code: def test_predict(batch_type):
      predictor = TorchPredictor(model=DummyModelMultiInput())
  
      raw_batch = pd.DataFrame(
          {"X0": [0.0, 0.0, 0.0], "X1": [1.0, 2.0, 3.0]}
      )
      data_batch = _convert_pandas_to_batch_type(
          raw_batch, type=TYPE_TO_ENUM[batch_type]
      )
      raw_predictions = predictor.predict(data_batch, dtype=torch.float)
      predictions = _convert_batch_type_to_pandas(raw_predictions)
  
      assert len(predictions) == 3
      assert predictions.to_numpy().flatten().tolist() == [
          1.0,
          2.0,
          3.0,
      ]
  
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 6 for calling a global function with example usage: test_predict(batch_type) without return types