code: def forward(self, obs):
      continuous_obs = obs[:, 0 : -2 - line_num]
      line_status = torch.as_tensor(
          obs[:, -2 - line_num : -2], dtype=torch.long
      )
      line_status = line_status.reshape(-1, 1)
      month = torch.as_tensor(obs[:, -1], dtype=torch.long)
      hour = torch.as_tensor(obs[:, -2], dtype=torch.long)
  
      month_emb = self.month_embedding(month)
      hour_emb = self.hour_embedding(hour)
      line_status_emb = self.line_status_embedding(line_status)
      line_status_emb = line_status_emb.reshape(-1, 186, 256)
      line_status_emb_sum = torch.sum(line_status_emb, dim=1)
  
      output = torch.cat(
          [continuous_obs, hour_emb, month_emb, line_status_emb_sum],
          dim=1,
      )
  
      output = self.fc1(output)
      output = self.fc2(output)
      output = self.fc3(output)
      output = self.fc4(output)
      output = self.fc5(output)
      output = self.fc6(output)
      return output
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the PowerNetModel obj's underlying member method code with example usage: obj.forward(obs) and returns: output