class_docstr: Base (Stochastic) gradient descent optimizer
  
  Parameters
  ----------
  learning_rate_init : float, default=0.1
      The initial learning rate used. It controls the step-size in updating
      the weights
  
  Attributes
  ----------
  learning_rate : float
      the current learning rate
docstr: Decides whether it is time to stop training
  
  Parameters
  ----------
  msg : str
      Message passed in for verbose output
  
  verbose : bool
      Print message to stdin if True
  
  Returns
  -------
  is_stopping : bool
      True if training needs to stop
docstr_tok: ["Decides","whether","it","is","time","to","stop","training","Parameters","--","--","--","--","--","msg",":","str","Message","passed","in","for","verbose","output","verbose",":","bool","Print","message","to","stdin","if","True","Returns","--","--","--","-","is_stopping",":","bool","True","if","training","needs","to","stop"]
code: def trigger_stopping(self, msg, verbose):
      """Decides whether it is time to stop training
  
      Parameters
      ----------
      msg : str
          Message passed in for verbose output
  
      verbose : bool
          Print message to stdin if True
  
      Returns
      -------
      is_stopping : bool
          True if training needs to stop
      """
      if verbose:
          print(msg + " Stopping.")
      return True
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/neural_network/_stochastic_optimizers.py
imports: import numpy
label_desc: Use image node_id 4 for calling the BaseOptimizer obj's underlying member method code with example usage: obj.trigger_stopping(msg, verbose) and returns: True