class_docstr: A no-operation early stopping handler which never stops. This is for developer
  convenience only, you will likely not need this in an application setting.
  
  .. versionadded:: 1.1.0
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docstr: Checks if the training should be stopped early. The decision is made based on
  the masured values of one or more quantitative metrics over time.
  
  Parameters
  ----------
  epoch : int
      The number of the current epoch (1-indexed). Multiple checks per epoch are allowed.
  measured_values : dict of str to float
      A dictionary of measured values.
docstr_tok: ["Checks","if","the","training","should","be","stopped","early",".","The","decision","is","made","based","on","the","masured","values","of","one","or","more","quantitative","metrics","over","time",".","Parameters","--","--","--","--","--","epoch",":","int","The","number","of","the","current","epoch","(","1-indexed",")",".","Multiple","checks","per","epoch","are","allowed",".","measured_values",":","dict","of","str","to","float","A","dictionary","of","measured","values","."]
code: def check_early_stop(self, epoch, measured_values):
      """Checks if the training should be stopped early. The decision is made based on
      the masured values of one or more quantitative metrics over time.
  
      Parameters
      ----------
      epoch : int
          The number of the current epoch (1-indexed). Multiple checks per epoch are allowed.
      measured_values : dict of str to float
          A dictionary of measured values.
      """
      _unused = epoch, measured_values  # noqa:F841
      return False
  
imports: import logging
  import numpy
  from abc import ABC
label_desc: Use image node_id 1 for calling the NoopEarlyStopping obj's underlying member method code with example usage: obj.check_early_stop(epoch, measured_values) and returns: False