code: def summarize_video(video, prefix, max_outputs=1):
    
    video_shape = shape_list(video)
    if len(video_shape) != 5:
      raise ValueError("Assuming videos given as tensors in the format "
                       "[batch, time, height, width, channels] but got one "
                       "of shape: %s" % str(video_shape))
    if tf.executing_eagerly():
      return
    if video.get_shape().as_list()[1] is None:
      tf.summary.image(
          "%s_last_frame" % prefix,
          tf.cast(video[:, -1, :, :, :], tf.uint8),
          max_outputs=max_outputs)
    else:
      for k in range(video_shape[1]):
        tf.summary.image(
            "%s_frame_%d" % (prefix, k),
            tf.cast(video[:, k, :, :, :], tf.uint8),
            max_outputs=max_outputs)
docs: Summarize the video using image summaries starting with prefix.