class_docstr: A Mixin class that adds q-subset index computations and caching.
class_docstr_tok: ["A","Mixin","class","that","adds","q-subset","index","computations","and","caching","."]
docstr: Initializes the class with q_out = -1 and an empty q_subset_indices dict.
docstr_tok: ["Initializes","the","class","with","q_out","=","-1","and","an","empty","q_subset_indices","dict","."]
code: def __init__(self):
      """Initializes the class with q_out = -1 and an empty q_subset_indices dict."""
      self.q_out: int = -1
      self.q_subset_indices: BufferDict[str, Tensor] = BufferDict()
  
imports: from __future__ import annotations
  import warnings
  from copy import deepcopy
  from itertools import combinations
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.acquisition.cached_cholesky import CachedCholeskyMCSamplerMixin
  from botorch.acquisition.multi_objective.objective import MCMultiOutputObjective
  from botorch.acquisition.multi_objective.utils import prune_inferior_points_multi_objective
  from botorch.exceptions.errors import BotorchError, BotorchTensorDimensionError, UnsupportedError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models.model import Model
  from botorch.sampling.base import MCSampler
  from botorch.utils.multi_objective.box_decompositions.box_decomposition_list import BoxDecompositionList
  from botorch.utils.multi_objective.box_decompositions.dominated import DominatedPartitioning
  from botorch.utils.multi_objective.box_decompositions.non_dominated import FastNondominatedPartitioning, NondominatedPartitioning
  from botorch.utils.multi_objective.box_decompositions.utils import _pad_batch_pareto_frontier
  from botorch.utils.objective import compute_feasibility_indicator
  from botorch.utils.torch import BufferDict
  from torch import Tensor
label_desc: Use image node_id 1 to create a new SubsetIndexCachingMixin object  with example: obj = SubsetIndexCachingMixin()