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 standardize(self, m: torch.Tensor, std: torch.Tensor):
      return TimeSeries(
          dataset_name=self.dataset_name,
          item_id=self.item_id,
          start_date=self.start_date,
          values=(self.values - m) / std,
          feat_static_cat=self.feat_static_cat,
          scale=torch.cat([m, std]),
      )
  
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 4 for calling the TimeSeries obj's underlying member method code with example usage: obj.standardize(m, std) and returns: TimeSeries