* - 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;

@pytest.mark.parametrize(
    "path", ["/a/compute", "/a/compute/", "/b/compute", "/b/compute/"]
)
def test_post(path):
    data = {"a": 2, "b": "foo"}
    response = client.post(path, json=data)
    assert response.status_code == 200, response.text
    assert data == response.json()


@pytest.mark.parametrize(
    "path", ["/a/compute", "/a/compute/", "/b/compute", "/b/compute/"]
)
def test_post_invalid(path):

 *
 * <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);
 * }
 *

    // 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("");

  type: KClass<T>,
  compute: () -> T,
): T {
  var computed: T? = null

  while (true) {
    val tags = get()

    // If the element is already present. Return it.
    val existing = tags[type]
    if (existing != null) return existing

    if (computed == null) {
      computed = compute()
    }

    // If we successfully add the computed element, we're done.

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '

        // use original key
        return parent.computeIfPresent(key, remappingFunction);
    }

    @Override
    public V compute(final K key, final BiFunction<? super K, ? super V, ? extends V> remappingFunction) {
        // use original key
        return parent.compute(key, remappingFunction);
    }

    @Override

            }

            // Compute Resource Group IDs with Resource Domain ID to get SIDs
            this.resourceGroupSids = new SID[resourceGroups.length];
            for (int i = 0; i < resourceGroups.length; i++) {
                this.resourceGroupSids[i] = new SID(resourceDomainId, resourceGroups[i].getId());
            }

            // Compute User IDs with Domain ID to get User SIDs