def METHOD_NAME(sagemaker_session, cpu_instance_type):
    job_name = unique_name_from_base("object2vec")
    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        data_path = os.path.join(DATA_DIR, "object2vec")
        data_filename = "train.jsonl"
        with open(os.path.join(data_path, data_filename), "r") as f:
            num_records = len(f.readlines())
        object2vec = Object2Vec(
            role="SageMakerRole",
            instance_count=1,
            instance_type=cpu_instance_type,
            epochs=3,
            enc0_max_seq_len=20,
            enc0_vocab_size=45000,
            enc_dim=16,
            num_classes=3,
            negative_sampling_rate=0,
            comparator_list="hadamard,concat,abs_diff",
            tied_token_embedding_weight=False,
            token_embedding_storage_type="dense",
            sagemaker_session=sagemaker_session,
        )
        record_set = prepare_record_set_from_local_files(
            data_path, object2vec.data_location, num_records, FEATURE_NUM, sagemaker_session
        )
        object2vec.fit(records=record_set, job_name=job_name)
    with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
        model = Object2VecModel(
            object2vec.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
        )
        predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
        assert isinstance(predictor, Predictor)
        predict_input = {"instances": [{"in0": [354, 623], "in1": [16]}]}
        result = predictor.predict(predict_input)
        assert len(result) == 1
        for record in result:
            assert record.label["scores"] is not None