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","."]
code: def pin_memory(self):
      self.sequences = self.sequences.pin_memory()
      self.lengths = self.lengths.pin_memory()
      self.split_sections = self.split_sections.pin_memory()
      if self.scales is not None:
          self.scales = self.scales.pin_memory()
      return self
  
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 2 for calling the SeriesBatch obj's underlying member method code with example usage: obj.pin_memory() and returns: self