code: def predict(self, dataset):
      if not self.fitted:
          raise ValueError("model is not fitted yet!")
  
      dl_test = dataset.prepare(
          "test",
          col_set=["feature", "label"],
          data_key=DataHandlerLP.DK_I,
      )
      dl_test.config(fillna_type="ffill+bfill")
      test_loader = DataLoader(
          dl_test, batch_size=self.batch_size, num_workers=self.n_jobs
      )
      self.model.eval()
      preds = []
  
      for data in test_loader:
          feature = data[:, :, 0:-1].to(self.device)
  
          with torch.no_grad():
              pred = self.model(feature.float()).detach().cpu().numpy()
  
          preds.append(pred)
  
      return pd.Series(np.concatenate(preds), index=dl_test.get_index())
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 9 for calling the TransformerModel obj's underlying member method code with example usage: obj.predict(dataset) and returns: pd