code: def _roll_tensor(
      self, data, lookback: int, target_feature_index: List[int]
  ):
      data = data[:, target_feature_index]
      data = torch.unsqueeze(data, 1)
      roll_data = torch.cat(
          [self._shift(data, i) for i in range(0, -lookback, -1)], dim=1
      )
      window_idx = torch.arange(lookback)
      if data.size()[0] >= lookback:
          roll_data = roll_data[
              : data.size()[0] - lookback + 1, window_idx, :
          ]
      else:
          roll_data = roll_data[:0, window_idx, :]
      return roll_data
  
imports: import torch
  import torch.nn
  from typing import List
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
  from .utils import _to_list
label_desc: Use image node_id 4 for calling the ExportJIT obj's underlying member method code with example usage: obj._roll_tensor(data, lookback, target_feature_index) and returns: roll_data