class_docstr: Base class for test functions with constraints.
  
  In addition to one or more objectives, a problem may have a number of outcome
  constraints of the form `c_i(x) >= 0` for `i=1, ..., n_c`.
  
  This base class provides common functionality for such problems.
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docstr: Evaluate the constraint slack (w/o observation noise) on a set of points.
  
  Args:
      X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
          constraint slacks: `c_1(X), ...., c_{n_c}(X)`.
  
  Returns:
      A `batch_shape x n_c`-dim tensor of constraint slack (where positive slack
          corresponds to the constraint being feasible).
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code: def evaluate_slack_true(self, X: Tensor) -> Tensor:
      r"""Evaluate the constraint slack (w/o observation noise) on a set of points.
  
      Args:
          X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
              constraint slacks: `c_1(X), ...., c_{n_c}(X)`.
  
      Returns:
          A `batch_shape x n_c`-dim tensor of constraint slack (where positive slack
              corresponds to the constraint being feasible).
      """
      pass
  
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 3 for calling the ConstrainedBaseTestProblem obj's underlying member method code with example usage: obj.evaluate_slack_true(X) without return types