code: def forward(self, X):
      X = self.laplacian @ self.Theta1(self.dropout(X))
      X = F.relu(X)
      X = self.laplacian @ self.Theta2(self.dropout(X))
      return X
  
file_path: dgl/examples/sparse/hgnn.py
imports: import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import CoraGraphDataset
  from torchmetrics.functional import accuracy
label_desc: Use image node_id 2 for calling the HGNN obj's underlying member method code with example usage: obj.forward(X) and returns: X