class_docstr: Variance of difference for Active Learning
  
  Given a model and an objective, calculate the posterior sample variance
  of the objective on the difference of pairs of points. See more implementation
  details in `forward`. This acquisition function is typically used with a
  pairwise model (e.g., PairwiseGP) and a likelihood/link function
  on the pair difference (e.g., logistic or probit) for pure exploration
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docstr: Pairwise Monte Carlo Posterior Variance
  
  Args:
      model: A fitted model.
      objective: An MCAcquisitionObjective representing the link function
          (e.g., logistic or probit.) applied on the difference of (usually 1-d)
          two samples. Can be implemented via GenericMCObjective.
      sampler: The sampler used for drawing MC samples.
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code: def __init__(
      self,
      model: Model,
      objective: MCAcquisitionObjective,
      sampler: Optional[MCSampler] = None,
  ) -> None:
      r"""Pairwise Monte Carlo Posterior Variance
  
      Args:
          model: A fitted model.
          objective: An MCAcquisitionObjective representing the link function
              (e.g., logistic or probit.) applied on the difference of (usually 1-d)
              two samples. Can be implemented via GenericMCObjective.
          sampler: The sampler used for drawing MC samples.
      """
      super().__init__(
          model=model,
          sampler=sampler,
          objective=objective,
          X_pending=None,
      )
  
file_path: botorch/botorch/acquisition/active_learning.py
imports: from __future__ import annotations
  from typing import Optional
  import torch
  from botorch import settings
  from botorch.acquisition.analytic import AnalyticAcquisitionFunction
  from botorch.acquisition.monte_carlo import MCAcquisitionFunction
  from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
  from botorch.models.model import Model
  from botorch.sampling.base import MCSampler
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
  from torch import Tensor
label_desc: Use image node_id 1 to create a new PairwiseMCPosteriorVariance object from inherited base classes: MCAcquisitionFunction with example: obj = PairwiseMCPosteriorVariance(model, objective, sampler)