How to use the visualdl.LogReader function in visualdl

def test_with_syntax(self):
        with self.writer.mode("train") as writer:
            scalar = writer.scalar("model/scalar/average")
            for i in range(10):
                scalar.add_record(i, float(i))

        self.writer.save()

        self.reader = LogReader(self.dir)
        with self.reader.mode("train") as reader:
            scalar = reader.scalar("model/scalar/average")
            self.assertEqual(scalar.caption(), "train")

def test_check_image(self):
        '''
        check whether the storage will keep image data consistent
        '''
        print('check image')
        tag = "layer1/check/image1"
        image_writer = self.writer.image(tag, 10)

        image = Image.open("./dog.jpg")
        shape = [image.size[1], image.size[0], 3]
        origin_data = np.array(image.getdata()).flatten()

        self.writer.save()

        self.reader = LogReader(self.dir)
        with self.reader.mode("train") as reader:

            image_writer.start_sampling()
            image_writer.add_sample(shape, list(origin_data))
            image_writer.finish_sampling()

            # read and check whether the original image will be displayed
            image_reader = reader.image(tag)
            image_record = image_reader.record(0, 0)
            data = image_record.data()
            shape = image_record.shape()

            PIL_image_shape = (shape[0] * shape[1], shape[2])
            data = np.array(data, dtype='uint8').reshape(PIL_image_shape)
            print('origin', origin_data.flatten())
            print('data', data.flatten())

image_writer = self.writer.image(tag, 10, 1)
        num_passes = 10
        num_samples = 100
        shape = [10, 10, 3]

        for pass_ in range(num_passes):
            image_writer.start_sampling()
            for ins in range(num_samples):
                data = np.random.random(shape) * 256
                data = np.ndarray.flatten(data)
                image_writer.add_sample(shape, list(data))
            image_writer.finish_sampling()

        self.writer.save()

        self.reader = LogReader(self.dir)
        with self.reader.mode("train") as reader:
            image_reader = reader.image(tag)
            self.assertEqual(image_reader.caption(), tag)
            self.assertEqual(image_reader.num_records(), num_passes)

            image_record = image_reader.record(0, 1)
            self.assertTrue(np.equal(image_record.shape(), shape).all())
            data = image_record.data()
            self.assertEqual(len(data), np.prod(shape))

            image_tags = reader.tags("image")
            self.assertTrue(image_tags)
            self.assertEqual(len(image_tags), 1)

def test_scalar(self):
        print('test write')
        scalar = self.writer.scalar("model/scalar/min")
        # scalar.set_caption("model/scalar/min")
        for i in range(10):
            scalar.add_record(i, float(i))

        print('test read')
        self.writer.save()
        self.reader = LogReader(self.dir)
        with self.reader.mode("train") as reader:
            scalar = reader.scalar("model/scalar/min")
            self.assertEqual(scalar.caption(), "train")
            records = scalar.records()
            ids = scalar.ids()
            self.assertTrue(
                np.equal(records, [float(i) for i in range(10)]).all())
            self.assertTrue(np.equal(ids, [float(i) for i in range(10)]).all())
            print('records', records)
            print('ids', ids)