class_docstr: An abstract class for box decompositions.
  
  Note: Internally, we store the negative reference point (minimization).
  
  :meta private:
docstr: Update non-dominated front and decomposition.
  
  By default, the partitioning is recomputed. Subclasses can override
  this functionality.
  
  Args:
      Y: A `(batch_shape) x n x m`-dim tensor of new, incremental outcomes.
code: def update(self, Y: Tensor) -> None:
      r"""Update non-dominated front and decomposition.
  
      By default, the partitioning is recomputed. Subclasses can override
      this functionality.
  
      Args:
          Y: A `(batch_shape) x n x m`-dim tensor of new, incremental outcomes.
      """
      self._update_neg_Y(Y=Y)
      self.reset()
  
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 12 for calling the BoxDecomposition obj's underlying member method code with example usage: obj.update(Y) without return types