docstr: Returns a dataset of random inputs and labels for binary classification.
docstr_tok: ["Returns","a","dataset","of","random","inputs","and","labels","for","binary","classification","."]
code: def _generate_dataset(
      self, num_samples: int, input_dim: int
  ) -> Dataset[Batch]:
      """Returns a dataset of random inputs and labels for binary classification."""
      data = torch.randn(num_samples, input_dim)
      labels = torch.randint(low=0, high=2, size=(num_samples,))
      return TensorDataset(data, labels)
  
file_path: tnt/tests/utils/data/test_data_prefetcher.py
imports: import unittest
  from typing import Tuple
  import torch
  from torch.utils.data.dataset import Dataset, TensorDataset
  from torchtnt.utils.data.data_prefetcher import CudaDataPrefetcher
label_desc: Use image node_id 1 for calling the DataTest obj's underlying member method code with example usage: obj._generate_dataset(num_samples, input_dim) and returns: TensorDataset