class_docstr: A default early stopping implementation which supports stopping based on thresholds
  or based on patience-based improvement.
  
  .. versionadded:: 1.1.0
class_docstr_tok: ["A","default","early","stopping","implementation","which","supports","stopping","based","on","thresholds","or","based","on","patience-based","improvement",".","..","versionadded",":",":","1.1.0"]
code: def _validate_arguments(self, metric, min_delta, patience, threshold):
      if min_delta < 0:
          raise ValueError(
              "Invalid value encountered: "
              '"min_delta" needs to be greater than zero.'
          )
  
      if patience < 0 and threshold <= 0:
          raise ValueError(
              "Invalid configuration encountered: "
              'Either "patience" or "threshold" must be enabled.'
          )
  
      if "_acc" in metric.name and (threshold < 0.0 or threshold > 1.0):
          raise ValueError(
              "Invalid value encountered: "
              '"threshold" needs to be within the interval [0, 1] '
              "for accuracy metrics."
          )
  
imports: import logging
  import numpy
  from abc import ABC
label_desc: Use image node_id 2 for calling the EarlyStopping obj's underlying member method code with example usage: obj._validate_arguments(metric, min_delta, patience, threshold) without return types