// 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;

      : forward_outputs_(f_outputs) {
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    AbstractTensorHandle* upstream_grad = grad_outputs[0];

        // 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

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];

 
-     The choice of symbol is arbitrary. The static address we obtain
-     by constructing a non GOT reference to the symbol, the dynamic
-     address of the symbol we compute using adrp/add to compute the
-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;
-  ElfW(Addr) dynamic_addr;
-
-  asm ("					\n\
-	adrp	%1, _dl_start;			\n\

     */
    @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

func (e *Expression) evalNode(r Record, tableAlias string) (*Value, error) {
	if len(e.And) == 1 {
		// In this case, result is not required to be boolean
		// type.
		return e.And[0].evalNode(r, tableAlias)
	}

	// Compute OR of conditions
	result := false
	for _, ex := range e.And {
		res, err := ex.evalNode(r, tableAlias)
		if err != nil {
			return nil, err
		}
		b, ok := res.ToBool()
		if !ok {
			return nil, errExpectedBool

 *
 * @since 4.0.0
 */
@Experimental
public interface ExtensibleEnum {

    /**
     * The {@code id} uniquely represents a value for this extensible enum.
     * This id should be used to compute the equality and hash code for the instance.
     *
     * @return the id
     */
    @Nonnull
    String id();

plugins {
    id("org.gradle.toolchains.foojay-resolver-convention") version("0.8.0")
}

// Shared basics for all
include("basics")

// Platform: defines shared dependency versions
include("build-platform")

// Compute the identity/version we are building and related details (like current git commit)
include("module-identity")

// Code quality rules common to :build-logic and the root build
include("code-quality-rules")

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.