def test_vectorise_with_default_model_different_properties(self):
        """same models with different properties should return different outputs
        """
        model_name = 'sentence-transformers/all-mpnet-base-v2'
        model_properties_default = {"name": "sentence-transformers/all-mpnet-base-v2",
                                    "dimensions": 768,
                                    "tokens": 128,
                                    "type": "sbert"}

        model_properties_custom = {"name": "sentence-transformers/all-mpnet-base-v2",
                                   "dimensions": 768,
                                   "tokens": 4,
                                   "type": "sbert"}

        content = "A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this " \
                  "case, from_tf should be set to True and a configuration object should be provided as config " \
                  "argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model " \
                  "using the provided conversion scripts and loading the PyTorch model afterwards. "

        res_default = vectorise(model_name=model_name, model_properties=model_properties_default, content=content, device="cpu")
        res_custom = vectorise(model_name='custom-model', model_properties=model_properties_custom, content=content, device="cpu")

        self.assertNotEqual(res_default, res_custom)
        # self.assertEqual(np.array(res_default).shape[-1], np.array(res_custom).shape[-1])
same models with different properties should return different outputs
        
tests/integ_tests/s2_inference/test_generic_model.py