input: Write a python function to solve a linear programming problem using the given constraints and objective function.
  assert solve_linear_program() == ("Optimal", -1.0, 2.0, 0.0)
  assert solve_linear_program() == ("Optimal", -1.0, 2.0, 0.0)
output: from scipy.optimize import linprog
  
  def solve_linear_program():
      # Coefficients of the objective function
      c = [-1, -2, 0]
  
      # Coefficients of the inequality constraints
      A = [[-1, 1, 1], [1, 2, 3]]
      b = [20, 40]
  
      # Coefficients of the equality constraints
      A_eq = [[0, 1, -1]]
      b_eq = [10]
  
      # Bounds for variables
      x_bounds = (0, None)
      y_bounds = (0, None)
      z_bounds = (0, None)
  
      result = linprog(c, A_ub=A, b_ub=b, A_eq=A_eq, b_eq=b_eq, bounds=[x_bounds, y_bounds, z_bounds], method='highs')
  
      if result.success:
          return ("Optimal", result.fun, result.x[0], result.x[1], result.x[2])
      else:
          return ("Infeasible", None, None, None, None)