class_docstr: Partition dominated space into axis-aligned hyperrectangles.
  
  This uses the Algorithm 1 from [Lacour17]_.
  
  Example:
      >>> bd = DominatedPartitioning(ref_point, Y)
docstr: Set the partitioning to be a single cell in the case of no Pareto points.
code: def _get_single_cell(self) -> None:
      r"""Set the partitioning to be a single cell in the case of no Pareto points."""
      # Set lower and upper bounds to be the reference point to define an empty cell
      cell_bounds = self.ref_point.expand(
          2, *self._neg_pareto_Y.shape[:-2], 1, self.num_outcomes
      ).clone()
      self.hypercell_bounds = cell_bounds
  
file_path: botorch/botorch/utils/multi_objective/box_decompositions/dominated.py
imports: from __future__ import annotations
  from botorch.utils.multi_objective.box_decompositions.box_decomposition import FastPartitioning
  from botorch.utils.multi_objective.box_decompositions.utils import compute_dominated_hypercell_bounds_2d, get_partition_bounds
  from torch import Tensor
label_desc: Use image node_id 4 for calling the DominatedPartitioning obj's underlying member method code with example usage: obj._get_single_cell() without return types