class_docstr: A batch of series from different base datasets, represented by the padded batch, the lengths of the series
  and the splits sizes indicating the corresponding base dataset.
class_docstr_tok: ["A","batch","of","series","from","different","base","datasets",",","represented","by","the","padded","batch",",","the","lengths","of","the","series","and","the","splits","sizes","indicating","the","corresponding","base","dataset","."]
docstr: Redo standardization. The series must contain the same time series in the same order as the dataset.
docstr_tok: ["Redo","standardization",".","The","series","must","contain","the","same","time","series","in","the","same","order","as","the","dataset","."]
code: def rescale(self) -> SeriesBatch:
      """
      Redo standardization. The series must contain the same time series in the same order as the dataset.
      """
      m = self.scales[:, 0].unsqueeze(1)
      std = self.scales[:, 1].unsqueeze(1)
      return SeriesBatch(
          self.sequences * std + m,
          self.lengths,
          self.split_sections,
      )
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import List, Optional
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from .sampling import Triplet
  from .dataset import TimeSeries
  from meta.common.torch import tensor_to_np
label_desc: Use image node_id 5 for calling the SeriesBatch obj's underlying member method code with example usage: obj.rescale() and returns: SeriesBatch