code: def tf_step(self, time, variables, source_variables, **kwargs):
          
          assert all(util.shape(source) == util.shape(target) for source, target in zip(source_variables, variables))
  
          last_sync = tf.get_variable(
              name='last-sync',
              shape=(),
              dtype=tf.int64,
              initializer=tf.constant_initializer(value=(-self.sync_frequency), dtype=tf.int64),
              trainable=False
          )
  
          def sync():
              deltas = list()
              for source_variable, target_variable in zip(source_variables, variables):
                  delta = self.update_weight * (source_variable - target_variable)
                  deltas.append(delta)
  
              applied = self.apply_step(variables=variables, deltas=deltas)
              last_sync_updated = last_sync.assign(value=time)
  
              with tf.control_dependencies(control_inputs=(applied, last_sync_updated)):
                  # Trivial operation to enforce control dependency
                  return [delta + 0.0 for delta in deltas]
  
          def no_sync():
              deltas = list()
              for variable in variables:
                  delta = tf.zeros(shape=util.shape(variable))
                  deltas.append(delta)
              return deltas
  
          do_sync = (time - last_sync >= self.sync_frequency)
          return tf.cond(pred=do_sync, true_fn=sync, false_fn=no_sync)
docs: Creates the TensorFlow operations for performing an optimization step.
  
          Args:
              time: Time tensor.
              variables: List of variables to optimize.
              source_variables: List of source variables to synchronize with.
              **kwargs: Additional arguments, not used.
  
          Returns:
              List of delta tensors corresponding to the updates for each optimized variable.