List<String> docIds = Arrays.asList("doc1", "doc2", "doc3");
        List<Integer> indexes = Arrays.asList(1, 2, 3);
        RelevanceEvaluationResult result = RelevanceEvaluationResult.withRelevantDocs(docIds, indexes);

        assertTrue(result.isHasRelevantResults());
        assertEquals(docIds, result.getRelevantDocIds());
        assertEquals(indexes, result.getRelevantIndexes());
    }

    @Test

    ImmutableSet<Integer> indexes = ImmutableSet.of(50, 90, 99);
    Map<Integer, Double> referenceQuantiles =
        REFERENCE_ALGORITHM.multipleQuantiles(indexes, 100, dataset.clone());
    assertThat(referenceQuantiles.keySet()).isEqualTo(indexes);
    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      Map<Integer, Double> quantiles = algorithm.multipleQuantiles(indexes, 100, dataset.clone());

    // array of indexes to be calculated is modified between the calls to indexes and compute: since
    // the contract is that it is snapshotted, this shouldn't make any difference to the result.
    int[] indexes = {0, 10, 5, 1, 8, 10};
    ScaleAndIndexes intermediate = Quantiles.scale(10).indexes(indexes);
    indexes[0] = 3;
    assertThat(intermediate.compute(SIXTEEN_SQUARES_DOUBLES))

    // array of indexes to be calculated is modified between the calls to indexes and compute: since
    // the contract is that it is snapshotted, this shouldn't make any difference to the result.
    int[] indexes = {0, 10, 5, 1, 8, 10};
    ScaleAndIndexes intermediate = Quantiles.scale(10).indexes(indexes);
    indexes[0] = 3;
    assertThat(intermediate.compute(SIXTEEN_SQUARES_DOUBLES))

				})
			}
		}
	}
	for _, index := range indexes {
		if index.Class == "UNIQUE" && len(index.Fields) == 1 {
			index.Fields[0].Field.UniqueIndex = index.Name
		}
	}
	return indexes
}

func (schema *Schema) LookIndex(name string) *Index {
	if schema != nil {
		indexes := schema.ParseIndexes()
		for _, index := range indexes {
			if index.Name == name {
				return index
			}

      return Quantiles.scale(scale).index(index).computeInPlace(dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      return Quantiles.scale(scale).indexes(indexes).computeInPlace(dataset);
    }
  },
  ;

  /**
   * Calculates a single quantile. Equivalent to {@code

     * @return the relevant document IDs
     */
    public List<String> getRelevantDocIds() {
        return relevantDocIds;
    }

    /**
     * Returns the list of relevant indexes (1-based) from search results.
     *
     * @return the relevant indexes
     */
    public List<Integer> getRelevantIndexes() {
        return relevantIndexes;
    }

    /**
     * Returns whether relevant results were found.
     *

			}
			if !reflect.DeepEqual(testCase.indexes, gotIndexes) {
				t.Errorf("Expected %v, got %v", testCase.indexes, gotIndexes)
			}
		})
	}
}

// Test tests calculating set indexes.
func TestGetSetIndexes(t *testing.T) {
	testCases := []struct {
		args       []string
		totalSizes []uint64
		indexes    [][]uint64
		success    bool
	}{
		// Invalid inputs.
		{

    // requireNonNull is safe because we use indexes that were populated by the constructor.
    V value = requireNonNull(values[rowIndex][columnIndex]);
    return cellOf(rowKey, columnKey, value);
  }

  @Override
  V getValue(int index) {
    // requireNonNull is safe because we use indexes that were populated by the constructor.

import java.util.Map;
import java.util.Map.Entry;
import org.jspecify.annotations.Nullable;

/**
 * Simple helper class to build a "sparse" array of objects based on the indexes that were added to
 * it. The array will be from 0 to the maximum index given. All non-set indexes will contain null
 * (so it's not really a sparse array, just a pseudo sparse array). The builder can also return a
 * CharEscaper based on the generated array.
 *