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
class_docstr_tok: ["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"]
docstr: Perform update to learning rate and potentially other states at the
  end of an iteration
docstr_tok: ["Perform","update","to","learning","rate","and","potentially","other","states","at","the","end","of","an","iteration"]
code: def iteration_ends(self, time_step):
      """Perform update to learning rate and potentially other states at the
      end of an iteration
      """
      pass
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/neural_network/_stochastic_optimizers.py
imports: import numpy
label_desc: Use image node_id 3 for calling the BaseOptimizer obj's underlying member method code with example usage: obj.iteration_ends(time_step) without return types