code: def __init__(
      self,
      d_feat=6,
      hidden_size=64,
      num_layers=1,
      dropout=0.0,
      dec_dropout=0.5,
      base_model="GRU",
      gamma=0.1,
      gamma_clip=0.4,
  ):
      super().__init__()
      self.d_feat = d_feat
      self.base_model = base_model
      if base_model == "GRU":
          self.enc_excess, self.enc_market = [
              nn.GRU(
                  input_size=d_feat,
                  hidden_size=hidden_size,
                  num_layers=num_layers,
                  batch_first=True,
                  dropout=dropout,
              )
              for _ in range(2)
          ]
      elif base_model == "LSTM":
          self.enc_excess, self.enc_market = [
              nn.LSTM(
                  input_size=d_feat,
                  hidden_size=hidden_size,
                  num_layers=num_layers,
                  batch_first=True,
                  dropout=dropout,
              )
              for _ in range(2)
          ]
      else:
          raise ValueError("unknown base model name `%s`" % base_model)
      self.dec = Decoder(
          d_feat, 2 * hidden_size, num_layers, dec_dropout, base_model
      )
  
      ctx_size = hidden_size * num_layers
      self.pred_excess, self.adv_excess = [
          nn.Sequential(
              nn.Linear(ctx_size, ctx_size),
              nn.BatchNorm1d(ctx_size),
              nn.Tanh(),
              nn.Linear(ctx_size, 1),
          )
          for _ in range(2)
      ]
      self.adv_market, self.pred_market = [
          nn.Sequential(
              nn.Linear(ctx_size, ctx_size),
              nn.BatchNorm1d(ctx_size),
              nn.Tanh(),
              nn.Linear(ctx_size, 3),
          )
          for _ in range(2)
      ]
      self.before_adv_market, self.before_adv_excess = [
          RevGrad(gamma, gamma_clip) for _ in range(2)
      ]
  
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 1 to create a new ADDModel object from inherited base classes: nn with example: obj = ADDModel(d_feat, hidden_size, num_layers, dropout, dec_dropout, base_model, gamma, gamma_clip)