class_docstr: python reference implemenation of sparse Ftrl optimizer.
  
  Referenced from *Ad Click Prediction: a View from the Trenches*, available at
  http://dl.acm.org/citation.cfm?id=2488200.
  
  Parameters
  ----------
  lamda1 : float, optional
      L1 regularization coefficient.
  learning_rate : float, optional
      The initial learning rate.
  beta : float, optional
      Per-coordinate learning rate correlation parameter.
  eta :
      .. math::
         \eta_{t,i} = \frac{learningrate}{\beta+\sqrt{\sum_{s=1}^tg_{s,i}^t}}
code: def __init__(self, lamda1=0.01, learning_rate=0.1, beta=1, **kwargs):
      super(PySparseFtrl, self).__init__(**kwargs)
      self.lamda1 = lamda1
      self.beta = beta
      self.lr = learning_rate
  
imports: import itertools
  import numpy
  import itertools
  import mxnet
  import mxnet.lr_scheduler
  from mxnet import gluon
  import unittest
  import pytest
  import math
  from mxnet.test_utils import *
  from common import retry, xfail_when_nonstandard_decimal_separator
label_desc: Use image node_id 1 to create a new PySparseFtrl object from inherited base classes: mx.optimizer with example: obj = PySparseFtrl(lamda1, learning_rate, beta)