class_docstr: Description
  -----------
  The propagation method which is with pre-conditioning and reparameterizing. Correspond to
  eq.28 in the paper.
class_docstr_tok: ["Description","--","--","--","--","--","-","The","propagation","method","which","is","with","pre-conditioning","and","reparameterizing",".","Correspond","to","eq.28","in","the","paper","."]
docstr: propagation part.
docstr_tok: ["propagation","part","."]
code: def _prop(self, graph, Y, lam):
      """propagation part."""
      Y = D_power_bias_X(graph, Y, -0.5, lam, 1 - lam)
      Y = AX(graph, Y)
      Y = D_power_bias_X(graph, Y, -0.5, lam, 1 - lam)
  
      return Y
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ....None import function
label_desc: Use image node_id 2 for calling the Propagate obj's underlying member method code with example usage: obj._prop(graph, Y, lam) and returns: Y