class_docstr: KRNN Model
  
  Parameters
  ----------
  d_feat : int
      input dimension for each time step
  metric: str
      the evaluation metric used in early stop
  optimizer : str
      optimizer name
  GPU : str
      the GPU ID(s) 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", data_key=DataHandlerLP.DK_I
      )
      index = x_test.index
      self.krnn_model.eval()
      x_values = x_test.values
      sample_num = x_values.shape[0]
      preds = []
  
      for begin in range(sample_num)[:: self.batch_size]:
          if sample_num - begin < self.batch_size:
              end = sample_num
          else:
              end = begin + self.batch_size
          x_batch = (
              torch.from_numpy(x_values[begin:end])
              .float()
              .to(self.device)
          )
          with torch.no_grad():
              pred = self.krnn_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 ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 10 for calling the KRNN obj's underlying member method code with example usage: obj.predict(dataset, segment) and returns: pd