new_contents: import pygsp
  import numpy as np
  from pygsp import graphs, reduction, data_handling
  
  G = graphs.Sensor(512, distribute=True)
  
  g = [lambda x: 5./(5 + x)]
  
  Gs = reduction.kron_pyramid(G, 5, epsilon=0.1)
  graphs.gutils.compute_fourier_basis(Gs)
  graphs.gutils.estimate_lmax(Gs)
  
  f = np.ones((G.N))
  f[np.arange(G.N/2)] = -1
  f = f + 10*Gs[0].U[:, 7]
  
  f2 = np.ones((G.N, 2))
  f2[np.arange(G.N/2)] = -1
  
  ca, pe = reduction.pyramid_analysis(Gs, f, filters=g, verbose=False)
  ca2, pe2 = reduction.pyramid_analysis(Gs, f2, filters=g, verbose=False)
  
  coeff = data_handling.pyramid_cell2coeff(ca, pe)
  coeff2 = data_handling.pyramid_cell2coeff(ca2, pe2)
  
  f_pred, _ = reduction.pyramid_synthesis(Gs, coeff, verbose=False)
  f_pred2, _ = reduction.pyramid_synthesis(Gs, coeff2, verbose=False)
  
  err = np.linalg.norm(f_pred-f)/np.linalg.norm(f)
  err2 = np.linalg.norm(f_pred2-f2)/np.linalg.norm(f2)
  print('erreur de f (1d) : {}'.format(err))
  print('erreur de f2 (2d) : {}'.format(err2))
  
subject: add demo_pyramid (need to be commented)