class_docstr: Class used to monitor and update the gradient/hessian information in Stochastic Gradient
  Trees.
  
  Represents the aggregated gradient/hessian data in a node (global node statistics), category,
  or numerical feature's discretized bin.
code: def delta_loss_mean_var(self, delta_pred: float) -> Var:
      m = self.mean
      n = self.total_weight
      mean = (
          delta_pred * m.gradient
          + 0.5 * m.hessian * delta_pred * delta_pred
      )
  
      variance = self.variance
      covariance = self.covariance
  
      grad_term_var = delta_pred * delta_pred * variance.gradient
      hess_term_var = 0.25 * variance.hessian * (delta_pred**4.0)
      sigma = max(
          0.0,
          grad_term_var
          + hess_term_var
          + (delta_pred**3) * covariance,
      )
      return Var._from_state(n, mean, sigma)
  
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 11 for calling the GradHessStats obj's underlying member method code with example usage: obj.delta_loss_mean_var(delta_pred) and returns: Var