class_docstr: Line search algorithm which iteratively optimizes the value $f(x)$ for $x$ on the line between
  $x'$ and $x_0$ by optimistically taking the first acceptable $x$ starting from $x_0$ and
  moving towards $x'$.
class_docstr_tok: ["Line","search","algorithm","which","iteratively","optimizes","the","value","$","f","(","x",")","$","for","$","x","$","on","the","line","between","$","x","'","$","and","$","x_0","$","by","optimistically","taking","the","first","acceptable","$","x","$","starting","from","$","x_0","$","and","moving","towards","$","x","'","$","."]
docstr: 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 $x = x'$.
      zero_value: Value $f(x_0)$ at $x = x_0$.
  
  Returns:
      Initial arguments for step.
docstr_tok: ["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","$","x","=","x","'","$",".","zero_value",":","Value","$","f","(","x_0",")","$","at","$","x","=","x_0","$",".","Returns",":","Initial","arguments","for","step","."]
code: def start(self, *, arguments, x_init, base_value, zero_value):
      """
      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 $x = x'$.
          zero_value: Value $f(x_0)$ at $x = x_0$.
  
      Returns:
          Initial arguments for step.
      """
      one_float = tf_util.constant(value=1.0, dtype="float")
      backtracking_factor = self.backtracking_factor.value()
      deltas = x_init.fmap(
          function=(lambda t: t * (backtracking_factor - one_float))
      )
  
      last_improvement = base_value - zero_value
  
      target_value = self.fn_x(arguments, deltas)
      improvement = base_value - target_value
  
      return (
          arguments,
          x_init,
          deltas,
          improvement,
          last_improvement,
          base_value,
      )
  
file_path: tensorforce/tensorforce/core/optimizers/solvers/line_search.py
imports: import tensorflow
  from tensorforce import util
  from tensorforce.core import parameter_modules, SignatureDict, TensorSpec, tf_function, tf_util
  from tensorforce.core.optimizers.solvers import Iterative
label_desc: Use image node_id 5 for calling the LineSearch obj's underlying member method code with example usage: obj.start() and returns: arguments,  x_init,  deltas,  improvement,  last_improvement,  base_value