input: from typing import List
  
  def solve_linear_programming(decision_vars: List[float], objective_func: List[float], constraints: List[List[float]], rhs: List[float]) -> List[float]:
      """ Solve a linear programming problem using the Simplex method in Python.
      This function takes the decision variables, objective function coefficients, 
      constraints coefficients, and right-hand side values, and returns the 
      optimal solution in terms of the decision variables.
      
      The method involves converting the problem into standard form, setting up 
      the initial tableau, and iteratively applying the Simplex algorithm until 
      an optimal solution is found.
      
      >>> solve_linear_programming([1, 2], [3, 4], [[1, 1], [2, 1]], [10, 15])
      [2.5, 7.5]
      >>> solve_linear_programming([1, 1], [2, 3], [[1, 2], [2, 1]], [20, 20])
      [10, 0]
      """
output:     import numpy as np
  
      def to_standard_form(decision_vars, objective_func, constraints, rhs):
          num_vars = len(decision_vars)
          num_constraints = len(constraints)
          A = np.array(constraints)
          b = np.array(rhs)
          c = np.array(objective_func)
          I = np.eye(num_constraints)
          A_std = np.hstack((A, I))
          c_std = np.hstack((c, np.zeros(num_constraints)))
          return A_std, b, c_std
  
      def initial_tableau(A, b, c):
          m, n = A.shape
          tableau = np.zeros((m + 1, n + m + 1))
          tableau[:-1, :n] = A
          tableau[:-1, n:n+m] = np.eye(m)
          tableau[:-1, -1] = b
          tableau[-1, :n] = c
          tableau[-1, -1] = 0
          return tableau
  
      def pivot_index(tableau):
          m, n = tableau.shape
          pivot_col = np.argmin(tableau[-1, :-1])
          if tableau[-1, pivot_col] >= 0:
              return None, None
          ratios = tableau[:-1, -1] / tableau[:-1, pivot_col]
          ratios[ratios <= 0] = np.inf
          pivot_row = np.argmin(ratios)
          return pivot_row, pivot_col
  
      def pivot(tableau, pivot_row, pivot_col):
          m, n = tableau.shape
          tableau[pivot_row, :] /= tableau[pivot_row, pivot_col]
          for i in range(m):
              if i != pivot_row:
                  tableau[i, :] -= tableau[i, pivot_col] * tableau[pivot_row, :]
  
      def simplex(tableau):
          while True:
              pivot_row, pivot_col = pivot_index(tableau)
              if pivot_row is None:
                  break
              pivot(tableau, pivot_row, pivot_col)
          return tableau
  
      A_std, b, c_std = to_standard_form(decision_vars, objective_func, constraints, rhs)
      tableau = initial_tableau(A_std, b, c_std)
      final_tableau = simplex(tableau)
      optimal_solution = final_tableau[-1, :-1]
      return optimal_solution.tolist()