docstr: Args:
      sample_shape: The `sample_shape` of the samples to generate.
      seed: The seed for the RNG. If omitted, use a random seed.
      max_num_comparisons:  Max number of comparisons drawn within samples.
          If None, use all possible pairwise comparisons.
      kwargs: Catch-all for deprecated arguments.
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code: def __init__(
      self,
      sample_shape: torch.Size,
      seed: Optional[int] = None,
      max_num_comparisons: int = None,
      **kwargs: Any,
  ) -> None:
      r"""
      Args:
          sample_shape: The `sample_shape` of the samples to generate.
          seed: The seed for the RNG. If omitted, use a random seed.
          max_num_comparisons:  Max number of comparisons drawn within samples.
              If None, use all possible pairwise comparisons.
          kwargs: Catch-all for deprecated arguments.
      """
      PairwiseMCSampler.__init__(
          self, max_num_comparisons=max_num_comparisons, seed=seed
      )
      SobolQMCNormalSampler.__init__(
          self, sample_shape=sample_shape, seed=seed, **kwargs
      )
  
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 PairwiseSobolQMCNormalSampler object from inherited base classes: PairwiseMCSampler, SobolQMCNormalSampler with example: obj = PairwiseSobolQMCNormalSampler(sample_shape, seed, max_num_comparisons)