code: def test_predict_unsupported_output():
      """Tests predictions with models that have unsupported output types."""
      predictor = TensorflowPredictor(model=build_model_unsupported())
  
      data_batch = np.array([1, 2, 3])
      # Unsupported output should fail
      with pytest.raises(ValueError):
          predictor.predict(data_batch)
  
      # Using a CustomPredictor should pass.
      class CustomPredictor(TensorflowPredictor):
          def call_model(self, tensor):
              model_output = super().call_model(tensor)
              return {
                  str(i): model_output[i]
                  for i in range(len(model_output))
              }
  
      predictor = CustomPredictor(model=build_model_unsupported())
      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
  from typing import Tuple
  import numpy
  import pandas
  import pytest
  import tensorflow
  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.data.preprocessor import Preprocessor
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tensorflow import TensorflowCheckpoint, TensorflowPredictor
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
label_desc: Use image node_id 15 for calling a global function with example usage: test_predict_unsupported_output() and returns: unknown