}

  public void testScale_index_compute_doubleCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
  }

  public void testScale_index_compute_longCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_LONGS))
        .isWithin(ALLOWED_ERROR)

  }

  public void testScale_index_compute_doubleCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
  }

  public void testScale_index_compute_longCollection() {
    assertThat(Quantiles.scale(10).index(1).compute(SIXTEEN_SQUARES_LONGS))
        .isWithin(ALLOWED_ERROR)

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * <pre>{@code
 * double myMedian = median().compute(myDataset);
 * }</pre>
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * <pre>{@code
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }</pre>
 *

        "Expected numeric or variant tensor, got dtype ", dtype);
  }
  return tensorflow::ops::Conj(ctx, input, output, name);
}

class AddGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(b/161805092): Support broadcasting.

		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:

            }

            // 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

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.

= vendor/cloud.google.com/go/compute/metadata licensed under: =


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,

= vendor/cloud.google.com/go/compute licensed under: =


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2
        int key2 = key1 ^ (1 << i);
        // compute hash values
        int hash1 = function.hashInt(key1).asInt();
        int hash2 = function.hashInt(key2).asInt();