class_docstr: Learning Node for regression tasks that always use the average target
      value as response.
  
  Parameters
  ----------
  stats
      In regression tasks the node keeps an instance of `river.stats.Var` to estimate
      the target's statistics.
  depth
      The depth of the node.
  splitter
      The numeric attribute observer algorithm used to monitor target statistics
      and perform split attempts.
  kwargs
      Other parameters passed to the learning node.
class_docstr_tok: ["Learning","Node","for","regression","tasks","that","always","use","the","average","target","value","as","response",".","Parameters","--","--","--","--","--","stats","In","regression","tasks","the","node","keeps","an","instance","of","`","river.stats.Var","`","to","estimate","the","target","'s","statistics",".","depth","The","depth","of","the","node",".","splitter","The","numeric","attribute","observer","algorithm","used","to","monitor","target","statistics","and","perform","split","attempts",".","kwargs","Other","parameters","passed","to","the","learning","node","."]
code: def __init__(self, stats, depth, splitter, **kwargs):
      if stats is None:
          # Enforce the usage of Var to keep track of target statistics
          stats = Var()
      super().__init__(stats, depth, splitter, **kwargs)
  
imports: from __future__ import annotations
  import inspect
  from river.stats import Var
  from ..splitter import EBSTSplitter
  from ..splitter.nominal_splitter_reg import NominalSplitterReg
  from .leaf import HTLeaf
label_desc: Use image node_id 1 to create a new LeafMean object from inherited base classes: HTLeaf with example: obj = LeafMean(stats, depth, splitter)