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.
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docstr: Estimate how likely a leaf node is going to be split.
  
  Uses the node's depth as a heuristic to estimate how likely the leaf is going to become
  a decision node. The deeper the node is in the tree, the more unlikely it is going to be
  split. To cope with the general tree memory management framework, takes the negative of
  the node's depth as return value. In this way, when sorting the tree leaves by their
  "promise value", the deepest nodes are going to be placed at the first positions as
  candidates to be deactivated.
  
  
  Returns
  -------
  int
      The smaller the value, the more unlikely the node is going to be split.
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code: def calculate_promise(self) -> int:
      """Estimate how likely a leaf node is going to be split.
  
      Uses the node's depth as a heuristic to estimate how likely the leaf is going to become
      a decision node. The deeper the node is in the tree, the more unlikely it is going to be
      split. To cope with the general tree memory management framework, takes the negative of
      the node's depth as return value. In this way, when sorting the tree leaves by their
      "promise value", the deepest nodes are going to be placed at the first positions as
      candidates to be deactivated.
  
  
      Returns
      -------
      int
          The smaller the value, the more unlikely the node is going to be split.
  
      """
      return -self.depth
  
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 7 for calling the LeafMean obj's underlying member method code with example usage: obj.calculate_promise() and returns: unknown