class_docstr: A wrapper around AcquisitionFunctions to fix a subset of features.
  
  Example:
      >>> model = SingleTaskGP(train_X, train_Y)  # d = 5
      >>> qEI = qExpectedImprovement(model, best_f=0.0)
      >>> columns = [2, 4]
      >>> values = X[..., columns]
      >>> qEI_FF = FixedFeatureAcquisitionFunction(qEI, 5, columns, values)
      >>> qei = qEI_FF(test_X)  # d' = 3
docstr: Sets the `X_pending` of the base acquisition function.
docstr_tok: ["Sets","the","`","X_pending","`","of","the","base","acquisition","function","."]
code: def X_pending(self, X_pending: Optional[Tensor]):
      r"""Sets the `X_pending` of the base acquisition function."""
      if X_pending is not None:
          self.acq_func.X_pending = self._construct_X_full(X_pending)
      else:
          self.acq_func.X_pending = X_pending
  
file_path: botorch/botorch/acquisition/fixed_feature.py
imports: from __future__ import annotations
  from numbers import Number
  from typing import List, Optional, Sequence, Union
  import torch
  from botorch.acquisition.acquisition import AcquisitionFunction
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 4 for calling the FixedFeatureAcquisitionFunction obj's underlying member method code with example usage: obj.X_pending(X_pending) without return types