class_docstr: An inducing point allocator that greedily chooses inducing points with large
  predictive variance and that are in promising regions of the search
  space (according to the model form the previous BO step), see [moss2023ipa]_.
docstr: Build the improvement-based quality function required for the greedy
  improvement reduction inducing point allocation strategy.
  
  Returns:
      A quality function.
docstr_tok: ["Build","the","improvement-based","quality","function","required","for","the","greedy","improvement","reduction","inducing","point","allocation","strategy",".","Returns",":","A","quality","function","."]
code: def _get_quality_function(
      self,
  ) -> QualityFunction:
      """
      Build the improvement-based quality function required for the greedy
      improvement reduction inducing point allocation strategy.
  
      Returns:
          A quality function.
      """
  
      return ExpectedImprovementQualityFunction(
          self._model, self._maximize
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.model import Model
  from botorch.utils.probability.utils import ndtr, phi
  from gpytorch.module import Module
  from linear_operator.operators import LinearOperator
  from torch import Tensor
label_desc: Use image node_id 2 for calling the GreedyImprovementReduction obj's underlying member method code with example usage: obj._get_quality_function() and returns: ExpectedImprovementQualityFunction