human
(Note that sequential_discrete_marginal is very general, but is also quite slow. For high-performance parallel enumeration that applies to a less general class of models, see the enumeration tutorial.)
Example: implementing lazy evaluation with the Messenger API
Now that we've learned more about the internals of Messenger, let's use it to implement a slightly more complicated effect: lazy evaluation. We first define a LazyValue class that we will use to build up a computation graph:"
Please write code following the instructions in jupyter notebook style.
 Can you provide a code to (Note that sequential_discrete_marginal is very general, but is also quite slow?
assistant
#Here is code to (Note that sequential_discrete_marginal is very general, but is also quite slow.

class LazyValue:
    def __init__(self, fn, *args, **kwargs):
        self._expr = (fn, args, kwargs)
        self._value = None
        
    def __str__(self):
        return "({} {})".format(str(self._expr[0]), " ".join(map(str, self._expr[1])))
        
    def evaluate(self):
        if self._value is None:
            fn, args, kwargs = self._expr
            fn = fn.evaluate() if isinstance(fn, LazyValue) else fn
            args = tuple(arg.evaluate() if isinstance(arg, LazyValue) else arg
                         for arg in args)
            kwargs = {k: v.evaluate() if isinstance(v, LazyValue) else v
                      for k, v in kwargs.items()}
            self._value = fn(*args, **kwargs)
        return self._value