text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """GradientDescent for TensorFlow."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import 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.training import training_ops
  
  
  class GradientDescentOptimizer(optimizer.Optimizer):
    """Optimizer that implements the gradient descent algorithm.
  
    @@__init__
    """
  
    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
  
    def _apply_dense(self, grad, var):
      return training_ops.apply_gradient_descent(
          var,
          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
          grad,
          use_locking=self._use_locking).op
  
    def _resource_apply_dense(self, grad, handle):
      return training_ops.resource_apply_gradient_descent(
          handle.handle, math_ops.cast(self._learning_rate_tensor,
                                       grad.dtype.base_dtype),
          grad, use_locking=self._use_locking)
  
    def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices):
      return resource_variable_ops.resource_scatter_add(
          handle.handle, indices, -grad * self._learning_rate)
  
    def _apply_sparse_duplicate_indices(self, grad, var):
      delta = ops.IndexedSlices(
          grad.values *
          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
          grad.indices, grad.dense_shape)
      return var.scatter_sub(delta, use_locking=self._use_locking)
  
    def _prepare(self):
      self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
                                                         name="learning_rate")
  