def tf_step(self, time, variables, **kwargs):
arguments = kwargs["<STR_LIT>"]<EOL>fn_loss = kwargs["<STR_LIT>"]<EOL>loss = fn_loss(**arguments)<EOL>with tf.control_dependencies(control_inputs=(loss,)):<EOL><INDENT>previous_variables = [variable + <NUM_LIT:0.0> for variable in variables]<EOL><DEDENT>with tf.control_dependencies(control_inputs=previous_variables):<EOL><INDENT>applied = self.tf_optimizer.minimize(loss=loss, var_list=variables)  <EOL><DEDENT>with tf.control_dependencies(control_inputs=(applied,)):<EOL><INDENT>return [<EOL>variable - previous_variable<EOL>for variable, previous_variable in zip(variables, previous_variables)<EOL>]<EOL><DEDENT>
Keyword Args:
    arguments: Dict of arguments for passing to fn_loss as **kwargs.
    fn_loss: A callable taking arguments as kwargs and returning the loss op of the current model.