class_docstr: Base class for evaluating optimization convergence.
  
  Stopping criteria are implemented as a objects rather than a function, so that they
  can keep track of past function values between optimization steps.
  
  :meta private:
docstr: Evaluate the stopping criterion.
  
  Args:
      fvals: tensor containing function values for the current iteration. If
          `fvals` contains more than one element, then the stopping criterion is
          evaluated element-wise and True is returned if the stopping criterion is
          true for all elements.
  
  Returns:
      Stopping indicator (if True, stop the optimziation).
code: def evaluate(self, fvals: Tensor) -> bool:
      r"""Evaluate the stopping criterion.
  
      Args:
          fvals: tensor containing function values for the current iteration. If
              `fvals` contains more than one element, then the stopping criterion is
              evaluated element-wise and True is returned if the stopping criterion is
              true for all elements.
  
      Returns:
          Stopping indicator (if True, stop the optimziation).
      """
      pass
  
file_path: botorch/botorch/optim/stopping.py
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling the StoppingCriterion obj's underlying member method code with example usage: obj.evaluate(fvals) without return types