class_docstr: Learning Node for multi-target regression tasks that dynamically selects between
  predictors and might behave as a regression tree node or a model tree node, depending
  on which predictor is the best one.
  
  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):
      pred = {}
      for t in tree.targets:
          if (
              self._fmse_mean[t] < self._fmse_model[t]
          ):  # Act as a regression tree
              pred[t] = (
                  self.stats[t].mean.get() if t in self.stats else 0.0
              )
          else:  # Act as a model tree
              try:
                  pred[t] = self._leaf_models[t].predict_one(x)
              except KeyError:
                  pred[t] = 0.0
      return pred
  
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 LeafAdaptiveMultiTarget obj's underlying member method code with example usage: obj.prediction(x) and returns: pred