class_docstr: A dataset split provides all the representations for a particular split (train/val/test) of a
  dataset.
class_docstr_tok: ["A","dataset","split","provides","all","the","representations","for","a","particular","split","(","train\/val\/test",")","of","a","dataset","."]
code: def support_set(self) -> List[List[TimeSeries]]:
      support_set_path = (
          self._directory / self._split / ".support_set.json"
      )
      assert (
          support_set_path.exists()
      ), "No support set has been saved for this dataset."
      support_set = []
      with open(support_set_path) as json_file:
          for line in json_file:
              series = [
                  TimeSeries(
                      dataset_name=self._dataset_name,
                      values=torch.FloatTensor(s["target"]).unsqueeze(
                          -1
                      ),
                      item_id=s["item_id"],
                      start_date=s["start"],
                  )
                  for s in json.loads(line)
              ]
              support_set.append(series)
      return support_set
  
imports: from __future__ import annotations
  import json
  from dataclasses import dataclass
  from typing import Tuple, Optional, List
  from pathlib import Path
  from gluonts.dataset.common import FileDataset, MetaData
  from gluonts.dataset.field_names import FieldName
  import torch
  import numpy
  import numpy.ma
  import shutil
  from meta.data.dataset import TimeSeries, TimeSeriesDataset
  from meta.common import tensor_to_np
label_desc: Use image node_id 2 for calling the DatasetSplit obj's underlying member method code with example usage: obj.support_set() and returns: support_set