code: def op(
      name, data, display_name=None, description=None, collections=None
  ):
      """Create a legacy text summary op.
  
      Text data summarized via this plugin will be visible in the Text Dashboard
      in TensorBoard. The standard TensorBoard Text Dashboard will render markdown
      in the strings, and will automatically organize 1D and 2D tensors into tables.
      If a tensor with more than 2 dimensions is provided, a 2D subarray will be
      displayed along with a warning message. (Note that this behavior is not
      intrinsic to the text summary API, but rather to the default TensorBoard text
      plugin.)
  
      Args:
        name: A name for the generated node. Will also serve as a series name in
          TensorBoard.
        data: A string-type Tensor to summarize. The text must be encoded in UTF-8.
        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 ops.GraphKeys. The collections to which to add
          the summary. Defaults to [Graph Keys.SUMMARIES].
  
      Returns:
        A TensorSummary op that is configured so that TensorBoard will recognize
        that it contains textual data. The TensorSummary is a scalar `Tensor` of
        type `string` which contains `Summary` protobufs.
  
      Raises:
        ValueError: If tensor has the wrong type.
      """
      # 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):
          with tf.control_dependencies(
              [tf.assert_type(data, tf.string)]
          ):
              return tf.summary.tensor_summary(
                  name="text_summary",
                  tensor=data,
                  collections=collections,
                  summary_metadata=summary_metadata,
              )
  
imports: from tensorboard.plugins.text import metadata
  from tensorboard.plugins.text import summary_v2
label_desc: Use image node_id 1 for calling a global function with example usage: op(name, data, display_name, description, collections) and returns: tf