How to use the gradio.outputs.Label function in gradio

def test_postprocessing_1D_array_no_confidences(self):
        array = np.array([0.1, 0.2, 0, 0.7, 0])
        true_label = {outputs.Label.LABEL_KEY: 3}
        out = outputs.Label(show_confidences=False)
        label = json.loads(out.postprocess(array))
        self.assertDictEqual(label, true_label)

def test_set_sample_data(self):
        test_array = ["test1", "test2", "test3"]
        temp_dir = tempfile.mkdtemp()
        inp = inputs.Sketchpad()
        out = outputs.Label()
        networking.build_template(temp_dir, inp, out)
        networking.set_sample_data_in_config_file(temp_dir, test_array)
        # We need to come up with a better way so that the config file isn't invalid json unless
        # the following parameters are set... (TODO: abidlabs)
        networking.set_always_flagged_in_config_file(temp_dir, False)
        networking.set_disabled_in_config_file(temp_dir, False)
        config_file = os.path.join(temp_dir, 'static/config.json')
        with open(config_file) as json_file:
            data = json.load(json_file)
            self.assertTrue(test_array == data["sample_inputs"])

def test_postprocessing_int(self):
        true_label_array = np.array([[[3]]])
        true_label = {outputs.Label.LABEL_KEY: 3}
        out = outputs.Label()
        label = json.loads(out.postprocess(true_label_array))
        self.assertDictEqual(label, true_label)

def postprocess(self, prediction):
        """
        """
        response = dict()
        # TODO(abidlabs): check if list, if so convert to numpy array
        if isinstance(prediction, np.ndarray):
            prediction = prediction.squeeze()
            if prediction.size == 1:  # if it's single value
                response[Label.LABEL_KEY] = self.get_label_name(np.asscalar(prediction))
            elif len(prediction.shape) == 1:  # if a 1D
                response[Label.LABEL_KEY] = self.get_label_name(int(prediction.argmax()))
                if self.show_confidences:
                    response[Label.CONFIDENCES_KEY] = []
                    for i in range(self.num_top_classes):
                        response[Label.CONFIDENCES_KEY].append({
                            Label.LABEL_KEY: self.get_label_name(int(prediction.argmax())),
                            Label.CONFIDENCE_KEY: float(prediction.max()),
                        })
                        prediction[prediction.argmax()] = 0
        elif isinstance(prediction, str):
            response[Label.LABEL_KEY] = prediction
        else:
            raise ValueError("Unable to post-process model prediction.")
        return json.dumps(response)