code: def __init__(
      self,
      d_feat=6,
      hidden_size=128,
      num_layers=1,
      dropout=0.5,
      base_model="GRU",
  ):
      super().__init__()
      self.base_model = base_model
      if base_model == "GRU":
          self.rnn = nn.GRU(
              input_size=d_feat,
              hidden_size=hidden_size,
              num_layers=num_layers,
              batch_first=True,
              dropout=dropout,
          )
      elif base_model == "LSTM":
          self.rnn = nn.LSTM(
              input_size=d_feat,
              hidden_size=hidden_size,
              num_layers=num_layers,
              batch_first=True,
              dropout=dropout,
          )
      else:
          raise ValueError("unknown base model name `%s`" % base_model)
  
      self.fc = nn.Linear(hidden_size, d_feat)
  
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 Decoder object from inherited base classes: nn with example: obj = Decoder(d_feat, hidden_size, num_layers, dropout, base_model)