new_contents: # 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.
  # ==============================================================================
  
  """GradientDescentDC 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 GradientDescentOptimizerDC(optimizer.Optimizer):
    """Optimizer that implements the gradient descent algorithm with delay compensation.
    """
  
    def __init__(self, learning_rate, variance_parameter, use_locking=False, name="GradientDescentDC"):
      """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.
        use_locking: If True use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients. Defaults to "GradientDescentDC".
      """
      super(GradientDescentOptimizerDC, self).__init__(use_locking, name)
      self._learning_rate = learning_rate
      self._lambda = variance_parameter
  
    def _apply_dense(self, grad, var):
      var_bak = var
      return training_ops.apply_gradient_descent(
          var,
          math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
          math_ops.add(grad, math_ops.multiply(math_ops.multiply(grad, math_ops.cast(self._lambda_tensor, grad.dtype.base_dtype)), math_ops.subtract(var, var_bak))),
          use_locking=self._use_locking).op
  
    def _resource_apply_dense(self, grad, handle):
      var_bak = handle.handle
      return training_ops.resource_apply_gradient_descent(
          handle.handle, math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
          math_ops.add(grad, math_ops.multiply(math_ops.multiply(grad, math_ops.cast(self._lambda_tensor, grad.dtype.base_dtype)), math_ops.subtract(handle.handle, var_bak))),
          use_locking=self._use_locking)
  
    def _prepare(self):
      self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
                                                         name="learning_rate")
      self._lambda_tensor = ops.convert_to_tensor(self._lambda,
                                                        name="lambda")
  
subject: Create gradient_descent_dc.py