class_docstr: Batch Knowledge Gradient using one-shot optimization.
  
  This computes the batch Knowledge Gradient using fantasies for the outer
  expectation and either the model posterior mean or MC-sampling for the inner
  expectation.
  
  In addition to the design variables, the input `X` also includes variables
  for the optimal designs for each of the fantasy models. For a fixed number
  of fantasies, all parts of `X` can be optimized in a "one-shot" fashion.
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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).
docstr_tok: ["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",")","."]
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).
      """
      return q + self.num_fantasies
  
imports: from __future__ import annotations
  from copy import deepcopy
  from typing import Any, Callable, Dict, Optional, Tuple, Type
  import torch
  from botorch import settings
  from botorch.acquisition.acquisition import AcquisitionFunction, MCSamplerMixin, OneShotAcquisitionFunction
  from botorch.acquisition.analytic import PosteriorMean
  from botorch.acquisition.cost_aware import CostAwareUtility
  from botorch.acquisition.monte_carlo import MCAcquisitionFunction, qSimpleRegret
  from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  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, match_batch_shape, t_batch_mode_transform
  from torch import Tensor
label_desc: Use image node_id 4 for calling the qKnowledgeGradient obj's underlying member method code with example usage: obj.get_augmented_q_batch_size(q) and returns: unknown