class_docstr: Gaussian penalty class to be added to any arbitrary acquisition function
  to construct a PenalizedAcquisitionFunction.
docstr: Initializing Gaussian regularization.
  
  Args:
      init_point: The "1 x dim" reference point against which
          we want to regularize.
      sigma: The parameter used in gaussian function.
docstr_tok: ["Initializing","Gaussian","regularization",".","Args",":","init_point",":","The","``","1","x","dim","''","reference","point","against","which","we","want","to","regularize",".","sigma",":","The","parameter","used","in","gaussian","function","."]
code: def __init__(self, init_point: Tensor, sigma: float):
      r"""Initializing Gaussian regularization.
  
      Args:
          init_point: The "1 x dim" reference point against which
              we want to regularize.
          sigma: The parameter used in gaussian function.
      """
      super().__init__()
      self.init_point = init_point
      self.sigma = sigma
  
imports: from __future__ import annotations
  import math
  from typing import Any, Callable, List, Optional
  import torch
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.analytic import AnalyticAcquisitionFunction
  from botorch.acquisition.objective import GenericMCObjective
  from botorch.exceptions import UnsupportedError
  from torch import Tensor
label_desc: Use image node_id 1 to create a new GaussianPenalty object from inherited base classes: torch.nn with example: obj = GaussianPenalty(init_point, sigma)