def predict_injective_single_example(
self, eval_saved_model: load.EvalSavedModel,
raw_example_bytes: bytes) -> types.FeaturesPredictionsLabels:
"""Run predict for a single example for a injective model.
Args:
eval_saved_model: EvalSavedModel
raw_example_bytes: Raw example bytes for the example
Returns:
The singular FPL returned by eval_saved_model.predict on the given
raw_example_bytes.
"""
fetched_list = eval_saved_model.predict(raw_example_bytes)
self.assertEqual(1, len(fetched_list))
self.assertEqual(0, fetched_list[0].input_ref)
return eval_saved_model.as_features_predictions_labels(fetched_list)[0]
def predict_injective_example_list(
self, eval_saved_model: load.EvalSavedModel,
raw_example_bytes_list: List[bytes]
) -> List[types.FeaturesPredictionsLabels]:
"""Run predict_list for a list of examples for a injective model.
Args:
eval_saved_model: EvalSavedModel
raw_example_bytes_list: List of raw example bytes
Returns:
The list of FPLs returned by eval_saved_model.predict on the given
raw_example_bytes.
"""
fetched_list = eval_saved_model.predict_list(raw_example_bytes_list)
# Check that each FPL corresponds to one example.
self.assertSequenceEqual(
range(0, len(raw_example_bytes_list)),
[fetched.input_ref for fetched in fetched_list])
return eval_saved_model.as_features_predictions_labels(fetched_list)
