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.
docstr: Returns:
       The number of samples in the original dataset.
docstr_tok: ["Returns",":","The","number","of","samples","in","the","original","dataset","."]
code: def __len__(self):
      r"""
      Returns:
           The number of samples in the original dataset.
      """
      return len(self.dataset)
  
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 4 for calling the TripleSet obj's underlying member method code with example usage: obj.__len__() and returns: len