class_docstr: Ftrl Proximal.
  
  The Ftrl-proximal algorithm, abbreviated for Follow-the-regularized-leader,
  is described in the paper below.
  
  It can give a good performance vs. sparsity tradeoff.
  
  Ftrl-proximal uses its own global base learning rate and can behave like
  Adagrad with `learning_rate_power=-0.5`, or like gradient descent with
  `learning_rate_power=0.0`.
  
  Examples:
      ```python
      # With TFLearn estimators.
      ftrl = Ftrl(learning_rate=0.01, learning_rate_power=-0.1)
      regression = regression(net, optimizer=ftrl)
  
      # Without TFLearn estimators (returns tf.Optimizer).
      ftrl = Ftrl(learning_rate=0.01).get_tensor()
      ```
  
  Arguments:
      learning_rate: `float`. Learning rate.
      learning_rate_power: `float`. Must be less or equal to zero.
      initial_accumulator_value: `float`. The starting value for accumulators.
          Only positive values are allowed.
      l1_regularization_strength: `float`. Must be less or equal to zero.
      l2_regularization_strength: `float`. Must be less or equal to zero.
      use_locking: `bool`. If True use locks for update operation.
      name: `str`. Optional name prefix for the operations created when
          applying gradients. Defaults to "Ftrl".
  
  Links:
      [Ad Click Prediction: a View from the Trenches](https://www.eecs.tufts.
      edu/~dsculley/papers/ad-click-prediction.pdf)
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code: def build(self, step_tensor=None):
      self.built = True
      with tf.device("/cpu:0"):
          self.tensor = tf.train.FtrlOptimizer(
              self.learning_rate,
              learning_rate_power=self.learning_rate_power,
              initial_accumulator_value=self.initial_accumulator_value,
              l1_regularization_strength=self.l1_regularization_strength,
              l2_regularization_strength=self.l2_regularization_strength,
              use_locking=self.use_locking,
              name=self.name,
          )
  
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 2 for calling the Ftrl obj's underlying member method code with example usage: obj.build(step_tensor) without return types