class_docstr: The Knowledge Gradient with one shot optimization.
  
  Args:
      cost_intercept: The cost intercept for the affine cost of the form
          `cost_intercept + n`, where `n` is the number of generated points.
          Only used for multi-fidelity optimzation (i.e., if fidelity_features
          are present).
      linear_truncated: If `False`, use an alternate downsampling + exponential
          decay Kernel instead of the default `LinearTruncatedFidelityKernel`
          (only relevant for multi-fidelity optimization).
      kwargs: Model-specific kwargs.
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docstr: Computes the value of the current best point. This is the current_value
  passed to KG.
  
  NOTE: The current value is computed as the current value of the 'best point
  acquisition function' (typically `PosteriorMean` or `qSimpleRegret`), not of
  the Knowledge Gradient acquisition function.
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code: def _get_current_value(
      self,
      model: Model,
      search_space_digest: SearchSpaceDigest,
      torch_opt_config: TorchOptConfig,
      X_observed: Tensor,
      seed_inner: Optional[int],
      qmc: bool,
  ) -> Tensor:
      r"""Computes the value of the current best point. This is the current_value
      passed to KG.
  
      NOTE: The current value is computed as the current value of the 'best point
      acquisition function' (typically `PosteriorMean` or `qSimpleRegret`), not of
      the Knowledge Gradient acquisition function.
      """
      target_fidelities = {
          k: v
          for k, v in search_space_digest.target_values.items()
          if k in search_space_digest.fidelity_features
      }
      (
          best_point_acqf,
          non_fixed_idcs,
      ) = get_out_of_sample_best_point_acqf(
          model=model,
          Xs=self.Xs,
          objective_weights=torch_opt_config.objective_weights,
          outcome_constraints=torch_opt_config.outcome_constraints,
          X_observed=X_observed,
          seed_inner=seed_inner,
          fixed_features=torch_opt_config.fixed_features,
          fidelity_features=self.fidelity_features,
          target_fidelities=target_fidelities,
          qmc=qmc,
      )
  
      # solution from previous iteration
      recommended_point = self.best_point(
          search_space_digest=search_space_digest,
          torch_opt_config=torch_opt_config,
      )
      # pyre-fixme[16]: `Optional` has no attribute `detach`.
      recommended_point = recommended_point.detach().unsqueeze(0)
      # ensure correct device (`best_point` always returns a CPU tensor)
      recommended_point = recommended_point.to(device=self.device)
      # Extract acquisition value (TODO: Make this less painful and repetitive)
      if non_fixed_idcs is not None:
          recommended_point = recommended_point[..., non_fixed_idcs]
      current_value = best_point_acqf(recommended_point).max()
      return current_value
  
imports: import dataclasses
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.botorch import BotorchModel, get_rounding_func
  from ax.models.torch.botorch_defaults import recommend_best_out_of_sample_point
  from ax.models.torch.utils import _get_X_pending_and_observed, _to_inequality_constraints, get_botorch_objective_and_transform, get_out_of_sample_best_point_acqf, subset_model
  from ax.models.torch_base import TorchGenResults, TorchOptConfig
  from ax.utils.common.typeutils import not_none
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.cost_aware import InverseCostWeightedUtility
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient, qMultiFidelityKnowledgeGradient
  from botorch.acquisition.objective import MCAcquisitionObjective, PosteriorTransform
  from botorch.acquisition.utils import expand_trace_observations, project_to_target_fidelity
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.cost import AffineFidelityCostModel
  from botorch.models.model import Model
  from botorch.optim.initializers import gen_one_shot_kg_initial_conditions
  from botorch.optim.optimize import optimize_acqf
  from botorch.sampling.normal import IIDNormalSampler, SobolQMCNormalSampler
  from torch import Tensor
label_desc: Use image node_id 4 for calling the KnowledgeGradient obj's underlying member method code with example usage: obj._get_current_value(model, search_space_digest, torch_opt_config, X_observed, seed_inner, qmc) and returns: current_value