/src/main/java/net/finmath/montecarlo/automaticdifferentiation/forward/RandomVariableDifferentiableAD.java
return ( x ! = ( ( RandomVariableDifferentiableAD) x).getOperatorTreeNode( ) ;
return ( x != null && x instanceof RandomVariableDifferentiableAD ) ? ( ( RandomVariableDifferentiableAD ) x ) .getOperatorTreeNode ( ) : null;
private static final RandomVariable minusOne = new Scalar(-1.0); OperatorTreeNode(final OperatorType operatorType, final List<RandomVariable> arguments, final Object operator) { this(operatorType, 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 RandomVariableDifferentiableAD) ? ((RandomVariableDifferentiableAD)x).getValues() : x;