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: Prepare samples for VaR computation by applying the Chebyshev scalarization
  and separating out the q-batch dimension.
  
  Args:
      samples: A `sample_shape x batch_shape x (q * n_w) x m`-dim tensor of
          posterior samples. The q-batches should be ordered so that each
          `n_w` block of samples correspond to the same input.
  
  Returns:
      A `sample_shape x batch_shape x q x n_w`-dim tensor of prepared samples.
docstr_tok: ["Prepare","samples","for","VaR","computation","by","applying","the","Chebyshev","scalarization","and","separating","out","the","q-batch","dimension",".","Args",":","samples",":","A","`","sample_shape","x","batch_shape","x","(","q","*","n_w",")","x","m","`","-dim","tensor","of","posterior","samples",".","The","q-batches","should","be","ordered","so","that","each","`","n_w","`","block","of","samples","correspond","to","the","same","input",".","Returns",":","A","`","sample_shape","x","batch_shape","x","q","x","n_w","`","-dim","tensor","of","prepared","samples","."]
code: def _prepare_samples(self, samples: Tensor) -> Tensor:
      r"""Prepare samples for VaR computation by applying the Chebyshev scalarization
      and separating out the q-batch dimension.
  
      Args:
          samples: A `sample_shape x batch_shape x (q * n_w) x m`-dim tensor of
              posterior samples. The q-batches should be ordered so that each
              `n_w` block of samples correspond to the same input.
  
      Returns:
          A `sample_shape x batch_shape x q x n_w`-dim tensor of prepared samples.
      """
      samples = self.chebyshev_objective(samples)
      return samples.view(*samples.shape[:-1], -1, self.n_w)
  
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 9 for calling the MARS obj's underlying member method code with example usage: obj._prepare_samples(samples) and returns: samples