fix issues due to merge

src/test/java/org/opensearch/neuralsearch/common/BaseNeuralSearchIT.java

@@ -772,7 +772,6 @@ private String registerModelGroup() {
         return modelGroupId;
     }
 
-
     protected List<Map<String, Object>> getNestedHits(Map<String, Object> searchResponseAsMap) {
         Map<String, Object> hitsMap = (Map<String, Object>) searchResponseAsMap.get("hits");
         return (List<Map<String, Object>>) hitsMap.get("hits");
@@ -787,7 +786,7 @@ protected Optional<Float> getMaxScore(Map<String, Object> searchResponseAsMap) {
         Map<String, Object> hitsMap = (Map<String, Object>) searchResponseAsMap.get("hits");
         return hitsMap.get("max_score") == null ? Optional.empty() : Optional.of(((Double) hitsMap.get("max_score")).floatValue());
     }
-  
+
     /**
      * Enumeration for types of pipeline processors, used to lookup resources like create
      * processor request as those are type specific

src/test/java/org/opensearch/neuralsearch/plugin/NeuralSearchTests.java

@@ -66,6 +66,7 @@ public void testProcessors() {
             null,
             mock(IngestService.class),
             null,
+            null,
             null
         );
         Map<String, Processor.Factory> processors = plugin.getProcessors(processorParams);

src/test/java/org/opensearch/neuralsearch/processor/HybridQueryZScoreIT.java

@@ -125,6 +125,7 @@ public void testComplexQuery_withZScoreNormalization() {
         NeuralQueryBuilder neuralQueryBuilder = new NeuralQueryBuilder(
             TEST_KNN_VECTOR_FIELD_NAME_1,
             TEST_QUERY_TEXT,
+            "",
             modelId,
             5,
             null,

src/test/java/org/opensearch/neuralsearch/processor/normalization/ZScoreNormalizationTechniqueTests.java

@@ -95,7 +95,8 @@ public void testNormalization_whenResultFromOneShardMultipleSubQueries_thenSucce
                 new TopDocs(new TotalHits(0, TotalHits.Relation.EQUAL_TO), new ScoreDoc[0]),
                 new TopDocs(
                     new TotalHits(3, TotalHits.Relation.EQUAL_TO),
-                        // Calculated based on the formula (score - mean_score)/std for the values of mean_score = (0.9 + 0.7 + 0.1)/3 ~ 0.56, std = sqrt(((0.9 - 0.56)^2 + (0.7 - 0.56)^2 + (0.1 - 0.56)^2)/3)
+                    // Calculated based on the formula (score - mean_score)/std for the values of mean_score = (0.9 + 0.7 + 0.1)/3 ~ 0.56,
+                    // std = sqrt(((0.9 - 0.56)^2 + (0.7 - 0.56)^2 + (0.1 - 0.56)^2)/3)
                     new ScoreDoc[] { new ScoreDoc(3, 0.98058068f), new ScoreDoc(4, 0.39223227f), new ScoreDoc(2, -1.37281295f) }
                 )
             )