code: def pb(
      name,
      audio,
      sample_rate,
      labels=None,
      max_outputs=3,
      encoding=None,
      display_name=None,
      description=None,
  ):
      """Create a legacy audio 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 node.
        audio: An `np.array` representing audio data with shape `[k, t, c]`,
          where `k` is the number of audio clips, `t` is the number of
          frames, and `c` is the number of channels. Elements should be
          floating-point values in `[-1.0, 1.0]`.
        sample_rate: An `int` that represents the sample rate, in Hz.
          Must be positive.
        labels: Deprecated. Do not set.
        max_outputs: Optional `int`. At most this many audio clips will be
          emitted. When more than `max_outputs` many clips are provided, the
          first `max_outputs` many clips will be used and the rest silently
          discarded.
        encoding: A constant `str` indicating the desired encoding. You
          can choose any format you like, as long as it's "wav". Please see
          the "API compatibility note" below.
        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.
  
      API compatibility note: The default value of the `encoding`
      argument is _not_ guaranteed to remain unchanged across TensorBoard
      versions. In the future, we will by default encode as FLAC instead of
      as WAV. If the specific format is important to you, please provide a
      file format explicitly.
      """
      if labels is not None:
          warnings.warn(_LABELS_WARNING)
  
      # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
      import tensorflow.compat.v1 as tf
  
      audio = np.array(audio)
      if audio.ndim != 3:
          raise ValueError("Shape %r must have rank 3" % (audio.shape,))
      if encoding is None:
          encoding = "wav"
  
      if encoding == "wav":
          encoding = metadata.Encoding.Value("WAV")
          encoder = functools.partial(
              encoder_util.encode_wav, samples_per_second=sample_rate
          )
      else:
          raise ValueError("Unknown encoding: %r" % encoding)
  
      limited_audio = audio[:max_outputs]
      if labels is None:
          limited_labels = [b""] * len(limited_audio)
      else:
          limited_labels = [
              tf.compat.as_bytes(label)
              for label in labels[:max_outputs]
          ]
  
      encoded_audio = [encoder(a) for a in limited_audio]
      content = np.array([encoded_audio, limited_labels]).transpose()
      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,
          encoding=encoding,
      )
      tf_summary_metadata = tf.SummaryMetadata.FromString(
          summary_metadata.SerializeToString()
      )
  
      summary = tf.Summary()
      summary.value.add(
          tag="%s/audio_summary" % name,
          metadata=tf_summary_metadata,
          tensor=tensor,
      )
      return summary
  
file_path: tensorboard/tensorboard/plugins/audio/summary.py
imports: import functools
  import warnings
  import numpy
  from tensorboard.util import encoder
  from tensorboard.plugins.audio import metadata
  from tensorboard.plugins.audio import summary_v2
label_desc: Use image node_id 2 for calling a global function with example usage: pb(name, audio, sample_rate, labels, max_outputs, encoding, display_name, description) and returns: summary