How to use the hub.backend.storage.S3 function in hub

def upload_tfrecord(dataset_type, filepath, version, start_frame):
    storage = S3(bucket='waymo-dataset-upload')
    # str_lasers_range_image = 'edward/{}-lasers-range-image:{}'.format(dataset_type, version)
    # str_lasers_range_image_first = 'edward/{}-lasers-range-image-first:{}'.format(dataset_type, version)
    # str_lasers_camera_proj = 'edward/{}-lasers-camera-proj:{}'.format(dataset_type, version)
    # str_lasers_camera_proj_first = 'edward/{}-lasers-camera-proj-first:{}'.format(dataset_type, version)
    # hub_lasers_range_image = hub.load(name=str_lasers_range_image, storage=storage)
    # hub_lasers_camera_proj = hub.load(name=str_lasers_camera_proj, storage = storage)
    # hub_lasers_range_image_first = hub.load(name=str_lasers_range_image_first, storage=storage)
    # hub_lasers_camera_proj_first = hub.load(name=str_lasers_camera_proj_first, storage=storage)

    str_labels_laser = 'edward/{}-labels-laser:{}'.format(dataset_type, version)
    hub_labels_laser = hub.load(name=str_labels_laser, storage=storage)

    dataset = tf.data.TFRecordDataset(filepath)

    for batch in dataset.batch(100):
        def get_frame_data(data):

def upload_tfrecord(dataset_type, filepath, version, start_frame):
    storage = S3(bucket='waymo-dataset-upload')
    label_name = 'edward/{}-labels:{}'.format(dataset_type, version)
    image_name = 'edward/{}-camera-images:{}'.format(dataset_type, version)
    # print('{} {}'.format(label_name, image_name))
    images_arr = hub.load(name=image_name, storage=storage)
    labels_arr = hub.load(name=label_name, storage=storage)
    frame_count = start_frame
    dataset = tf.data.TFRecordDataset(filepath)
    # print('Yeah {}'.format(frame_count))
    for batch in dataset.batch(16):
        # print('Cycle')
        t1 = clock()
        l = batch.shape[0]
        arr = np.zeros(shape=(l, 6, 1280, 1920, 3), dtype='uint8')
        lab = np.zeros(shape=(l, 2, 6, 30, 7), dtype='float64')
        for i in range(0, l):
            # print('Cycle2')

def upload_tfrecord(dataset_type, filepath, version, start_frame):
    storage = S3(bucket='waymo-dataset-upload')
    str_lasers_range_image = 'edward/{}-lasers-range-image:{}'.format(dataset_type, version)
    str_lasers_range_image_first = 'edward/{}-lasers-range-image-first:{}'.format(dataset_type, version)
    str_lasers_camera_proj = 'edward/{}-lasers-camera-proj:{}'.format(dataset_type, version)
    str_lasers_camera_proj_first = 'edward/{}-lasers-camera-proj-first:{}'.format(dataset_type, version)
    hub_lasers_range_image = hub.load(name=str_lasers_range_image, storage=storage)
    hub_lasers_camera_proj = hub.load(name=str_lasers_camera_proj, storage = storage)
    hub_lasers_range_image_first = hub.load(name=str_lasers_range_image_first, storage=storage)
    hub_lasers_camera_proj_first = hub.load(name=str_lasers_camera_proj_first, storage=storage)

    dataset = tf.data.TFRecordDataset(filepath)

    for batch in dataset.batch(1):
        def get_arr_image(range_image_compressed):
           data = zlib.decompress(range_image_compressed)
           mt = open_dataset.MatrixFloat()
           mt.ParseFromString(data)

def upload_all():
    path = '/home/edward/waymo/training/'
    filenames = os.listdir(path)
    filenames.sort()
    pool = ProcessPool(16)
    data = pool.map(frames_tfrecord, map(lambda f: path + f, filenames))
    frames = sum(data, 0)
    print('Frames in files: {}, Total: {}'.format(data, frames))

    start_frame = []
    for i in range(0, frames):
        start_frame.append(sum(data[:i],0))
    dataset_type = 'training'
    version = 'v2'
    storage = S3(bucket='waymo-dataset-upload')
    labels_arr = hub.array(shape=(frames, 2, 6, 30, 7), chunk_size=(100, 2, 6, 30, 7), storage=storage, name='edward/{}-labels:{}'.format(dataset_type, version), backend='s3', dtype='float64')
    images_arr = hub.array(compression='jpeg', shape=(frames, 6, 1280, 1920, 3), storage=storage, name='edward/{}-camera-images:{}'.format(dataset_type, version), backend='s3', dtype='uint8', chunk_size=(1, 6, 1280, 1920, 3))    

