class_docstr: GATs 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
class_docstr_tok: ["GATs","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"
  ):
      if not self.fitted:
          raise ValueError("model is not fitted yet!")
  
      x_test = dataset.prepare(segment, col_set="feature")
      index = x_test.index
      self.GAT_model.eval()
      x_values = x_test.values
      preds = []
  
      # organize the data into daily batches
      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.GAT_model(x_batch).detach().cpu().numpy()
  
          preds.append(pred)
  
      return pd.Series(np.concatenate(preds), index=index)
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...contrib.model.pytorch_lstm import LSTMModel
  from ...contrib.model.pytorch_gru import GRUModel
label_desc: Use image node_id 10 for calling the GATs obj's underlying member method code with example usage: obj.predict(dataset, segment) and returns: pd