class_docstr: Dataset to benchmark a translation like seq2seq task.
  Args:
      vocab_size (int, optional): size of the vocabulary (default 10000).
      max_source_positions (int, optional): max number of tokens in the
          source sentence (default: 1024).
      total_samples (int, optional): the total number of rows in the
          dataset (default: 10000).
code: def __getitem__(self, index):
      length = self.sizes[index]
      source = torch.randint(1, self.vocab_size, (length,))
      target = source.clone()
      return {
          "id": index,
          "source": source,
          "target": target,
      }
  
imports: import torch
  from torch.utils.data import Dataset
label_desc: Use image node_id 2 for calling the BenchmarkLMDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: dict