code: def tf_initialize(self, x_init, base_value, target_value, estimated_improvement):
          
          self.base_value = base_value
  
          if estimated_improvement is None:  # TODO: Is this a good alternative?
              estimated_improvement = tf.abs(x=base_value)
  
          first_step = super(LineSearch, self).tf_initialize(x_init)
  
          improvement = tf.divide(
              x=(target_value - self.base_value),
              y=tf.maximum(x=estimated_improvement, y=util.epsilon)
          )
  
          last_improvement = improvement - 1.0
  
          if self.mode == 'linear':
              deltas = [-t * self.parameter for t in x_init]
              self.estimated_incr = -estimated_improvement * self.parameter
  
          elif self.mode == 'exponential':
              deltas = [-t * self.parameter for t in x_init]
  
          return first_step + (deltas, improvement, last_improvement, estimated_improvement)
docs: Initialization step preparing the arguments for the first iteration of the loop body.
  
          Args:
              x_init: Initial solution guess $x_0$.
              base_value: Value $f(x')$ at $x = x'$.
              target_value: Value $f(x_0)$ at $x = x_0$.
              estimated_improvement: Estimated value at $x = x_0$, $f(x')$ if None.
  
          Returns:
              Initial arguments for tf_step.