code: def test_predict_multi_output(use_gpu):
      predictor = TorchPredictor(
          model=DummyModelMultiOutput(), use_gpu=use_gpu
      )
  
      data_batch = np.array([1, 2, 3])
      predictions = predictor.predict(data_batch)
  
      # Model outputs two tensors
      assert len(predictions) == 2
      for k, v in predictions.items():
          # Each tensor is of size 3
          assert len(v) == 3
          assert v.flatten().tolist() == [1, 2, 3]
  
imports: import re
  import numpy
  import pandas
  import pytest
  import torch
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_batch_type_to_pandas, _convert_pandas_to_batch_type
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
  from ray.train.torch import TorchCheckpoint, TorchPredictor
label_desc: Use image node_id 10 for calling a global function with example usage: test_predict_multi_output(use_gpu) without return types