code: def py_gif_summary(
      tag, images, max_outputs, fps, return_summary_value=False
  ):
      """Outputs a `Summary` protocol buffer with gif animations.
  
      Args:
        tag: Name of the summary.
        images: A 5-D `uint8` `np.array` 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.
        return_summary_value: If set to True, return a list of tf.Summary.Value
                              objects in addition to the protocol buffer.
  
      Returns:
        The serialized `Summary` protocol buffer.
  
      Raises:
        ValueError: If `images` is not a 5-D `uint8` array with 1 or 3 channels.
      """
      images = np.asarray(images)
      if images.dtype != np.uint8:
          raise ValueError(
              "Tensor must have dtype uint8 for gif summary."
          )
      if images.ndim != 5:
          raise ValueError("Tensor must be 5-D for gif summary.")
      batch_size, _, height, width, channels = images.shape
      if channels not in (1, 3):
          raise ValueError(
              "Tensors must have 1 or 3 channels for gif summary."
          )
  
      summ = tf.Summary()
      all_summ_values = []
      num_outputs = min(batch_size, max_outputs)
      for i in range(num_outputs):
          image_summ = tf.Summary.Image()
          image_summ.height = height
          image_summ.width = width
          image_summ.colorspace = channels  # 1: grayscale, 3: RGB
          try:
              image_summ.encoded_image_string = _encode_gif(
                  images[i], fps
              )
          except (IOError, OSError) as e:
              tf.logging.warning(
                  "Unable to encode images to a gif string because either ffmpeg is "
                  "not installed or ffmpeg returned an error: %s. Falling back to an "
                  "image summary of the first frame in the sequence.",
                  e,
              )
              try:
                  from PIL import (
                      Image,
                  )  # pylint: disable=g-import-not-at-top
                  import io  # pylint: disable=g-import-not-at-top
  
                  with io.BytesIO() as output:
                      Image.fromarray(images[i][0]).save(output, "PNG")
                      image_summ.encoded_image_string = (
                          output.getvalue()
                      )
              except ImportError as e:
                  tf.logging.warning(
                      "Gif summaries requires ffmpeg or PIL to be installed: %s",
                      e,
                  )
                  image_summ.encoded_image_string = ""
          if num_outputs == 1:
              summ_tag = "{}/gif".format(tag)
          else:
              summ_tag = "{}/gif/{}".format(tag, i)
          curr_summ_value = tf.Summary.Value(
              tag=summ_tag, image=image_summ
          )
          all_summ_values.append(curr_summ_value)
          summ.value.add(tag=summ_tag, image=image_summ)
      summ_str = summ.SerializeToString()
      if return_summary_value:
          return all_summ_values, summ_str
      return summ_str
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
  from tensorflow.python.ops import summary_op_util
label_desc: Use image node_id 16 for calling a global function with example usage: py_gif_summary(tag, images, max_outputs, fps, return_summary_value) and returns: summ_str, all_summ_values,  summ_str