class_docstr: The super sampling triplet dataset randomly samples support sets and past queries
  along with their future prediction horizon from a list of sampling datasets.
  First a sampling dataset is randomly chosen.
  Then the chosen triplet dataset samples support, query past and query future set.
  The dataset yields infinitely many items.
class_docstr_tok: ["The","super","sampling","triplet","dataset","randomly","samples","support","sets","and","past","queries","along","with","their","future","prediction","horizon","from","a","list","of","sampling","datasets",".","First","a","sampling","dataset","is","randomly","chosen",".","Then","the","chosen","triplet","dataset","samples","support",",","query","past","and","query","future","set",".","The","dataset","yields","infinitely","many","items","."]
code: def __iter__(self) -> Iterator[Triplet]:
      while True:
          index = np.random.choice(
              len(self.datasets), p=self.dataset_weights
          )
          # Return item of chosen base dataset
          yield next(self.datasets[index])
  
imports: from __future__ import annotations
  import numpy
  import pandas
  from dataclasses import dataclass, fields
  from typing import Iterator, List, Optional
  import torch
  from torch.utils.data import IterableDataset, Dataset
  from .dataset import TimeSeries, TimeSeriesDataset
label_desc: Use image node_id 2 for calling the SuperSamplingTripletDataset obj's underlying member method code with example usage: obj.__iter__() without return types