code: def __getitem__(self, index):
      if isinstance(index, pd.Timestamp) or (
          isinstance(index, List) and isinstance(index[0], pd.Timestamp)
      ):
          index = self.index_by_timestamp(index)
  
      left_pad = right_pad = None
      if isinstance(index, slice):
          if (index.stop is not None) and (index.stop > 0):
              stop = index.stop
              if stop > len(self):
                  right_pad = stop - len(self)
                  stop = None
              start = index.start
              if (index.start is not None) and (index.start < 0):
                  left_pad = -index.start
                  start = None
              index = slice(start, stop)
      target = self.target[index]
      time_index = self.time_index[index]
  
      if isinstance(index, int):
          obj = TimeSeriesInstant(
              target,
              time_index,
              series_name=self.series_name,
              target_names=self.target_names,
          )
          for name in self._categorical_features:
              if name in self._static_features:
                  data = self._features[name]
              else:
                  data = self._features[name][index]
              obj.add_features(name, data, data_type="cat")
          for name in self._numerical_features:
              if name in self._static_features:
                  data = self._features[name]
              else:
                  data = self._features[name][index]
              obj.add_features(name, data, data_type="num")
          return obj
      else:
          if left_pad is not None:
              if self.time_unit is not None:
                  td = pd.Timedelta(1, unit=self.time_unit).to_numpy()
              else:
                  if len(time_index) < 2:
                      raise IndexError(
                          "Cannot pad time index with a single timestamp"
                      )
                  td = time_index[1] - time_index[0]
              time_index = np.r_[
                  np.arange(
                      time_index[0] - td * left_pad, time_index[0], td
                  ),
                  time_index,
              ]
              target = _pad_array_along_axis(
                  target, (left_pad, 0), constant_values=np.nan
              )
          if right_pad is not None:
              if self.time_unit is not None:
                  td = pd.Timedelta(1, unit=self.time_unit).to_numpy()
              else:
                  if len(time_index) < 2:
                      raise IndexError(
                          "Cannot pad time index with a single timestamp"
                      )
                  td = time_index[-1] - time_index[-2]
              time_index = np.r_[
                  time_index,
                  np.arange(
                      time_index[-1] + td,
                      time_index[-1] + td * (right_pad + 1),
                      td,
                  ),
              ]
              target = _pad_array_along_axis(
                  target, (0, right_pad), constant_values=np.nan
              )
          obj = TimeSeries(
              target,
              time_index=time_index,
              series_name=self.series_name,
              target_names=self.target_names,
          )
          for name in self._static_features:
              data_type = (
                  "cat" if name in self._categorical_features else "num"
              )
              data = self._features[name]
              obj.add_features(
                  name, data, data_type=data_type, feature_type="static"
              )
          for name in self._revealed_features | self._observed_features:
              data_type = (
                  "cat" if name in self._categorical_features else "num"
              )
              feature_type = (
                  "revealed"
                  if name in self._revealed_features
                  else "observed"
              )
              val = -1 if data_type == "cat" else np.nan
              data = self._features[name][index]
              if left_pad is not None:
                  data = _pad_array_along_axis(
                      data, (left_pad, 0), constant_values=val
                  )
              if right_pad is not None:
                  data = _pad_array_along_axis(
                      data, (0, right_pad), constant_values=val
                  )
              obj.add_features(
                  name,
                  data,
                  data_type=data_type,
                  feature_type=feature_type,
              )
          return obj
  
imports: from __future__ import annotations
  from typing import List, Tuple, Dict, Union, Sequence, Optional, Callable, overload
  from operator import eq
  from collections import defaultdict
  import warnings
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from numpy import ndarray
  from tqdm import tqdm
  import numpy
  import pandas
  import torch
label_desc: Use image node_id 22 for calling the TimeSeries obj's underlying member method code with example usage: obj.__getitem__(index) and returns: obj, obj