Understanding Bayes Net Inference Algorithm from Russel & Norvig

[0rthodontist]

I don't understand this simple algorithm from Russel & Norvig for computing a distribution of X given certain observed values e:

function Enumeration-Ask(X, e, bn) returns a distribution over X
	inputs: X, the query variable
		e, observed values for variables E
		bn, a Bayes net with variables {X} u E u Y /* Y = hidden variables */
	
	Q(X) <- a distribution over X, initially empty
	for each value xi of X do
		extend e with value xi for X
		Q(xi) <- Enumerate-All(Vars[bn], e)
	return Normalize(Q(X))
	
function Enumerate-All(vars, e) returns a real number
	if Empty?(vars) then return 1.0
	Y <- First(vars)
	if Y has value y in e
		then return P(y | parents(Y)) * Enumerate-All(Rest(vars), e)
		else return the sum over y of P(y | parents(Y)) * Enumerate-All(Rest(vars), ey)
			where ey is e extended with Y = y

I am having trouble understanding exactly what Enumerate-All does. Specifically I do not understand how P(y | parents(Y)) is computed. We don't know the values that the parents of Y take, do we?