class_docstr: Early stop the experiment when a metric plateaued across trials.
  
  Stops the entire experiment when the metric has plateaued
  for more than the given amount of iterations specified in
  the patience parameter.
  
  Args:
      metric: The metric to be monitored.
      std: The minimal standard deviation after which
          the tuning process has to stop.
      top: The number of best models to consider.
      mode: The mode to select the top results.
          Can either be "min" or "max".
      patience: Number of epochs to wait for
          a change in the top models.
  
  Raises:
      ValueError: If the mode parameter is not "min" nor "max".
      ValueError: If the top parameter is not an integer
          greater than 1.
      ValueError: If the standard deviation parameter is not
          a strictly positive float.
      ValueError: If the patience parameter is not
          a strictly positive integer.
docstr: Return whether to stop and prevent trials from starting.
docstr_tok: ["Return","whether","to","stop","and","prevent","trials","from","starting","."]
code: def stop_all(self):
      """Return whether to stop and prevent trials from starting."""
      return self.has_plateaued() and self._iterations >= self._patience
  
file_path: ray/python/ray/tune/stopper/experiment_plateau.py
imports: import numpy
  from ray.util.annotations import PublicAPI
  from ray.tune.stopper.stopper import Stopper
label_desc: Use image node_id 4 for calling the ExperimentPlateauStopper obj's underlying member method code with example usage: obj.stop_all() and returns: unknown