code: def forward(self, A, X):
      # Compute Y = Y0 = f(X; W) using a two-layer MLP.
      Y = Y0 = self.mlp(X)
  
      # Compute diagonal matrix D_tild.
      I = dglsp.identity(A.shape, device=A.device)
      D_tild = self.lam * dglsp.diag(A.sum(1)) + I
  
      # Iteratively compute new Y by equation (6) in the paper.
      for k in range(self.num_steps):
          Y_hat = self.lam * A @ Y + Y0
          # The inverse of a diagonal matrix inverses its diagonal values.
          Y = (1 - self.alpha) * Y + self.alpha * (D_tild**-1) @ Y_hat
  
      # Apply a linear layer on the final output.
      return self.linear_out(Y)
  
imports: import argparse
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data import CoraGraphDataset
  from torch.optim import Adam
label_desc: Use image node_id 2 for calling the TWIRLS obj's underlying member method code with example usage: obj.forward(A, X) and returns: self