input: from typing import List
  
  def solve_linear_programming_problem(coefficients: List[float], constraints: List[List[float]], rhs: List[float], obj_type: str) -> List[float]:
      """
      This function solves a linear programming problem using the Simplex method in Python.
      The Simplex method is an iterative algorithm used for finding the maximum or minimum value
      of a linear function subject to linear constraints. This function first sets up the problem
      in a tableau format and then iterates through the vertices of the polytope until the optimal
      solution is found.
      
      >>> solve_linear_programming_problem([3, 2], [[1, 2], [2, 1]], [10, 10], 'max')
      [2.0, 4.0]
      >>> solve_linear_programming_problem([2, 3], [[3, 2], [2, 5]], [12, 15], 'min')
      [3.75, 0.0]
      """
output:     from scipy.optimize import linprog
  
      # Convert the objective function to a minimization problem if it's a maximization problem
      if obj_type == 'max':
          coefficients = [-x for x in coefficients]
  
      # Solve the linear programming problem using scipy's linprog
      result = linprog(coefficients, A_ub=constraints, b_ub=rhs, method='simplex')
  
      # Check if the problem was solved successfully
      if result.success:
          return result.x.tolist()
      else:
          raise ValueError("The linear programming problem could not be solved.")