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: Description
  -----------
  Propagation forward.
  
  Parameters
  ----------
  graph : DGLGraph
      The graph.
  Y : torch.Tensor
      The feature under propagation. Corresponds to :math:`Z^{(k)}` in eq.28 in the paper.
  X : torch.Tensor
      The original feature. Corresponds to :math:`Z^{(0)}` in eq.28 in the paper.
  alp : float
      The step size. Corresponds to :math:`\alpha` in the paper.
  lam : torch.Tensor
      The coefficient of smoothing term. Corresponds to :math:`\lambda` in the paper.
  Returns
  -------
  torch.Tensor
      Propagated feature. :math:`Z^{(k+1)}` in eq.28 in the paper.
docstr_tok: ["Description","--","--","--","--","--","-","Propagation","forward",".","Parameters","--","--","--","--","--","graph",":","DGLGraph","The","graph",".","Y",":","torch.Tensor","The","feature","under","propagation",".","Corresponds","to",":","math",":","`","Z^","{","(","k",")","}","`","in","eq.28","in","the","paper",".","X",":","torch.Tensor","The","original","feature",".","Corresponds","to",":","math",":","`","Z^","{","(","0",")","}","`","in","eq.28","in","the","paper",".","alp",":","float","The","step","size",".","Corresponds","to",":","math",":","`","\\alpha","`","in","the","paper",".","lam",":","torch.Tensor","The","coefficient","of","smoothing","term",".","Corresponds","to",":","math",":","`","\\lambda","`","in","the","paper",".","Returns","--","--","--","-","torch.Tensor","Propagated","feature",".",":","math",":","`","Z^","{","(","k+1",")","}","`","in","eq.28","in","the","paper","."]
code: def forward(self, graph, Y, X, alp, lam):
      r"""
  
      Description
      -----------
      Propagation forward.
  
      Parameters
      ----------
      graph : DGLGraph
          The graph.
      Y : torch.Tensor
          The feature under propagation. Corresponds to :math:`Z^{(k)}` in eq.28 in the paper.
      X : torch.Tensor
          The original feature. Corresponds to :math:`Z^{(0)}` in eq.28 in the paper.
      alp : float
          The step size. Corresponds to :math:`\alpha` in the paper.
      lam : torch.Tensor
          The coefficient of smoothing term. Corresponds to :math:`\lambda` in the paper.
      Returns
      -------
      torch.Tensor
          Propagated feature. :math:`Z^{(k+1)}` in eq.28 in the paper.
      """
  
      return (
          (1 - alp) * Y
          + alp * lam * self._prop(graph, Y, lam)
          + alp * D_power_bias_X(graph, X, -1, lam, 1 - lam)
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ....None import function
label_desc: Use image node_id 3 for calling the Propagate obj's underlying member method code with example usage: obj.forward(graph, Y, X, alp, lam) and returns: unknown