def tf_step(self, time, variables, **kwargs):
deltas = self.optimizer.step(time=time, variables=variables, **kwargs)<EOL>with tf.control_dependencies(control_inputs=deltas):<EOL><INDENT>clipped_deltas = list()<EOL>exceeding_deltas = list()<EOL>for delta in deltas:<EOL><INDENT>clipped_delta = tf.clip_by_value(<EOL>t=delta,<EOL>clip_value_min=-self.clipping_value,<EOL>clip_value_max=self.clipping_value<EOL>)<EOL>clipped_deltas.append(clipped_delta)<EOL>exceeding_deltas.append(clipped_delta - delta)<EOL><DEDENT><DEDENT>applied = self.apply_step(variables=variables, deltas=exceeding_deltas)<EOL>with tf.control_dependencies(control_inputs=(applied,)):<EOL><INDENT>return [delta + <NUM_LIT:0.0> for delta in clipped_deltas]<EOL><DEDENT>
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.