class_docstr: Optimizer that implements the proximal gradient descent algorithm.
  
  References:
    Efficient Learning using Forward-Backward Splitting:
      [Duchi et al., 2009](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting)
      ([pdf](http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf))
class_docstr_tok: ["Optimizer","that","implements","the","proximal","gradient","descent","algorithm",".","References",":","Efficient","Learning","using","Forward-Backward","Splitting",":","[","Duchi","et","al.",",","2009","]","(","http",":","\/\/papers.nips.cc\/paper\/3793-efficient-learning-using-forward-backward-splitting",")","(","[","pdf","]","(","http",":","\/\/papers.nips.cc\/paper\/3793-efficient-learning-using-forward-backward-splitting.pdf",")",")"]
code: def _prepare(self):
      self._learning_rate_tensor = ops.convert_to_tensor(
          self._learning_rate, name="learning_rate"
      )
      self._l1_regularization_strength_tensor = ops.convert_to_tensor(
          self._l1_regularization_strength,
          name="l1_regularization_strength",
      )
      self._l2_regularization_strength_tensor = ops.convert_to_tensor(
          self._l2_regularization_strength,
          name="l2_regularization_strength",
      )
  
file_path: tensorflow-upstream/tensorflow/python/training/proximal_gradient_descent.py
imports: from tensorflow.python.framework import ops
  from tensorflow.python.ops import gen_training_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import optimizer
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 6 for calling the ProximalGradientDescentOptimizer obj's underlying member method code with example usage: obj._prepare() without return types