code: def forward(self, g, is_training=True):
      labels = g.ndata.pop("a")
  
      input_digits = self.digit_embed(g.ndata.pop("q"))
      rows = self.row_embed(g.ndata.pop("row"))
      cols = self.col_embed(g.ndata.pop("col"))
  
      x = self.input_layer(torch.cat([input_digits, rows, cols], -1))
      g.ndata["x"] = x
      g.ndata["h"] = x
      g.ndata["rnn_h"] = torch.zeros_like(x, dtype=torch.float)
      g.ndata["rnn_c"] = torch.zeros_like(x, dtype=torch.float)
  
      outputs = self.rrn(g, is_training)
      logits = self.output_layer(outputs)
  
      preds = torch.argmax(logits, -1)
  
      if is_training:
          labels = torch.stack([labels] * self.num_steps, 0)
      logits = logits.view([-1, 10])
      labels = labels.view([-1])
      loss = self.loss_func(logits, labels)
      return preds, loss
  
file_path: dgl/examples/pytorch/rrn/sudoku.py
imports: import torch
  from rrn import RRN
  from torch import nn
label_desc: Use image node_id 2 for calling the SudokuNN obj's underlying member method code with example usage: obj.forward(g, is_training) and returns: preds,  loss