code: def test_array_real_model(use_gpu):
      model = torch.nn.Linear(2, 1)
      predictor = TorchPredictor(model=model, use_gpu=use_gpu)
  
      data = np.array([[1, 2], [3, 4]])
      predictions = predictor.predict(data, dtype=torch.float)
      assert len(predictions) == 1
      assert len(predictions["predictions"]) == 2
  
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 14 for calling a global function with example usage: test_array_real_model(use_gpu) without return types