class_docstr: A list of box decompositions.
class_docstr_tok: ["A","list","of","box","decompositions","."]
docstr: This returns the non-dominated set.
  
  Note: Internally, we store the negative pareto set (minimization).
  
  Returns:
      A list where the ith element is the `n_pareto_i x m`-dim tensor
          of pareto optimal outcomes for each box_decomposition `i`.
docstr_tok: ["This","returns","the","non-dominated","set",".","Note",":","Internally",",","we","store","the","negative","pareto","set","(","minimization",")",".","Returns",":","A","list","where","the","ith","element","is","the","`","n_pareto_i","x","m","`","-dim","tensor","of","pareto","optimal","outcomes","for","each","box_decomposition","`","i","`","."]
code: def pareto_Y(self) -> List[Tensor]:
      r"""This returns the non-dominated set.
  
      Note: Internally, we store the negative pareto set (minimization).
  
      Returns:
          A list where the ith element is the `n_pareto_i x m`-dim tensor
              of pareto optimal outcomes for each box_decomposition `i`.
      """
      return [p.pareto_Y for p in self.box_decompositions]
  
file_path: botorch/botorch/utils/multi_objective/box_decompositions/box_decomposition_list.py
imports: from __future__ import annotations
  from typing import List, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.utils.multi_objective.box_decompositions.box_decomposition import BoxDecomposition
  from torch import Tensor
  from torch.nn import Module, ModuleList
label_desc: Use image node_id 2 for calling the BoxDecompositionList obj's underlying member method code with example usage: obj.pareto_Y() and returns: unknown