class_docstr: MVaR Approximation based on Random Scalarizations as introduced
  in [Daulton2022MARS]_.
  
  This approximates MVaR via VaR of Chebyshev scalarizations, where each
  scalarization corresponds to a point in the MVaR set. As implemented,
  this uses one set of scalarization weights to approximate a single MVaR value.
  Note that due to the normalization within the Chebyshev scalarization,
  the output of this risk measure may not be on the same scale as its inputs.
docstr: Update the baseline outcomes.
  
  Invalidates the cached Chebyshev objective.
  
  Args:
      baseline_Y: An `n' x d`-dim tensor of baseline outcomes to use in
          determining the normalization bounds for Chebyshev scalarization.
          It is recommended to set this via `set_baseline_Y` helper.
docstr_tok: ["Update","the","baseline","outcomes",".","Invalidates","the","cached","Chebyshev","objective",".","Args",":","baseline_Y",":","An","`","n","'","x","d","`","-dim","tensor","of","baseline","outcomes","to","use","in","determining","the","normalization","bounds","for","Chebyshev","scalarization",".","It","is","recommended","to","set","this","via","`","set_baseline_Y","`","helper","."]
code: def baseline_Y(self, baseline_Y: Optional[Tensor]) -> None:
      r"""Update the baseline outcomes.
  
      Invalidates the cached Chebyshev objective.
  
      Args:
          baseline_Y: An `n' x d`-dim tensor of baseline outcomes to use in
              determining the normalization bounds for Chebyshev scalarization.
              It is recommended to set this via `set_baseline_Y` helper.
      """
      self._chebyshev_objective = None
      self.register_buffer("_baseline_Y", baseline_Y)
  
imports: import warnings
  from abc import ABC, abstractmethod
  from math import ceil
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.acquisition.multi_objective.objective import IdentityMCMultiOutputObjective, MCMultiOutputObjective
  from botorch.acquisition.risk_measures import CVaR, RiskMeasureMCObjective, VaR
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models.model import Model
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from botorch.utils.transforms import normalize
  from torch import Tensor
label_desc: Use image node_id 6 for calling the MARS obj's underlying member method code with example usage: obj.baseline_Y(baseline_Y) without return types