input: 
  def solve_linear_programming_problem(objective_function: List[float], constraints: List[List[float]], rhs: List[float]) -> float:
      """
      Explain the approach for solving a linear programming problem using the Simplex method in Python.
      The function takes as input the coefficients of the objective function, the coefficients of the constraints, and the right-hand side values of the constraints.
      It returns the optimal value of the objective function.
  
      >>> solve_linear_programming_problem([2, 3], [[1, 0], [0, 1]], [4, 6])
      20
      >>> solve_linear_programming_problem([3, -5], [[2, -1], [1, -5]], [8, 5])
      15
      """
  
output:     from scipy.optimize import linprog
  
      # Convert the objective function to a minimization problem
      objective_function = [-x for x in objective_function]
  
      # Solve the linear programming problem using the Simplex method
      result = linprog(objective_function, A_ub=constraints, b_ub=rhs, method='simplex')
  
      # Return the optimal value of the objective function (negate it back)
      return -result.fun