class_docstr: Base class for test functions.
class_docstr_tok: ["Base","class","for","test","functions","."]
docstr: Base constructor for test functions.
  
  Args:
      noise_std: Standard deviation of the observation noise. If a list is
          provided, specifies separate noise standard deviations for each
          objective in a multiobjective problem.
      negate: If True, negate the function.
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code: def __init__(
      self,
      noise_std: Union[None, float, List[float]] = None,
      negate: bool = False,
  ) -> None:
      r"""Base constructor for test functions.
  
      Args:
          noise_std: Standard deviation of the observation noise. If a list is
              provided, specifies separate noise standard deviations for each
              objective in a multiobjective problem.
          negate: If True, negate the function.
      """
      super().__init__()
      self.noise_std = noise_std
      self.negate = negate
      if len(self._bounds) != self.dim:
          raise InputDataError(
              "Expected the bounds to match the dimensionality of the domain. "
              f"Got {self.dim=} and {len(self._bounds)=}."
          )
      self.register_buffer(
          "bounds",
          torch.tensor(self._bounds, dtype=torch.double).transpose(
              -1, -2
          ),
      )
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import List, Tuple, Union
  import torch
  from botorch.exceptions.errors import InputDataError
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 to create a new BaseTestProblem object from inherited base classes: Module, ABC with example: obj = BaseTestProblem(noise_std, negate)