class_docstr: Predicts random values sampled from a normal distribution.
  
  The parameters of the normal distribution are fitted with running statistics. They parameters
  are independent of the user, the item, or the context, and are instead fitted globally. This
  recommender therefore acts as a dummy model that any serious model should easily outperform.
  
  Parameters
  ----------
  seed
      Random number generation seed. Set this for reproducibility.
  
  Attributes
  ----------
  mean
      stats.Mean
  variance
      stats.Var
  
  Examples
  --------
  
  >>> from river import reco
  
  >>> dataset = (
  ...     ({'user': 'Alice', 'item': 'Superman'}, 8),
  ...     ({'user': 'Alice', 'item': 'Terminator'}, 9),
  ...     ({'user': 'Alice', 'item': 'Star Wars'}, 8),
  ...     ({'user': 'Alice', 'item': 'Notting Hill'}, 2),
  ...     ({'user': 'Alice', 'item': 'Harry Potter'}, 5),
  ...     ({'user': 'Bob', 'item': 'Superman'}, 8),
  ...     ({'user': 'Bob', 'item': 'Terminator'}, 9),
  ...     ({'user': 'Bob', 'item': 'Star Wars'}, 8),
  ...     ({'user': 'Bob', 'item': 'Notting Hill'}, 2)
  ... )
  
  >>> model = reco.RandomNormal(seed=42)
  
  >>> for x, y in dataset:
  ...     model.learn_one(**x, y=y)
  
  >>> model.predict_one(user='Bob', item='Harry Potter')
  6.147299621751425
code: def predict_one(self, user, item, x=None):
      μ = self.mean.get() or 0
      σ = (self.variance.get() or 1) ** 0.5
      return self._rng.gauss(μ, σ)
  
imports: from __future__ import annotations
  from river import reco, stats
label_desc: Use image node_id 3 for calling the RandomNormal obj's underlying member method code with example usage: obj.predict_one(user, item, x) and returns: self