class_docstr: An implementation of Ftrl https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf.
  Support L1 penalty, L2 penalty and shrinkage-type L2 penalty.
  
  :param learningrate learning rate
  :param learningrate_power double, must be less or equal to zero. Default is -0.5.
  :param initial_accumulator_value double, the starting value for accumulators,
      require zero or positive values.
  :param l1_regularization_strength double, must be greater or equal to zero. Default is zero.
  :param l2_regularization_strength double, must be greater or equal to zero. Default is zero.
  :param l2_shrinkage_regularization_strength double, must be greater or equal to zero.
      Default is zero. This differs from l2RegularizationStrength above. L2 above is a
      stabilization penalty, whereas this one is a magnitude penalty.
  >>> ftrl = Ftrl()
  creating: createFtrl
  >>> ftrl2 = Ftrl(1e-2, -0.1, 0.2, 0.3, 0.4, 0.5)
  creating: createFtrl
code: def get_optimizer(self):
      return self.optimizer
  
imports: from abc import ABC, abstractmethod
  from bigdl.util.common import DOUBLEMAX
  from zoo.orca.learn.optimizers.schedule import Scheduler
label_desc: Use image node_id 2 for calling the Ftrl obj's underlying member method code with example usage: obj.get_optimizer() and returns: self