Outputs a `Summary` protocol buffer with gif animations.

  Args:
    name: Name of the summary.
    tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
      channels]` where `channels` is 1 or 3.
    max_outputs: Max number of batch elements to generate gifs for.
    fps: frames per second of the animation
    collections: Optional list of tf.GraphKeys.  The collections to add the
      summary to.  Defaults to [tf.GraphKeys.SUMMARIES]
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.

  Raises:
    ValueError: if the given tensor has the wrong shape.

def gif_summary(name, tensor, max_outputs=3, fps=10, collections=None,
                family=None):
  """Outputs a `Summary` protocol buffer with gif animations.

  Args:
    name: Name of the summary.
    tensor: A 5-D `uint8` `Tensor` of shape `[batch_size, time, height, width,
      channels]` where `channels` is 1 or 3.
    max_outputs: Max number of batch elements to generate gifs for.
    fps: frames per second of the animation
    collections: Optional list of tf.GraphKeys.  The collections to add the
      summary to.  Defaults to [tf.GraphKeys.SUMMARIES]
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.

  Raises:
    ValueError: if the given tensor has the wrong shape.
  """
  tensor = tf.convert_to_tensor(tensor)
  if len(tensor.get_shape()) != 5:
    raise ValueError("Assuming videos given as tensors in the format "
                     "[batch, time, height, width, channels] but got one "
                     "of shape: %s" % str(tensor.get_shape()))
  tensor = tf.cast(tensor, tf.uint8)
  if distribute_summary_op_util.skip_summary():
    return tf.constant("")
  with summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    val = tf.py_func(
        py_gif_summary,
        [tag, tensor, max_outputs, fps],
        tf.string,
        stateful=False,
        name=scope)
    summary_op_util.collect(val, collections, [tf.GraphKeys.SUMMARIES])
  return val