code: def tf_step(self, time, variables, **kwargs):
          
          deltas = self.optimizer.step(time=time, variables=variables, **kwargs)
  
          with tf.control_dependencies(control_inputs=deltas):
              clipped_deltas = list()
              exceeding_deltas = list()
              for delta in deltas:
                  clipped_delta = tf.clip_by_value(
                      t=delta,
                      clip_value_min=-self.clipping_value,
                      clip_value_max=self.clipping_value
                  )
                  clipped_deltas.append(clipped_delta)
                  exceeding_deltas.append(clipped_delta - delta)
  
          applied = self.apply_step(variables=variables, deltas=exceeding_deltas)
  
          with tf.control_dependencies(control_inputs=(applied,)):
              return [delta + 0.0 for delta in clipped_deltas]
docs: Creates the TensorFlow operations for performing an optimization step.
  
          Args:
              time: Time tensor.
              variables: List of variables to optimize.
              **kwargs: Additional arguments passed on to the internal optimizer.
  
          Returns:
              List of delta tensors corresponding to the updates for each optimized variable.