input: from typing import List
  
  def solve_linear_programming_problem_simplex_method(decision_vars: int, objective_func: List[float], constraints: List[List[float]], rhs_constraints: List[float]) -> List[float]:
      """ Solve a linear programming problem using the Simplex Method.
      Given the number of decision variables, the objective function coefficients, the constraint coefficients,
      and the right-hand side values of the constraints, this function returns the optimal values of the decision variables.
      
      >>> solve_linear_programming_problem_simplex_method(2, [3, 2], [[1, 1], [2, 1]], [4, 5])
      [2.0, 2.0]
      """
output:     from scipy.optimize import linprog
  
      # Convert the objective function to a minimization problem
      c = [-x for x in objective_func]
  
      # Convert the constraints to the format required by linprog
      A = constraints
      b = rhs_constraints
  
      # Solve the linear programming problem using the Simplex Method
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Extract the optimal values of the decision variables
      optimal_values = result.x
  
      return optimal_values