def tf_step(self, x, iteration, deltas, improvement, last_improvement, estimated_improvement):
x, next_iteration, deltas, improvement, last_improvement, estimated_improvement = super(LineSearch, self).tf_step(<EOL>x, iteration, deltas, improvement, last_improvement, estimated_improvement<EOL>)<EOL>next_x = [t + delta for t, delta in zip(x, deltas)]<EOL>if self.mode == '<STR_LIT>':<EOL><INDENT>next_deltas = deltas<EOL>next_estimated_improvement = estimated_improvement + self.estimated_incr<EOL><DEDENT>elif self.mode == '<STR_LIT>':<EOL><INDENT>next_deltas = [delta * self.parameter for delta in deltas]<EOL>next_estimated_improvement = estimated_improvement * self.parameter<EOL><DEDENT>target_value = self.fn_x(next_deltas)<EOL>next_improvement = tf.divide(<EOL>x=(target_value - self.base_value),<EOL>y=tf.maximum(x=next_estimated_improvement, y=util.epsilon)<EOL>)<EOL>return next_x, next_iteration, next_deltas, next_improvement, improvement, next_estimated_improvement<EOL>
Iteration loop body of the line search algorithm.

Args:
    x: Current solution estimate $x_t$.
    iteration: Current iteration counter $t$.
    deltas: Current difference $x_t - x'$.
    improvement: Current improvement $(f(x_t) - f(x')) / v'$.
    last_improvement: Last improvement $(f(x_{t-1}) - f(x')) / v'$.
    estimated_improvement: Current estimated value $v'$.

Returns:
    Updated arguments for next iteration.