parameters: (self, learning_rate, use_locking=False, name="GradientDescent")
docstring: Construct a new gradient descent optimizer.
  
      Args:
        learning_rate: A Tensor or a floating point value.  The learning
          rate to use.
        use_locking: If True use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients. Defaults to "GradientDescent".
docstring_summary: Construct a new gradient descent optimizer.
docstring_tokens: Construct a new gradient descent optimizer .
function: def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
      """Construct a new gradient descent optimizer.
  
      Args:
        learning_rate: A Tensor or a floating point value.  The learning
          rate to use.
        use_locking: If True use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients. Defaults to "GradientDescent".
      """
      super(GradientDescentOptimizer, self).__init__(use_locking, name)
      self._learning_rate = learning_rate
function_tokens: def __init__ ( self , learning_rate , use_locking = False , name = "GradientDescent" ) : super ( GradientDescentOptimizer , self ) . __init__ ( use_locking , name ) self . _learning_rate = learning_rate