class_docstr: Class used to keep the split merit of each split candidate, accordingly to its
  gradient and hessian information.
  
  In Stochastic Gradient Trees, the split merit is given by a combination of the loss mean and
  variance. Additionally, the loss in each resulting tree branch is also accounted.
code: def __eq__(self, other):
      return self.loss_mean == other.loss_mean
  
imports: from __future__ import annotations
  import collections
  import copy
  import dataclasses
  import functools
  import math
  import sys
  import typing
  from river.base.typing import FeatureName
  from river.stats import Cov, Var
label_desc: Use image node_id 2 for calling the GradHessMerit obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown