def prepare_video_inputs(image_processor_tester, equal_resolution=False, numpify=False, torchify=False):
    """This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
    One can specify whether the videos are of the same resolution or not.
    """

    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    video_inputs = []
    for i in range(image_processor_tester.batch_size):
        if equal_resolution:
            width = height = image_processor_tester.max_resolution
        else:
            width, height = np.random.choice(
                np.arange(image_processor_tester.min_resolution, image_processor_tester.max_resolution), 2
            )
            video = prepare_video(
                image_processor_tester=image_processor_tester,
                width=width,
                height=height,
                numpify=numpify,
                torchify=torchify,
            )
        video_inputs.append(video)

    return video_inputs
This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
    one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
    One can specify whether the videos are of the same resolution or not.
    
dpkd/transformers/tests/models/tvlt/test_image_processor_tvlt.py