class_docstr: Exponential moving average.
  
  This class is a checkpointable implementation of a subset of the functionality
  provided by tf.train.ExponentialMovingAverage. The tf version is not
  checkpointable due to use of tf.Variable.ref() to associate tf.Variables
  objects to their corresponding averages
  (cf. https://github.com/tensorflow/tensorflow/issues/38452). This version uses
  the order of the tf.Variable objects in a sequence to associate the variables
  with their averages.
  
  Note: This class offers less functionality than the tensorflow version and it
  is only implemented for replica context.
  
  Attributes:
    averaged_variables: A sequence of tf.Variables that stores the averages for
      the variables. They are associated to the new values that are provided to
      ExponentialMovingAverage.apply() by the order in the sequence. If None a
      call to ExponentialMovingAverage.apply() initializes the variable before
      applying the update.
docstr: Initializes exponential moving average.
  
  Args:
    decay: The decay rate of the exponential moving average.
docstr_tok: ["Initializes","exponential","moving","average",".","Args",":","decay",":","The","decay","rate","of","the","exponential","moving","average","."]
code: def __init__(self, decay: float = 0.999):
      """Initializes exponential moving average.
  
      Args:
        decay: The decay rate of the exponential moving average.
      """
      self.averaged_variables: Sequence[tf.Variable] = None
      self._decay = decay
  
imports: from typing import Sequence
  import tensorflow
label_desc: Use image node_id 1 to create a new ExponentialMovingAverage object from inherited base classes: tf with example: obj = ExponentialMovingAverage(decay)