code: def forward(
      self, samples: Tensor, X: Optional[Tensor] = None
  ) -> Tensor:
      prepared_samples = self._prepare_samples(samples)
      return prepared_samples.sum(dim=-1)
  
imports: from typing import Optional
  import torch
  from botorch.acquisition.objective import LinearMCObjective
  from botorch.acquisition.risk_measures import CVaR, Expectation, RiskMeasureMCObjective, VaR, WorstCase
  from botorch.utils.testing import BotorchTestCase
  from torch import Tensor
label_desc: Use image node_id 1 for calling the NotSoAbstractRiskMeasure obj's underlying member method code with example usage: obj.forward(samples, X) and returns: prepared_samples