class_docstr: Abstract base class for acquisition functions using one-shot optimization
  
  :meta private:
docstr: Extract the candidates from a full "one-shot" parameterization.
  
  Args:
      X_full: A `b x q_aug x d`-dim Tensor with `b` t-batches of `q_aug`
          design points each.
  
  Returns:
      A `b x q x d`-dim Tensor with `b` t-batches of `q` design points each.
docstr_tok: ["Extract","the","candidates","from","a","full","``","one-shot","''","parameterization",".","Args",":","X_full",":","A","`","b","x","q_aug","x","d","`","-dim","Tensor","with","`","b","`","t-batches","of","`","q_aug","`","design","points","each",".","Returns",":","A","`","b","x","q","x","d","`","-dim","Tensor","with","`","b","`","t-batches","of","`","q","`","design","points","each","."]
code: def extract_candidates(self, X_full: Tensor) -> Tensor:
      r"""Extract the candidates from a full "one-shot" parameterization.
  
      Args:
          X_full: A `b x q_aug x d`-dim Tensor with `b` t-batches of `q_aug`
              design points each.
  
      Returns:
          A `b x q x d`-dim Tensor with `b` t-batches of `q` design points each.
      """
      pass
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from typing import Optional
  import torch
  from botorch.exceptions import BotorchWarning
  from botorch.models.model import Model, ModelDict
  from botorch.posteriors.posterior import Posterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.get_sampler import get_sampler
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the OneShotAcquisitionFunction obj's underlying member method code with example usage: obj.extract_candidates(X_full) without return types