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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def pb(name, images, max_outputs=3, display_name=None, description=None):
 """Create a legacy image summary protobuf. This behaves as if you were to create an `op` with the same arguments (wrapped with constant tensors where appropriate) and then execute that summary op in a TensorFlow session. Arguments: name: A unique name for the generated summary, including any desired name scopes. images: An `np.array` representing pixel data with shape `[k, h, w, c]`, where `k` is the number of images, `w` and `h` are the width and height of the images, and `c` is the number of channels, which should be 1, 3, or 4. max_outputs: Optional `int`. At most this many images will be emitted. If more than this many images are provided, the first `max_outputs` many images will be used and the rest silently discarded. display_name: Optional name for this summary in TensorBoard, as a `str`. Defaults to `name`. description: Optional long-form description for this summary, as a `str`. Markdown is supported. Defaults to empty. Returns: A `tf.Summary` protobuf object. """

  # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
  import tensorflow.compat.v1 as tf

  images = np.array(images).astype(np.uint8)
  if images.ndim != 4:
    raise ValueError('Shape %r must have rank 4' % (images.shape, ))

  limited_images = images[:max_outputs]
  encoded_images = [encoder.encode_png(image) for image in limited_images]
  (width, height) = (images.shape[2], images.shape[1])
  content = [str(width), str(height)] + encoded_images
  tensor = tf.make_tensor_proto(content, dtype=tf.string)

  if display_name is None:
    display_name = name
  summary_metadata = metadata.create_summary_metadata(
      display_name=display_name, description=description)
  tf_summary_metadata = tf.SummaryMetadata.FromString(
      summary_metadata.SerializeToString())

  summary = tf.Summary()
  summary.value.add(tag='%s/image_summary' % name,
                    metadata=tf_summary_metadata,
                    tensor=tensor)
  return summary