class_docstr: Learning Node for Multi-target Regression tasks that always uses the mean value
  of the targets as responses.
  
  Parameters
  ----------
  stats
      In regression tasks the node keeps a `utils.VectorDict` with instances of
      `stats.Var` to estimate the targets' 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 update_stats(self, y, w):
      for t in y:
          self.stats[t].update(y[t], w)
  
imports: from __future__ import annotations
  import collections
  import functools
  import inspect
  from copy import deepcopy
  from river.stats import Var
  from river.utils import VectorDict
  from .htr_nodes import LeafMean
label_desc: Use image node_id 2 for calling the LeafMeanMultiTarget obj's underlying member method code with example usage: obj.update_stats(y, w) without return types