parameters: (self, learning_rate, variance_parameter, num_workers=1,
                 use_locking=False, name="DelayCompensatedGradientDescent")
docstring: Construct a new gradient descent optimizer with delay compensation.
  
      Args:
        learning_rate: A Tensor or a floating point value.  The learning
          rate to use.
        variance_parameter: A Tensor or a floating point value. The lambda
          value to use.
        num_workers: A value to indicate number of workers computing gradients
          asynchronously.
        use_locking: If True use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients. Defaults to "DelayCompensatedGradientDescent".
docstring_summary: Construct a new gradient descent optimizer with delay compensation.
docstring_tokens: Construct a new gradient descent optimizer with delay compensation .
function: def __init__(self, learning_rate, variance_parameter, num_workers=1,
                 use_locking=False, name="DelayCompensatedGradientDescent"):
      """Construct a new gradient descent optimizer with delay compensation.
  
      Args:
        learning_rate: A Tensor or a floating point value.  The learning
          rate to use.
        variance_parameter: A Tensor or a floating point value. The lambda
          value to use.
        num_workers: A value to indicate number of workers computing gradients
          asynchronously.
        use_locking: If True use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients. Defaults to "DelayCompensatedGradientDescent".
        """
      if num_workers <= 0:
        raise ValueError("num_workers must be positive: %s" % num_workers)
      super(DelayCompensatedGradientDescentOptimizer, self).__init__(
            use_locking, name)
      self._learning_rate = learning_rate
      self._lambda = variance_parameter
      self._num_workers = num_workers
function_tokens: def __init__ ( self , learning_rate , variance_parameter , num_workers = 1 , use_locking = False , name = "DelayCompensatedGradientDescent" ) : if num_workers <= 0 : raise ValueError ( "num_workers must be positive: %s" % num_workers ) super ( DelayCompensatedGradientDescentOptimizer , self ) . __init__ ( use_locking , name ) self . _learning_rate = learning_rate self . _lambda = variance_parameter self . _num_workers = num_workers