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 get_data(
      self,
      num_bits: int,
      min_num_elems: int,
      max_num_elems: int,
  ) -> Data:
      num_elems = int(
          torch.randint(min_num_elems, max_num_elems + 1, (1,))
      )
  
      x = torch.randint(0, 2, (num_elems, num_bits))
  
      power = torch.pow(2, torch.arange(num_bits)).flip([0])
      ints = (x * power.view(1, -1)).sum(dim=-1)
      y = x[ints.topk(k=2, largest=False).indices[-1:]].to(torch.float)
  
      return Data(x=x, y=y)
  
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 2 for calling the Random2ndMinimumDataset obj's underlying member method code with example usage: obj.get_data(num_bits, min_num_elems, max_num_elems) and returns: Data