class_docstr: A class for partitioning the (non-)dominated space into hyper-cells.
  
  Note: this assumes maximization. Internally, it multiplies outcomes by -1
  and performs the decomposition under minimization.
  
  This class is abstract to support to two applications of Alg 1 from
  [Lacour17]_: 1) partitioning the space that is dominated by the Pareto
  frontier and 2) partitioning the space that is not dominated by the
  Pareto frontier.
  
  :meta private:
docstr: Compute partitioning given local upper bounds for the minimization problem.
  
  This method should set self.hypercell_bounds
code: def _get_partitioning(self) -> None:
      r"""Compute partitioning given local upper bounds for the minimization problem.
  
      This method should set self.hypercell_bounds
      """
      pass
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Optional
  import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.utils.multi_objective.box_decompositions.utils import _expand_ref_point, _pad_batch_pareto_frontier, update_local_upper_bounds_incremental
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 6 for calling the FastPartitioning obj's underlying member method code with example usage: obj._get_partitioning() without return types