// 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;

}


fun Test.includeSpockAnnotation(fqcn: String) {
    systemProperties.compute("include.spock.annotation") { _, oldValue ->
        if (oldValue == null) fqcn else "$oldValue,$fqcn"
    }
}


fun Test.excludeSpockAnnotation(fqcn: String) {
    systemProperties.compute("exclude.spock.annotation") { _, oldValue ->
        if (oldValue == null) fqcn else "$oldValue,$fqcn"
    }

from fastapi import APIRouter, Body

router = APIRouter()


@router.post("/compute/")
def compute(a: int = Body(), b: str = Body()):

      : 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

		return opVal, opErr
	}

	// Need to evaluate the ConditionRHS
	switch {
	case e.ConditionRHS.Compare != nil:
		cmpRight, cmpRErr := e.ConditionRHS.Compare.Operand.evalNode(r, tableAlias)
		if cmpRErr != nil {
			return nil, cmpRErr
		}

		b, err := opVal.compareOp(strings.ToUpper(e.ConditionRHS.Compare.Operator), cmpRight)
		return FromBool(b), err

	case e.ConditionRHS.Between != nil:

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

 
-     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\

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.