class_docstr: Optimizer that implements the gradient descent algorithm.
    
class_docstr_tok: ["Optimizer","that","implements","the","gradient","descent","algorithm","."]
docstr: 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".
  
  @compatibility(eager)
  When eager execution is enabled, `learning_rate` can be a callable that
  takes no arguments and returns the actual value to use. This can be useful
  for changing these values across different invocations of optimizer
  functions.
  @end_compatibility
docstr_tok: ["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","''",".","@","compatibility","(","eager",")","When","eager","execution","is","enabled",",","`","learning_rate","`","can","be","a","callable","that","takes","no","arguments","and","returns","the","actual","value","to","use",".","This","can","be","useful","for","changing","these","values","across","different","invocations","of","optimizer","functions",".","@","end_compatibility"]
code: 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".
  
      @compatibility(eager)
      When eager execution is enabled, `learning_rate` can be a callable that
      takes no arguments and returns the actual value to use. This can be useful
      for changing these values across different invocations of optimizer
      functions.
      @end_compatibility
      """
      super(GradientDescentOptimizer, self).__init__(use_locking, name)
      self._learning_rate = learning_rate
      self._learning_rate_tensor = None
  
file_path: tensorflow-upstream/tensorflow/python/training/gradient_descent.py
imports: from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new GradientDescentOptimizer object from inherited base classes: optimizer with example: obj = GradientDescentOptimizer(learning_rate, use_locking, name)