    def upload_record(i):
        upload_tfrecord(dataset_type, path + filenames[i], version, start_frame[i])

    for i in range(0, 5):
        print("Stage {}".format(i))
        pool.map(upload_record, range(i, len(filenames), 5))

def S3(bucket=None, public=False, aws_access_key_id=None, aws_secret_access_key=None, parallel=25):
    return HubBucket(hub.backend.storage.S3(bucket, public, aws_access_key_id, aws_secret_access_key, parallel), 's3')

return frame_count

path = '/home/edward/waymo/validation/'
filenames = os.listdir(path)
filenames.sort()
pool = ProcessPool(16)
data = pool.map(frames_tfrecord, map(lambda f: path + f, filenames))
frames = sum(data, 0)
print('Frames in files: {}, Total: {}'.format(data, frames))

start_frame = []
for i in range(0, frames):
    start_frame.append(sum(data[:i],0))
dataset_type = 'validation'
version = 'v2'
storage = S3(bucket='waymo-dataset-upload')
labels_arr = hub.array(shape=(frames, 2, 6, 30, 7), chunk_size=(100, 2, 6, 30, 7), storage=storage, name='edward/{}-labels:{}'.format(dataset_type, version), backend='s3', dtype='float64')
images_arr = hub.array(compression='jpeg', shape=(frames, 6, 1280, 1920, 3), storage=storage, name='edward/{}-camera-images:{}'.format(dataset_type, version), backend='s3', dtype='uint8', chunk_size=(1, 6, 1280, 1920, 3))    


def upload_record(i):
    waymo_upload.upload_tfrecord(dataset_type, path + filenames[i], version, start_frame[i])
    # os.system('python3 -c "from waymo_upload import upload_the_record; upload_the_record() ", {} {} {} {}'.format(dataset_type, path + filenames[i], version, start_frame[i]))

# for i in range(0, len(filenames), 2):
#     upload_record(i)
#     print("Finished {}".format(filenames[i]))

print("Second stage")

# for i in range(1, len(filenames), 2):
#     upload_record(i)

def __map(self, name):
        # S3 object storage
        if isinstance(name, str) and name == 's3':
            return S3()
        # GS object storage
        elif isinstance(name, str) and name == 'gs':
            return GS()
        # FileSystem object Storage
        elif isinstance(name, str) and name == 'fs':
            return FS()

        elif isinstance(name, Storage):
            return name

        raise Exception('Backend not found {}'.format(name))

def main():
    path = '/home/edward/waymo/validation/'
    dataset_type = 'validation'
    version = 'v2'
    filenames = os.listdir(path)
    filenames.sort()
    pool = ProcessPool(16)
    frame_count_arr = pool.map(frames_tfrecord, map(lambda f: path + f, filenames)) 
    frames = sum(frame_count_arr, 0)

    str_labels_laser = 'edward/{}-labels-laser:{}'.format(dataset_type, version)
    storage = S3(bucket='waymo-dataset-upload')

    hub.array(shape=(frames, 30, 7), dtype='float64', storage=storage, chunk_size=(100, 30, 7), name=str_labels_laser)

    # str_lasers_range_image = 'edward/{}-lasers-range-image:{}'.format(dataset_type, version)
    # str_lasers_range_image_first = 'edward/{}-lasers-range-image-first:{}'.format(dataset_type, version)
    # str_lasers_camera_proj = 'edward/{}-lasers-camera-proj:{}'.format(dataset_type, version)
    # str_lasers_camera_proj_first = 'edward/{}-lasers-camera-proj-first:{}'.format(dataset_type, version)
    # storage = S3(bucket='waymo-dataset-upload')
    # hub.array(shape=(frames, 4, 2, 200, 600, 4), dtype='float32', backend='s3', storage=storage, name=str_lasers_range_image, chunk_size=(1, 4, 2, 200, 600, 4))
    # hub.array(shape=(frames, 4, 2, 200, 600, 6), dtype='int32', backend='s3', storage=storage, name=str_lasers_camera_proj, chunk_size=(1, 4, 2, 200, 600, 6))
    # hub.array(shape=(frames, 2, 64, 2650, 4), dtype='float32', backend='s3', storage=storage, name=str_lasers_range_image_first, chunk_size=(1, 2, 64, 2650, 4))
    # hub.array(shape=(frames, 2, 64, 2650, 6), dtype='int32', backend='s3', storage=storage, name=str_lasers_camera_proj_first, chunk_size=(1, 2, 64, 2650, 6))

    start_frame_arr = []
    for i in range(0, len(filenames)):
        start_frame_arr.append(sum(frame_count_arr[:i], 0))