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 __init__(self, dataset, transform=None):
      self.dataset = dataset
      self.transform = transform
      self._preprocess()
  
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 1 to create a new TripleSet object from inherited base classes: Dataset with example: obj = TripleSet(dataset, transform)