<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def op(name, images, max_outputs=3, display_name=None, description=None, collections=None):
 """Create a legacy image summary op for use in a TensorFlow graph. Arguments: name: A unique name for the generated summary node. images: A `Tensor` representing pixel data with shape `[k, h, w, c]`, where `k` is the number of images, `h` and `w` are the height and width of the images, and `c` is the number of channels, which should be 1, 3, or 4. Any of the dimensions may be statically unknown (i.e., `None`). max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this many images will be emitted at each step. When more than `max_outputs` 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 constant `str`. Defaults to `name`. description: Optional long-form description for this summary, as a constant `str`. Markdown is supported. Defaults to empty. collections: Optional list of graph collections keys. The new summary op is added to these collections. Defaults to `[Graph Keys.SUMMARIES]`. Returns: A TensorFlow summary op. """

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

  if display_name is None:
    display_name = name
  summary_metadata = metadata.create_summary_metadata(
      display_name=display_name, description=description)
  with tf.name_scope(name), \
       tf.control_dependencies([tf.assert_rank(images, 4),
                                tf.assert_type(images, tf.uint8),
                                tf.assert_non_negative(max_outputs)]):
    limited_images = images[:max_outputs]
    encoded_images = tf.map_fn(tf.image.encode_png, limited_images,
                               dtype=tf.string,
                               name='encode_each_image')
    image_shape = tf.shape(input=images)
    dimensions = tf.stack([tf.as_string(image_shape[2], name='width'),
                           tf.as_string(image_shape[1], name='height')],
                          name='dimensions')
    tensor = tf.concat([dimensions, encoded_images], axis=0)
    return tf.summary.tensor_summary(name='image_summary',
                                     tensor=tensor,
                                     collections=collections,
                                     summary_metadata=summary_metadata)