class_docstr: Abstract class for Pairwise MC Sampler.
  
  This sampler will sample pairwise comparisons. It is to be used together
  with PairwiseGP and BoTorch acquisition functions (e.g., qKnowledgeGradient)
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docstr: Args:
      max_num_comparisons: Max number of comparisons drawn within samples.
          If None, use all possible pairwise comparisons
      seed: The seed for np.random.seed. If omitted, use a random seed.
          May be overwritten by sibling classes or subclasses.
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code: def __init__(
      self, max_num_comparisons: int = None, seed: int = None
  ) -> None:
      r"""
      Args:
          max_num_comparisons: Max number of comparisons drawn within samples.
              If None, use all possible pairwise comparisons
          seed: The seed for np.random.seed. If omitted, use a random seed.
              May be overwritten by sibling classes or subclasses.
      """
      self.max_num_comparisons = max_num_comparisons
      self.seed = (
          seed
          if seed is not None
          else torch.randint(0, 1000000, (1,)).item()
      )
  
imports: from __future__ import annotations
  from itertools import combinations
  from typing import Any, Optional
  import numpy
  import torch
  from botorch.posteriors.posterior import Posterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
  from torch import Tensor
label_desc: Use image node_id 1 to create a new PairwiseMCSampler object from inherited base classes: MCSampler with example: obj = PairwiseMCSampler(max_num_comparisons, seed)