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 whether the constraints are feasible on a set of points.
  
  Args:
      X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
          constraints.
      noise: If `True`, add observation noise as specified by `noise_std`.
  
  Returns:
      A `batch_shape`-dim boolean tensor that is `True` iff all constraint
          slacks (potentially including observation noise) are positive.
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code: def is_feasible(self, X: Tensor, noise: bool = True) -> Tensor:
      r"""Evaluate whether the constraints are feasible on a set of points.
  
      Args:
          X: A `batch_shape x d`-dim tensor of point(s) at which to evaluate the
              constraints.
          noise: If `True`, add observation noise as specified by `noise_std`.
  
      Returns:
          A `batch_shape`-dim boolean tensor that is `True` iff all constraint
              slacks (potentially including observation noise) are positive.
      """
      return (self.evaluate_slack(X=X, noise=noise) >= 0.0).all(dim=-1)
  
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 2 for calling the ConstrainedBaseTestProblem obj's underlying member method code with example usage: obj.is_feasible(X, noise) and returns: unknown