class_docstr: Description
  -----------
  The propagation method which is without pre-conditioning and reparameterizing and using
  normalized laplacian.
  Correspond to eq.30 in the paper.
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docstr: Description
  -----------
  Propagation forward.
  
  Parameters
  ----------
  graph : DGLGraph
      The graph.
  Y : torch.Tensor
      The feature under propagation. Corresponds to :math:`Y^{(k)}` in eq.30 in the paper.
  X : torch.Tensor
      The original feature. Corresponds to :math:`Y^{(0)}` in eq.30 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:`Y^{(k+1)}` in eq.30 in the paper.
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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:`Y^{(k)}` in eq.30 in the paper.
      X : torch.Tensor
          The original feature. Corresponds to :math:`Y^{(0)}` in eq.30 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:`Y^{(k+1)}` in eq.30 in the paper.
      """
  
      return (
          (1 - alp * lam - alp) * Y
          + alp * lam * normalized_AX(graph, Y)
          + alp * X
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ....None import function
label_desc: Use image node_id 2 for calling the PropagateNoPrecond obj's underlying member method code with example usage: obj.forward(graph, Y, X, alp, lam) and returns: unknown