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 $x'$.
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docstr: Iteratively optimize $f(x)$ for $x$ on the line between $x'$ and $x_0$.
  
  Args:
      x_init: Initial solution guess $x_0$.
      base_value: Value $f(x')$ at $x = x'$.
      zero_value: Value $f(x_0)$ at $x = x_0$.
      fn_x: A callable returning the value $f(x)$ at $x$, with potential side effect.
  
  Returns:
      A solution $x$ to the problem as given by the solver.
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code: def solve(self, *, arguments, x_init, base_value, zero_value, fn_x):
      """
      Iteratively optimize $f(x)$ for $x$ on the line between $x'$ and $x_0$.
  
      Args:
          x_init: Initial solution guess $x_0$.
          base_value: Value $f(x')$ at $x = x'$.
          zero_value: Value $f(x_0)$ at $x = x_0$.
          fn_x: A callable returning the value $f(x)$ at $x$, with potential side effect.
  
      Returns:
          A solution $x$ to the problem as given by the solver.
      """
      return super().solve(
          arguments=arguments,
          x_init=x_init,
          base_value=base_value,
          zero_value=zero_value,
          fn_x=fn_x,
      )
  
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 4 for calling the LineSearch obj's underlying member method code with example usage: obj.solve() and returns: super