code: def forward(
      self, samples: Tensor, X: Optional[Tensor] = None
  ) -> Tensor:
      return self.objective(
          self.apply_feasibility_weights(samples), X=X
      )
  
imports: from __future__ import annotations
  import warnings
  from abc import abstractmethod
  from typing import List, Optional
  import torch
  from botorch.acquisition.objective import GenericMCObjective, MCAcquisitionObjective
  from botorch.exceptions.errors import BotorchError, BotorchTensorDimensionError
  from botorch.models.model import Model
  from botorch.posteriors import GPyTorchPosterior
  from botorch.utils import apply_constraints
  from botorch.utils.transforms import normalize_indices
  from torch import Tensor
label_desc: Use image node_id 2 for calling the FeasibilityWeightedMCMultiOutputObjective obj's underlying member method code with example usage: obj.forward(samples, X) and returns: self