")).isEqualTo("b");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "c")).isEqualTo("c");
    assertThat(c.size()).isEqualTo(1);
    assertThat(c.asMap().computeIfAbsent("hash-2", k -> "")).isEqualTo("");

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }
 *

    assertEquals(0x388ee898bad75cbfL, hasher.hash().asLong());
  }

  /** Convenience method to compute a fingerprint on a full bytes array. */
  private static long fingerprint(byte[] bytes) {
    return fingerprint(bytes, bytes.length);
  }

  /** Convenience method to compute a fingerprint on a subset of a byte array. */
  private static long fingerprint(byte[] bytes, int length) {

    }

    computedRuntimes {
        client = false
        daemon = false
        worker = false
    }

    // TODO: Most of these properties are the same across projects. We should
    // compute these at the settings-level instead of the project-level.
    identity {
        baseName = "gradle-$name"
        buildTimestamp = buildTimestamp()
        promotionBuild = isPromotionBuild

   * while iteration is in progress.
   *
   * <p>The returned {@code Iterable} can be iterated over multiple times. Every iterator will
   * compute its next element on the fly. It is thus possible to limit the traversal to a certain
   * number of nodes as follows:
   *
   * {@snippet :
   * Iterables.limit(Traverser.forGraph(graph).breadthFirst(node), maxNumberOfNodes);

   *       CacheLoader#loadAll bulk loading implementations}
   * </ul>
   *
   * <p><b>Warning:</b> For any given key, every {@code loader} used with it should compute the same
   * value. Otherwise, a call that passes one {@code loader} may return the result of another call
   * with a differently behaving {@code loader}. For example, a call that requests a short timeout

   *
   * <p>Since this field isn't {@code volatile}, if an instance of this class is shared across
   * threads before it is initialized, then each thread is likely to compute their own copy of the
   * value.
   */
  @SuppressWarnings("Immutable")
  @LazyInit
  private int publicSuffixIndexCache = SUFFIX_NOT_INITIALIZED;

  /**

common:cuda_clang --copt=-Qunused-arguments
# Select supported compute capabilities (supported graphics cards).
# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current