class_docstr: A time series contains the time series data along with metadata about the time series as well
  as static and dynamic features.
class_docstr_tok: ["A","time","series","contains","the","time","series","data","along","with","metadata","about","the","time","series","as","well","as","static","and","dynamic","features","."]
code: def __getitem__(self, sequence: slice) -> TimeSeries:
      assert (
          sequence.start >= 0
      ), "Time series cannot be sliced prior to start."
      assert (
          sequence.step is None or sequence.step == 1
      ), "Time series cannot be sliced with a step size other than 1."
  
      return TimeSeries(
          dataset_name=self.dataset_name,
          item_id=self.item_id,
          start_date=self.start_date
          + sequence.start * self.start_date.freq,
          values=self.values[sequence],
          feat_static_cat=self.feat_static_cat,
          scale=self.scale,
      )
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/data/dataset.py
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import List, Optional
  import pandas
  import torch
  from torch.utils.data import Dataset
label_desc: Use image node_id 6 for calling the TimeSeries obj's underlying member method code with example usage: obj.__getitem__(sequence) and returns: TimeSeries