t.Fatalf("%+v: expected = %d, got: %d", testCase, testCase.expectedIndex, index)
		}
	}
}

// Test objectPartIndex(). generates a sample FileInfo data and asserts
// the output of objectPartIndex() with the expected value.
func TestObjectPartIndex(t *testing.T) {
	testCases := []struct {
		partNum       int
		expectedIndex int
	}{
		{2, 1},

Generic validations and metadata:

* `alias`
* `title`
* `description`
* `deprecated`

Validations specific for strings:

* `min_length`
* `max_length`
* `pattern`

In these examples you saw how to declare validations for `str` values.

 * are contractually guaranteed to be unmodifiable and thread-safe.
 *
 * <p>The Guava User Guide has <a
 * href="https://github.com/google/guava/wiki/GraphsExplained#building-graph-instances">more
 * information on (and examples of) building graphs</a>.
 *
 * <h3>Additional documentation</h3>
 *
 * <p>See the Guava User Guide for the {@code common.graph} package (<a

*   [Performance] Increase performance of `tf.layers.conv2d` when setting
    use_bias=True by 2x by using nn.bias_add.
*   Update iOS examples to use CocoaPods, and moved to tensorflow/examples/ios.
*   Adds a family= attribute in `tf.summary` ops to allow controlling the tab
    name used in Tensorboard for organizing summaries.

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

  /**
   * Returns the sample covariance of the values. The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same pair of values
   * multiple times, due to numerical errors.
   *

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

        }
    }

    @Test
    public void searchTestWithBoost() throws Exception {
        String field = "content";
        String query1 = "java";
        String query2 = "sample";
        Map<String, String> params = new HashMap<>();
        params.put("q", field + ":" + query1 + "^1000 OR " + field + ":" + query2);
        params.put("num", "100");

 * contractually guaranteed to be unmodifiable and thread-safe.
 *
 * <p>The Guava User Guide has <a
 * href="https://github.com/google/guava/wiki/GraphsExplained#building-graph-instances">more
 * information on (and examples of) building graphs</a>.
 *
 * <h3>Additional documentation</h3>
 *
 * <p>See the Guava User Guide for the {@code common.graph} package (<a