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 __init__(
      self,
      learningrate=1e-3,
      learningrate_power=-0.5,
      initial_accumulator_value=0.1,
      l1_regularization_strength=0.0,
      l2_regularization_strength=0.0,
      l2_shrinkage_regularization_strength=0.0,
  ):
      from bigdl.optim.optimizer import Ftrl as BFtrl
  
      self.optimizer = BFtrl(
          learningrate,
          learningrate_power,
          initial_accumulator_value,
          l1_regularization_strength,
          l2_regularization_strength,
          l2_shrinkage_regularization_strength,
          bigdl_type="float",
      )
  
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 1 to create a new Ftrl object from inherited base classes: Optimizer with example: obj = Ftrl(learningrate, learningrate_power, initial_accumulator_value, l1_regularization_strength, l2_regularization_strength, l2_shrinkage_regularization_strength)