input: 
  def solve_linear_programming_simplex(coefficients: List[float], constraints: List[List[float]], rhs: List[float], objective: str) -> List[float]:
      """ Solves a linear programming problem using the Simplex Method in Python. 
      The method involves converting the problem into a standard form, setting up a tableau,
      and iteratively moving from a vertex to the next in the polytope until an optimum solution is found.
      The function takes the coefficients of the decision variables, the constraints (as a list of lists), 
      the right-hand side values of the constraints, and the objective ('max' or 'min').
      
      Example:
      >>> solve_linear_programming_simplex([3, 2], [[1, 0], [0, 2], [3, 2]], [4, 12, 18], 'max')
      [6.0, 3.0]
      """
      
  
output:     
      from scipy.optimize import linprog
  
      num_vars = len(coefficients)
      num_constraints = len(constraints)
      
      # Convert the problem to standard form
      if objective == 'max':
          coefficients = [-x for x in coefficients]
      
      # Construct the A_ub matrix and b_ub vector
      A_ub = constraints
      b_ub = rhs
      
      # Solve the problem using scipy's linprog
      result = linprog(coefficients, A_ub=A_ub, b_ub=b_ub, method='simplex')
      
      if result.success:
          return result.x
      else:
          raise ValueError("No feasible solution found.")
  