input: Write a python function to maximize a linear function given certain constraints using the Simplex algorithm.
  assert maximize_linear_function([(1,1)], [(1, 0, 1), (0, 1, 1)], (1, 1)) == (1, 1)
  assert maximize_linear_function([(2, 3)], [(1, 0, 4), (0, 1, 6)], (1, 1)) == (0, 6)
  assert maximize_linear_function([(3, 2)], [(2, 1, 14), (4, 3, 35)], (1, 1)) == (7, 0)
output: def maximize_linear_function(c, A_b, x_bounds):
      from scipy.optimize import linprog
      import numpy as np
  
      c = np.array(c[0])
      A = np.array([row[:-1] for row in A_b])
      b = np.array([row[-1] for row in A_b])
      x_bounds = [(0, bound) for bound in x_bounds]
  
      res = linprog(-c, A_ub=A, b_ub=b, bounds=x_bounds, method='simplex')
      
      return tuple(res.x)