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 stateless_apply(
      self, optimizer_variables, grads, trainable_variables
  ):
      # This is mainly due to the interaction with tf.distribute.Strategy,
      # which requires tf.Variable as the inputs for most of its APIs.
      raise ValueError(
          "stateless_apply is not supported with the TensorFlow backend "
          "(as it is incompatible with tf.distribute)."
      )
  
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 3 for calling the TFOptimizer obj's underlying member method code with example usage: obj.stateless_apply(optimizer_variables, grads, trainable_variables) without return types