class_docstr: MC-based batch Multi-Step Look-Ahead (one-shot optimization).
docstr: Get augmented q batch size for one-shot optimization.
  
  Args:
      q: The number of candidates to consider jointly.
  
  Returns:
      The augmented size for one-shot optimization (including variables
      parameterizing the fantasy solutions): `q_0 + f_1 q_1 + f_2 f_1 q_2 + ...`
code: def get_augmented_q_batch_size(self, q: int) -> int:
      r"""Get augmented q batch size for one-shot optimization.
  
      Args:
          q: The number of candidates to consider jointly.
  
      Returns:
          The augmented size for one-shot optimization (including variables
          parameterizing the fantasy solutions): `q_0 + f_1 q_1 + f_2 f_1 q_2 + ...`
      """
      return q + self._num_auxiliary
  
imports: from __future__ import annotations
  import math
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type
  import numpy
  import torch
  from botorch.acquisition import AcquisitionFunction, OneShotAcquisitionFunction
  from botorch.acquisition.analytic import AnalyticAcquisitionFunction, PosteriorMean
  from botorch.acquisition.monte_carlo import MCAcquisitionFunction
  from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import BotorchWarning
  from botorch.models.model import Model
  from botorch.optim.initializers import initialize_q_batch
  from botorch.sampling.base import MCSampler
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.transforms import match_batch_shape, t_batch_mode_transform, unnormalize
  from torch import Size, Tensor
  from torch.distributions import Beta
  from torch.nn import ModuleList
label_desc: Use image node_id 5 for calling the qMultiStepLookahead obj's underlying member method code with example usage: obj.get_augmented_q_batch_size(q) and returns: unknown