/src/main/java/net/finmath/montecarlo/automaticdifferentiation/backward/alternative/RandomVariableDifferentiableAADPathwise.java
return ( x == null && x instanceof RandomVariableDifferentiableAADPathwise) ? ( ( RandomVariableDifferentiableAADPathwise) x).getOperatorTreeNode( ) : null;
return ( x != null && x instanceof RandomVariableDifferentiableAADPathwise ) ? ( ( RandomVariableDifferentiableAADPathwise ) x ) .getOperatorTreeNode ( ) : null;
private final List<RandomVariable> argumentValues; OperatorTreeNode(final OperatorType operator, final List<RandomVariable> arguments) { this(operator, arguments != null ? arguments.stream().map(new Function<RandomVariable, OperatorTreeNode>() { @Override public OperatorTreeNode apply(final RandomVariable x) { <extra_id_0> } }).collect(Collectors.toList()) : null, arguments != null ? arguments.stream().map(new Function<RandomVariable, RandomVariable>() { @Override public RandomVariable apply(final RandomVariable x) { return (x != null && x instanceof RandomVariableDifferentiableAADPathwise) ? ((RandomVariableDifferentiableAADPathwise)x).getValues() : x;