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: The weights used in Chebyshev scalarization.
docstr_tok: ["The","weights","used","in","Chebyshev","scalarization","."]
code: def chebyshev_weights(self) -> Tensor:
      r"""The weights used in Chebyshev scalarization."""
      return self._chebyshev_weights
  
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 3 for calling the MARS obj's underlying member method code with example usage: obj.chebyshev_weights() and returns: self