// Compute the filter.
    long filter = 0;
    int size = chars.cardinality();
    boolean containsZero = chars.get(0);
    // Compute the hash table.
    char[] table = new char[chooseTableSize(size)];
    int mask = table.length - 1;
    for (int c = chars.nextSetBit(0); c != -1; c = chars.nextSetBit(c + 1)) {
      // Compute the filter at the same time.
      filter |= 1L << c;

    // Compute the filter.
    long filter = 0;
    int size = chars.cardinality();
    boolean containsZero = chars.get(0);
    // Compute the hash table.
    char[] table = new char[chooseTableSize(size)];
    int mask = table.length - 1;
    for (int c = chars.nextSetBit(0); c != -1; c = chars.nextSetBit(c + 1)) {
      // Compute the filter at the same time.
      filter |= 1L << c;

            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 '

  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.

namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,
//                  absl::Span<AbstractTensorHandle*> grad_outputs) override {
//     grad_outputs[0] = grad_inputs[0];

     */
    @Nullable
    <T> T get(@Nonnull Key<T> key);

    /**
     * Retrieve of compute the data associated with the specified key.
     *
     * @param key the key for which to retrieve the session data, must not be {@code null}
     * @param supplier the supplier will compute the new value
     * @return the session data associated with the key
     */
    @Nullable

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build ignore

// Compute Fibonacci numbers with two goroutines
// that pass integers back and forth.  No actual
// concurrency, just threads and synchronization
// and foreign code on multiple pthreads.

package main

import (
	big "."
	"runtime"
)

    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) {

            }

            // 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