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: Update parameters with given gradients
  
  Parameters
  ----------
  params : list of length = len(coefs_) + len(intercepts_)
      The concatenated list containing coefs_ and intercepts_ in MLP
      model. Used for initializing velocities and updating params
  
  grads : list of length = len(params)
      Containing gradients with respect to coefs_ and intercepts_ in MLP
      model. So length should be aligned with params
docstr_tok: ["Update","parameters","with","given","gradients","Parameters","--","--","--","--","--","params",":","list","of","length","=","len","(","coefs_",")","+","len","(","intercepts_",")","The","concatenated","list","containing","coefs_","and","intercepts_","in","MLP","model",".","Used","for","initializing","velocities","and","updating","params","grads",":","list","of","length","=","len","(","params",")","Containing","gradients","with","respect","to","coefs_","and","intercepts_","in","MLP","model",".","So","length","should","be","aligned","with","params"]
code: def update_params(self, params, grads):
      """Update parameters with given gradients
  
      Parameters
      ----------
      params : list of length = len(coefs_) + len(intercepts_)
          The concatenated list containing coefs_ and intercepts_ in MLP
          model. Used for initializing velocities and updating params
  
      grads : list of length = len(params)
          Containing gradients with respect to coefs_ and intercepts_ in MLP
          model. So length should be aligned with params
      """
      updates = self._get_updates(grads)
      for param, update in zip((p for p in params), updates):
          param += update
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/neural_network/_stochastic_optimizers.py
imports: import numpy
label_desc: Use image node_id 2 for calling the BaseOptimizer obj's underlying member method code with example usage: obj.update_params(params, grads) without return types