output: #fixed code
                               public Schema encodeSchema(SparkMLEncoder encoder){
  		T model = getTransformer();
  
  		Label label = null;
  
  		if(model instanceof HasLabelCol){
  			HasLabelCol hasLabelCol = (HasLabelCol)model;
  
  			String labelCol = hasLabelCol.getLabelCol();
  
  			Feature feature = encoder.getOnlyFeature(labelCol);
  
  			MiningFunction miningFunction = getMiningFunction();
  			switch(miningFunction){
  				case CLASSIFICATION:
  					{
  						if(feature instanceof BooleanFeature){
  							BooleanFeature booleanFeature = (BooleanFeature)feature;
  
  							label = new CategoricalLabel(booleanFeature.getName(), booleanFeature.getDataType(), booleanFeature.getValues());
  						} else
  
  						if(feature instanceof CategoricalFeature){
  							CategoricalFeature categoricalFeature = (CategoricalFeature)feature;
  
  							DataField dataField = (DataField)categoricalFeature.getField();
  
  							label = new CategoricalLabel(dataField);
  						} else
  
  						if(feature instanceof ContinuousFeature){
  							ContinuousFeature continuousFeature = (ContinuousFeature)feature;
  
  							int numClasses = 2;
  
  							if(model instanceof ClassificationModel){
  								ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>)model;
  
  								numClasses = classificationModel.numClasses();
  							}
  
  							List<String> categories = new ArrayList<>();
  
  							for(int i = 0; i < numClasses; i++){
  								categories.add(String.valueOf(i));
  							}
  
  							Field<?> field = encoder.toCategorical(continuousFeature.getName(), categories);
  
  							encoder.putOnlyFeature(labelCol, new CategoricalFeature(encoder, field, categories));
  
  							label = new CategoricalLabel(field.getName(), field.getDataType(), categories);
  						} else
  
  						{
  							throw new IllegalArgumentException("Expected a categorical or categorical-like continuous feature, got " + feature);
  						}
  					}
  					break;
  				case REGRESSION:
  					{
  						Field<?> field = encoder.toContinuous(feature.getName());
  
  						field.setDataType(DataType.DOUBLE);
  
  						label = new ContinuousLabel(field.getName(), field.getDataType());
  					}
  					break;
  				default:
  					throw new IllegalArgumentException("Mining function " + miningFunction + " is not supported");
  			}
  		}
  
  		if(model instanceof ClassificationModel){
  			ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>)model;
  
  			int numClasses = classificationModel.numClasses();
  
  			CategoricalLabel categoricalLabel = (CategoricalLabel)label;
  
  			SchemaUtil.checkSize(numClasses, categoricalLabel);
  		}
  
  		String featuresCol = model.getFeaturesCol();
  
  		List<Feature> features = encoder.getFeatures(featuresCol);
  
  		if(model instanceof PredictionModel){
  			PredictionModel<?, ?> predictionModel = (PredictionModel<?, ?>)model;
  
  			int numFeatures = predictionModel.numFeatures();
  			if(numFeatures != -1){
  				SchemaUtil.checkSize(numFeatures, features);
  			}
  		}
  
  		Schema result = new Schema(label, features);
  
  		return result;
  	}
input: #vulnerable code 
                               public Schema encodeSchema(SparkMLEncoder encoder){
  		T model = getTransformer();
  
  		Label label = null;
  
  		if(model instanceof HasLabelCol){
  			HasLabelCol hasLabelCol = (HasLabelCol)model;
  
  			String labelCol = hasLabelCol.getLabelCol();
  
  			Feature feature = encoder.getOnlyFeature(labelCol);
  
  			MiningFunction miningFunction = getMiningFunction();
  			switch(miningFunction){
  				case CLASSIFICATION:
  					{
  						if(feature instanceof BooleanFeature){
  							BooleanFeature booleanFeature = (BooleanFeature)feature;
  
  							label = new CategoricalLabel(booleanFeature.getName(), booleanFeature.getDataType(), booleanFeature.getValues());
  						} else
  
  						if(feature instanceof CategoricalFeature){
  							CategoricalFeature categoricalFeature = (CategoricalFeature)feature;
  
  							DataField dataField = (DataField)categoricalFeature.getField();
  
  							label = new CategoricalLabel(dataField);
  						} else
  
  						if(feature instanceof ContinuousFeature){
  							ContinuousFeature continuousFeature = (ContinuousFeature)feature;
  
  							int numClasses = 2;
  
  							if(model instanceof ClassificationModel){
  								ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>)model;
  
  								numClasses = classificationModel.numClasses();
  							}
  
  							List<String> categories = new ArrayList<>();
  
  							for(int i = 0; i < numClasses; i++){
  								categories.add(String.valueOf(i));
  							}
  
  							Field<?> field = encoder.toCategorical(continuousFeature.getName(), categories);
  
  							encoder.putOnlyFeature(labelCol, new CategoricalFeature(encoder, field, categories));
  
  							label = new CategoricalLabel(field.getName(), field.getDataType(), categories);
  						} else
  
  						{
  							throw new IllegalArgumentException("Expected a categorical or categorical-like continuous feature, got " + feature);
  						}
  					}
  					break;
  				case REGRESSION:
  					{
  						Field<?> field = encoder.toContinuous(feature.getName());
  
  						field.setDataType(DataType.DOUBLE);
  
  						label = new ContinuousLabel(field.getName(), field.getDataType());
  					}
  					break;
  				default:
  					throw new IllegalArgumentException("Mining function " + miningFunction + " is not supported");
  			}
  		}
  
  		if(model instanceof ClassificationModel){
  			ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>)model;
  
  			CategoricalLabel categoricalLabel = (CategoricalLabel)label;
  
  			int numClasses = classificationModel.numClasses();
  			if(numClasses != categoricalLabel.size()){
  				throw new IllegalArgumentException("Expected " + numClasses + " target categories, got " + categoricalLabel.size() + " target categories");
  			}
  		}
  
  		String featuresCol = model.getFeaturesCol();
  
  		List<Feature> features = encoder.getFeatures(featuresCol);
  
  		if(model instanceof PredictionModel){
  			PredictionModel<?, ?> predictionModel = (PredictionModel<?, ?>)model;
  
  			int numFeatures = predictionModel.numFeatures();
  			if(numFeatures != -1 && features.size() != numFeatures){
  				throw new IllegalArgumentException("Expected " + numFeatures + " features, got " + features.size() + " features");
  			}
  		}
  
  		Schema result = new Schema(label, features);
  
  		return result;
  	} 
                              #location 80 
                              #vulnerability type NULL_DEREFERENCE