class_docstr: ADD Model
  
  Parameters
  ----------
   lr : float
       learning rate
   d_feat : int
       input dimensions for each time step
   metric : str
       the evaluation metric used in early stop
   optimizer : str
       optimizer name
   GPU : int
       the GPU ID used for training
code: def fit(
      self,
      dataset: DatasetH,
      evals_result=dict(),
      save_path=None,
  ):
      label_train, label_valid = dataset.prepare(
          ["train", "valid"],
          col_set=["label"],
          data_key=DataHandlerLP.DK_R,
      )
      self.fit_thresh(label_train)
      df_train, df_valid = dataset.prepare(
          ["train", "valid"],
          col_set=["feature", "label"],
          data_key=DataHandlerLP.DK_L,
      )
      df_train = self.gen_market_label(df_train, label_train)
      df_valid = self.gen_market_label(df_valid, label_valid)
  
      x_train, y_train, m_train = (
          df_train["feature"],
          df_train["label"],
          df_train["market_return"],
      )
      x_valid, y_valid, m_valid = (
          df_valid["feature"],
          df_valid["label"],
          df_valid["market_return"],
      )
  
      evals_result["train"] = []
      evals_result["valid"] = []
      # load pretrained base_model
  
      if self.base_model == "LSTM":
          pretrained_model = LSTMModel()
      elif self.base_model == "GRU":
          pretrained_model = GRUModel()
      else:
          raise ValueError(
              "unknown base model name `%s`" % self.base_model
          )
  
      if self.model_path is not None:
          self.logger.info("Loading pretrained model...")
          pretrained_model.load_state_dict(
              torch.load(self.model_path, map_location=self.device)
          )
  
          model_dict = self.ADD_model.enc_excess.state_dict()
          pretrained_dict = {
              k: v
              for k, v in pretrained_model.rnn.state_dict().items()
              if k in model_dict
          }
          model_dict.update(pretrained_dict)
          self.ADD_model.enc_excess.load_state_dict(model_dict)
          model_dict = self.ADD_model.enc_market.state_dict()
          pretrained_dict = {
              k: v
              for k, v in pretrained_model.rnn.state_dict().items()
              if k in model_dict
          }
          model_dict.update(pretrained_dict)
          self.ADD_model.enc_market.load_state_dict(model_dict)
          self.logger.info("Loading pretrained model Done...")
  
      self.bootstrap_fit(
          x_train, y_train, m_train, x_valid, y_valid, m_valid
      )
  
      best_param = copy.deepcopy(self.ADD_model.state_dict())
      save_path = get_or_create_path(save_path)
      torch.save(best_param, save_path)
      if self.use_gpu:
          torch.cuda.empty_cache()
  
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 19 for calling the ADD obj's underlying member method code with example usage: obj.fit(dataset, evals_result, save_path) without return types