class_docstr: "A labeled dataset, where each sample is a multiset of integers
  encoded as bit-vectors, and the label is the second smallest integer
  in the multiset.
code: def __init__(
      self,
      num_examples: int,
      num_bits: int,
      min_num_elems: int,
      max_num_elems: int,
  ):
      super().__init__(transform=RandomPermutation())
  
      self.data, self.slices = self.collate(
          [
              self.get_data(num_bits, min_num_elems, max_num_elems)
              for _ in range(num_examples)
          ]
      )
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import LCMAggregation
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 1 to create a new Random2ndMinimumDataset object from inherited base classes: InMemoryDataset with example: obj = Random2ndMinimumDataset(num_examples, num_bits, min_num_elems, max_num_elems)