class_docstr: Learning Node for Multi-target Regression tasks that always uses learning models
  for each target.
  
  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.
  leaf_models
      A dictionary composed of target identifiers and their respective predictive models.
  kwargs
      Other parameters passed to the learning node.
code: def prediction(self, x, *, tree=None):
      return {
          t: self._leaf_models[t].predict_one(x)
          if t in self._leaf_models
          else 0.0
          for t in tree.targets
      }
  
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 3 for calling the LeafModelMultiTarget obj's underlying member method code with example usage: obj.prediction(x) and returns: unknown