code: def test_predict_array_with_different_dtypes(
      model, input_dtype, expected_output_dtype, use_gpu
  ):
      predictor = TorchPredictor(model=model, use_gpu=use_gpu)
  
      data_batch = np.array([1, 2, 3])
      predictions = predictor.predict(data_batch, dtype=input_dtype)
  
      assert predictions["predictions"].dtype == expected_output_dtype
  
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 12 for calling a global function with example usage: test_predict_array_with_different_dtypes(model, input_dtype, expected_output_dtype, use_gpu) without return types