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.
code: def __init__(
      self,
      metric: str,
      std: float = 0.001,
      top: int = 10,
      mode: str = "min",
      patience: int = 0,
  ):
      if mode not in ("min", "max"):
          raise ValueError(
              "The mode parameter can only be either min or max."
          )
      if not isinstance(top, int) or top <= 1:
          raise ValueError(
              "Top results to consider must be"
              " a positive integer greater than one."
          )
      if not isinstance(patience, int) or patience < 0:
          raise ValueError(
              "Patience must be a strictly positive integer."
          )
      if not isinstance(std, float) or std <= 0:
          raise ValueError(
              "The standard deviation must be a strictly positive float number."
          )
      self._mode = mode
      self._metric = metric
      self._patience = patience
      self._iterations = 0
      self._std = std
      self._top = top
      self._top_values = []
  
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 1 to create a new ExperimentPlateauStopper object from inherited base classes: Stopper with example: obj = ExperimentPlateauStopper(metric, std, top, mode, patience)