Artificial dataset recipes with shape specified still requires length in evaluate

[emptymalei]

gluonts/src/gluonts/dataset/artificial/recipe.py

Lines 908 to 924 in cac5e6b

	class RandomUniform(Lifted):
	@validated()
	def __init__(
	self,
	low: ValueOrCallable = 0.0,
	high: ValueOrCallable = 1.0,
	shape=(0,),
	) -> None:
	self.low = low
	self.high = high
	self.shape = shape
	def __call__(self, x: Env, length: int, *args, **kwargs):
	low = resolve(self.low, x, length, *args, **kwargs)
	high = resolve(self.high, x, length, *args, **kwargs)
	s = expand_shape(self.shape, length)
	return np.random.uniform(low, high, s)

For example, I create a variable rcp.RandomUniform(low=0, high=3, shape=(1,)) and I would like to evaluate it, I use the following code

ru = rcp.RandomUniform(low=0, high=3, shape=(1,))

rcp.evaluate(
    ru,
    length=None
)

Here the second argument, length is not needed to evaluate this random variable, however, evaluate requires this length argument.

Similarly, if we simply call this

ru(None, None)

While the two arguments are required, it seems that none of the two required arguments is useful here.

This seems to be a consistent behavior of expand_shape

I am wondering if there is a different way to evaluate such variables. It is a bit confusing to have required arguments that is not used at all.