class_docstr: This class inherits from the :class:`torch.utils.data.Dataset` class and in
  addition to each anchor sample, it returns a random positive and negative
  sample from the dataset. A positive sample has the same label as the
  anchor sample and a negative sample has a different label than the anchor
  sample.
  
  Args:
      dataset (:class:`torch.utils.data.Dataset`): The dataset for which the
          triple set will be created.
      transform (function, optional): A transformation that is applied on all
          original samples. In other words, this transformation is applied
          to the anchor, positive, and negative sample. Default is None.
code: def _preprocess(self):
      self.label_to_index_list = {}
      for i, data in enumerate(self.dataset):
          y = int(data.y.item())
          if not y in self.label_to_index_list:
              self.label_to_index_list[y] = [i]
          else:
              self.label_to_index_list[y].append(i)
  
imports: import random
  import torch
  import torch.nn.functional
  from torch.utils.data import Dataset
  from torch_geometric.utils import degree
label_desc: Use image node_id 2 for calling the TripleSet obj's underlying member method code with example usage: obj._preprocess() without return types