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 predict(
      self, dataset: DatasetH, segment: Union[Text, slice] = "test"
  ):
      x_test = dataset.prepare(
          segment, col_set="feature", data_key=DataHandlerLP.DK_I
      )
      index = x_test.index
      self.ADD_model.eval()
      x_values = x_test.values
      preds = []
  
      daily_index, daily_count = self.get_daily_inter(
          x_test, shuffle=False
      )
  
      for idx, count in zip(daily_index, daily_count):
          batch = slice(idx, idx + count)
          x_batch = (
              torch.from_numpy(x_values[batch]).float().to(self.device)
          )
  
          with torch.no_grad():
              pred = self.ADD_model(x_batch)
              pred = pred["excess"].detach().cpu().numpy()
  
          preds.append(pred)
  
      r = pd.Series(np.concatenate(preds), index=index)
      return r
  
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 20 for calling the ADD obj's underlying member method code with example usage: obj.predict(dataset, segment) and returns: r