class_docstr: An abstract class for box decompositions.
  
  Note: Internally, we store the negative reference point (minimization).
  
  :meta private:
docstr: Update the set of outcomes.
  
  Returns:
      A boolean indicating if _neg_Y was initialized.
code: def _update_neg_Y(self, Y: Tensor) -> bool:
      r"""Update the set of outcomes.
  
      Returns:
          A boolean indicating if _neg_Y was initialized.
      """
      if Y.isnan().any():
          raise ValueError(
              "NaN inputs are not supported. Got Y with "
              f"{Y.isnan().sum()} NaN values."
          )
      # multiply by -1, since internally we minimize.
      if self._neg_Y is not None:
          self._neg_Y = torch.cat([self._neg_Y, -Y], dim=-2)
          return False
      self._neg_Y = -Y
      return True
  
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 11 for calling the BoxDecomposition obj's underlying member method code with example usage: obj._update_neg_Y(Y) and returns: True, False