class_docstr: A class for Tensorflow specific optimizer logic.
  
  The major behavior change for this class is for tf.distribute.
  
  It will override methods from base Keras core Optimizer,
  which provide distribute specific functionality, e.g. variable
  creation, loss reduction, etc.
class_docstr_tok: ["A","class","for","Tensorflow","specific","optimizer","logic",".","The","major","behavior","change","for","this","class","is","for","tf.distribute",".","It","will","override","methods","from","base","Keras","core","Optimizer",",","which","provide","distribute","specific","functionality",",","e.g",".","variable","creation",",","loss","reduction",",","etc","."]
code: def _distributed_tf_update_step(
      self, distribution, grads_and_vars, learning_rate
  ):
      def apply_grad_to_update_var(var, grad):
          return self.update_step(grad, var, learning_rate)
  
      for grad, var in grads_and_vars:
          distribution.extended.update(
              var, apply_grad_to_update_var, args=(grad,), group=False
          )
  
file_path: keras/keras/backend/tensorflow/optimizer.py
imports: import tensorflow
  from keras import backend
  from keras.backend.common import KerasVariable
  from keras.optimizers import base_optimizer
label_desc: Use image node_id 9 for calling the TFOptimizer obj's underlying member method code with example usage: obj._distributed_tf_update_step(distribution, grads_and_vars, learning_rate) and returns: self