input: from typing import List, Dict
  
  def solve_linear_programming_problem(decision_variables: List[str], objective_function: Dict[str, float], constraints: List[Dict[str, float]]) -> Dict[str, float]:
      """ Solve a linear programming problem using the Simplex Method in Python.
      This function takes a list of decision variables, an objective function, and a list of constraints. It converts the problem into standard form, sets up the initial tableau, and iteratively applies the Simplex algorithm to find the optimal solution.
  
      >>> solve_linear_programming_problem(['x1', 'x2'], {'x1': 3, 'x2': 2}, [{'x1': 1, 'x2': 2, 'constant': 10}, {'x1': -1, 'x2': 1, 'constant': 0}])
      {'x1': 2.0, 'x2': 4.0}
      >>> solve_linear_programming_problem(['x1', 'x2'], {'x1': 2, 'x2': -1}, [{'x1': 1, 'x2': -2, 'constant': 4}, {'x1': 2, 'x2': -1, 'constant': 8}])
      {'x1': 4.0, 'x2': 0.0}
      """
output:     
      from scipy.optimize import linprog
  
      # Extract the coefficients of the objective function
      c = [objective_function[var] for var in decision_variables]
  
      # Extract the coefficients of the constraints
      A = []
      b = []
      for constraint in constraints:
          A.append([constraint[var] for var in decision_variables])
          b.append(constraint['constant'])
  
      # Solve the linear programming problem using scipy's linprog
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Extract the optimal values of the decision variables
      solution = {var: result.x[i] for i, var in enumerate(decision_variables)}
  
      return solution
  