def is_pathwise(self, tensor: StochasticTensor, cost_node: CostTensor) -> bool:
if has_differentiable_path(cost_node, tensor):
# There is a differentiable path, so we will just use reparameterization here.
return True
else:
# No automatic baselines. Use the score function.
return False
def adds_loss(self, tensor: StochasticTensor,
cost_node: CostTensor) -> bool:
if has_differentiable_path(cost_node, tensor):
# There is a differentiable path, so we will just use reparameterization here.
return False
raise ValueError(
"There is no differentiable path between the cost tensor and the stochastic tensor. "
"We cannot use reparameterization. Use a different gradient estimator, or make sure your"
"code is differentiable.")