(in px)\n        .map(str => toValue(str, measurement, popperOffsets, referenceOffsets))\n    );\n  });\n\n  // Loop trough the offsets arrays and execute the operations\n  ops.forEach((op, index) => {\n    op.forEach((frag, index2) => {\n      if (isNumeric(frag)) {\n        offsets[index] += frag * (op[index2 - 1] === '-' ? -1 : 1);\n      }\n    });\n  });\n  return offsets;\n}\n\n/**\n * @function\n * @memberof Modifiers\n * @argument {Object} data - The data object generated by update method\n...

        return client.index(request);
    }

    /**
     * Indexes a document asynchronously with a callback.
     *
     * @param request  the index request
     * @param listener the response listener
     */
    @Override
    public void index(final IndexRequest request, final ActionListener<IndexResponse> listener) {
        client.index(request, listener);

      int hash = map.hash(key);
      int index = hash & (table.length() - 1);
      ReferenceEntry<Object, Object> first = table.get(index);
      entry = map.newEntry(key, hash, first);
      ValueReference<Object, Object> valueRef = map.newValueReference(entry, value, 1);
      entry.setValueReference(valueRef);
      segment.recordWrite(entry, 1, map.ticker.read());
      table.set(index, entry);
      originalMap.put(key, value);

      int hash = map.hash(key);
      int index = hash & (table.length() - 1);
      ReferenceEntry<Object, Object> first = table.get(index);
      entry = map.newEntry(key, hash, first);
      ValueReference<Object, Object> valueRef = map.newValueReference(entry, value, 1);
      entry.setValueReference(valueRef);
      segment.recordWrite(entry, 1, map.ticker.read());
      table.set(index, entry);
      originalMap.put(key, value);

  if (ic->num_outputs() <= new_src.index) {
    status->status = tensorflow::errors::OutOfRange(
        "Cannot update edge. Output index [", new_src.index,
        "] is greater than the number of total outputs [", ic->num_outputs(),
        "].");
    return;
  }
  tensorflow::shape_inference::ShapeHandle shape = ic->output(new_src.index);

  tensorflow::shape_inference::InferenceContext* ic_dst =

        preWriteCleanup(now);

        int newCount = this.count - 1;
        AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
        int index = hash & (table.length() - 1);
        ReferenceEntry<K, V> first = table.get(index);

        for (e = first; e != null; e = e.getNext()) {
          K entryKey = e.getKey();
          if (e.getHash() == hash
              && entryKey != null

        preWriteCleanup(now);

        int newCount = this.count - 1;
        AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
        int index = hash & (table.length() - 1);
        ReferenceEntry<K, V> first = table.get(index);

        for (e = first; e != null; e = e.getNext()) {
          K entryKey = e.getKey();
          if (e.getHash() == hash
              && entryKey != null

import org.opensearch.index.query.RangeQueryBuilder;
import org.opensearch.index.query.RegexpQueryBuilder;
import org.opensearch.index.query.SpanTermQueryBuilder;
import org.opensearch.index.query.TermQueryBuilder;
import org.opensearch.index.query.TermsQueryBuilder;
import org.opensearch.index.query.WildcardQueryBuilder;
import org.opensearch.index.query.functionscore.FunctionScoreQueryBuilder;

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