code: def forward(self, x):
      x = x.reshape(len(x), self.d_feat, -1)
      N = x.shape[0]
      T = x.shape[-1]
      x = x.permute(0, 2, 1)
  
      out, hidden_excess = self.enc_excess(x)
      out, hidden_market = self.enc_market(x)
      if self.base_model == "LSTM":
          feature_excess = (
              hidden_excess[0].permute(1, 0, 2).reshape(N, -1)
          )
          feature_market = (
              hidden_market[0].permute(1, 0, 2).reshape(N, -1)
          )
      else:
          feature_excess = hidden_excess.permute(1, 0, 2).reshape(N, -1)
          feature_market = hidden_market.permute(1, 0, 2).reshape(N, -1)
      predicts = {}
      predicts["excess"] = self.pred_excess(feature_excess).squeeze(1)
      predicts["market"] = self.pred_market(feature_market)
      predicts["adv_market"] = self.adv_market(
          self.before_adv_market(feature_excess)
      )
      predicts["adv_excess"] = self.adv_excess(
          self.before_adv_excess(feature_market).squeeze(1)
      )
      if self.base_model == "LSTM":
          hidden = [
              torch.cat([hidden_excess[i], hidden_market[i]], -1)
              for i in range(2)
          ]
      else:
          hidden = torch.cat([hidden_excess, hidden_market], -1)
      x = torch.zeros_like(x[:, 1, :])
      reconstructed_feature = []
      for i in range(T):
          x, hidden = self.dec(x, hidden)
          reconstructed_feature.append(x)
      reconstructed_feature = torch.stack(reconstructed_feature, 1)
      predicts["reconstructed_feature"] = reconstructed_feature
      return predicts
  
imports: from __future__ import division
  from __future__ import print_function
  import copy
  import math
  from typing import Text, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from qlib.contrib.model.pytorch_gru import GRUModel
  from qlib.contrib.model.pytorch_lstm import LSTMModel
  from qlib.contrib.model.pytorch_utils import count_parameters
  from qlib.data.dataset import DatasetH
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import get_or_create_path
  from torch.autograd import Function
label_desc: Use image node_id 2 for calling the ADDModel obj's underlying member method code with example usage: obj.forward(x) and returns: predicts