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 end_date(self) -> pd.Timestamp:
      return (
          self.start_date + (self.__len__() - 1) * self.start_date.freq
      )
  
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 1 for calling the TimeSeries obj's underlying member method code with example usage: obj.end_date() and returns: unknown