// root credentials set via environment variable.
		globalActiveCred = cfg.Credential
	}

	// Init compression config. For future migration, Compression config needs to be copied over from previous version.
	switch cfg.Version {
	case "29":
		// V29 -> V30
		cfg.Compression.Enabled = false
		cfg.Compression.Extensions = strings.Split(compress.DefaultExtensions, config.ValueSeparator)

	"github.com/minio/minio/internal/kms"
)

const (
	minioConfigPrefix = "config"
	minioConfigBucket = minioMetaBucket + SlashSeparator + minioConfigPrefix
	kvPrefix          = ".kv"

	// Captures all the previous SetKV operations and allows rollback.
	minioConfigHistoryPrefix = minioConfigPrefix + "/history"

	// MinIO configuration file.
	minioConfigFile = "config.json"
)

   *
   * @param key the key with which the specified value is to be associated
   * @param value the value to be associated with the specified key
   * @return the value that was previously associated with the key, or {@code null} if there was no
   *     previous entry. (If the bimap contains null values, then {@code forcePut}, like {@code
   *     put}, returns {@code null} both if the key is absent and if it is present with a null
   *     value.)

   *
   * @param key the key with which the specified value is to be associated
   * @param value the value to be associated with the specified key
   * @return the value that was previously associated with the key, or {@code null} if there was no
   *     previous entry. (If the bimap contains null values, then {@code forcePut}, like {@code
   *     put}, returns {@code null} both if the key is absent and if it is present with a null
   *     value.)

         * @return the total page count
         */
        public int getAllPageCount() {
            return allPageCount;
        }

        /**
         * Checks if a previous page exists.
         *
         * @return true if there is a previous page, false otherwise
         */
        public boolean isExistPrePage() {
            return currentPageNumber != 1;
        }

        /**

      factorials[i] = RANDOM_SOURCE.nextInt(factorialBound);
      slowFactorials[i] = RANDOM_SOURCE.nextInt(factorialBound);
      binomials[i] = RANDOM_SOURCE.nextInt(factorials[i] + 1);
    }
  }

  /** Previous version of BigIntegerMath.factorial, kept for timing purposes. */
  private static BigInteger oldSlowFactorial(int n) {
    if (n <= 20) {
      return BigInteger.valueOf(LongMath.factorial(n));
    } else {

# Tutorial - User Guide { #tutorial-user-guide }

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference so you can come back and see exactly what you need.

## Run the code { #run-the-code }

    okayToRun.countDown();
    thread1.join();
    thread2.join();
    assertEquals(1, runCalled.get());
  }

  public void testAddAfterRun() throws Exception {
    // Run the previous test
    testRunOnPopulatedList();

    // If it passed, then verify an Add will be executed without calling run
    CountDownLatch countDownLatch = new CountDownLatch(1);

}

// Entry is an LRU Entry
type Entry[K comparable, V any] struct {
	// Next and previous pointers in the doubly-linked list of elements.
	// To simplify the implementation, internally a list l is implemented
	// as a ring, such that &l.root is both the next element of the last
	// list element (l.Back()) and the previous element of the first list
	// element (l.Front()).
	next, prev *Entry[K, V]

within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training...