class_docstr: Base (Stochastic) gradient descent optimizer
  
  Parameters
  ----------
  params : list, length = len(coefs_) + len(intercepts_)
      The concatenated list containing coefs_ and intercepts_ in MLP model.
      Used for initializing velocities and updating params
  
  learning_rate_init : float, optional, 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
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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
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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/py2/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