code: def __getitem__(self, idx):
      start_idx = self.roll_start_idxes[idx]
  
      # cal x
      x = self.arr[start_idx : start_idx + self.lookback]
      x = torch.from_numpy(x).float()
      if self.is_predict is True and not self.time_enc:
          return x
  
      # cal y
      arr_target_only = self.arr[:, : self.target_num]
      if isinstance(self.horizon, int):
          y = arr_target_only[
              start_idx
              + self.lookback
              - self.label_len : start_idx
              + self.lookback
              + self.horizon
          ]
      else:
          # horizon is a list of int
          horizons = np.array(self.horizon)
          y = np.take(
              arr_target_only,
              horizons + start_idx + self.lookback - 1,
              axis=0,
          )
      y = torch.from_numpy(y).float()
  
      if self.time_enc:
          # cal x_enc
          x_enc = self.data_stamp_arr[
              start_idx : start_idx + self.lookback
          ]
          x_enc = torch.from_numpy(x_enc).float()
          # cal y_enc
          y_enc = self.data_stamp_arr[
              start_idx
              + self.lookback
              - self.label_len : start_idx
              + self.lookback
              + self.horizon_time
          ]
          y_enc = torch.from_numpy(y_enc).float()
  
      if self.time_enc:
          return x, y, x_enc, y_enc
      else:
          return x, y
  
file_path: BigDL/python/chronos/src/bigdl/chronos/data/utils/roll_dataset.py
imports: import numpy
  from torch.utils.data import Dataset
  import torch
  import pandas
  from bigdl.chronos.data.utils.utils import _check_cols_no_na, _to_list
  from bigdl.chronos.data.utils.time_feature import time_features, gen_time_enc_arr
label_desc: Use image node_id 3 for calling the RollDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: x, x,  y,  x_enc,  y_enc, x,  y