def test_inference_pipe_do_not_care_about_modality(self):
        """Ensure that the HuggingFaceModelInferencePipeline can still vectorise the content even if the modality is not
        TEXT."""
        text_inference_request = InferenceRequest(
            modality=Modality.IMAGE,
            contents=[TestImageUrls.IMAGE1.value],
            device="cpu",
            model_config=ModelConfig(
                model_name="hf/e5-base-v2",
                model_properties={
                    "name": 'intfloat/e5-base-v2',
                    "dimensions": 768,
                    "tokens": 512,
                    "type": "hf",
                },
                normalize_embeddings=True
            ),
            preprocessing_config=ImagePreprocessingConfig(
                download_header=dict(),
                download_thread_count=1,
            )
        )

        results = self.inference.vectorise(text_inference_request)
        self.assertTrue(isinstance(results, InferenceResult))
        self.assertTrue(isinstance(results.result, list))
        self.assertTrue(len(results.result) == 1)

        results_1: list[tuple[str, ndarray]] = results.result[0]
        self.assertTrue(isinstance(results_1, list))
        self.assertTrue(len(results_1) == 1)
        self.assertTrue(isinstance(results_1[0], tuple))
        self.assertTrue(isinstance(results_1[0][0], str))
        self.assertTrue(isinstance(results_1[0][1], np.ndarray))
        self.assertEqual((768, ), results_1[0][1].shape)
        self.assertEqual(TestImageUrls.IMAGE1.value, results_1[0][0])
Ensure that the HuggingFaceModelInferencePipeline can still vectorise the content even if the modality is not
        TEXT.
tests/integ_tests/inference/native_inference/test_hugging_face_model_inference_pipeline.py