code: def make_dataset() -> torch.utils.data.Dataset:
      training_data = np.array(
          [[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32
      )
      training_data = torch.Tensor(training_data)
      training_labels = np.array([0, 1, 1, 0], dtype=np.float32)
      training_labels = torch.Tensor(training_labels)
      return torch.utils.data.TensorDataset(
          training_data, training_labels
      )
  
imports: import logging
  import queue
  import typing
  from logging import handlers
  import numpy
  import pytest
  import torch
  import determined
  from determined.pytorch import data_length, samplers, to_device
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset() and returns: torch