Write an audio summary.

  Arguments:
    name: A name for this summary. The summary tag used for TensorBoard will
      be this name prefixed by any active name scopes.
    data: A `Tensor` 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]`. Any of the dimensions may
      be statically unknown (i.e., `None`).
    sample_rate: An `int` or rank-0 `int32` `Tensor` that represents the
      sample rate, in Hz. Must be positive.
    step: Explicit `int64`-castable monotonic step value for this summary. If
      omitted, this defaults to `tf.summary.experimental.get_step()`, which must
      not be None.
    max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
      many audio clips will be emitted at each step. When more than
      `max_outputs` many clips are provided, the first `max_outputs`
      many clips will be used and the rest silently discarded.
    encoding: Optional constant `str` for the desired encoding. Only "wav"
      is currently supported, but this is not guaranteed to remain the
      default, so if you want "wav" in particular, set this explicitly.
    description: Optional long-form description for this summary, as a
      constant `str`. Markdown is supported. Defaults to empty.

  Returns:
    True on success, or false if no summary was emitted because no default
    summary writer was available.

  Raises:
    ValueError: if a default writer exists, but no step was provided and
      `tf.summary.experimental.get_step()` is None.

def audio(name,
          data,
          sample_rate,
          step=None,
          max_outputs=3,
          encoding=None,
          description=None):
  """Write an audio summary.

  Arguments:
    name: A name for this summary. The summary tag used for TensorBoard will
      be this name prefixed by any active name scopes.
    data: A `Tensor` 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]`. Any of the dimensions may
      be statically unknown (i.e., `None`).
    sample_rate: An `int` or rank-0 `int32` `Tensor` that represents the
      sample rate, in Hz. Must be positive.
    step: Explicit `int64`-castable monotonic step value for this summary. If
      omitted, this defaults to `tf.summary.experimental.get_step()`, which must
      not be None.
    max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this
      many audio clips will be emitted at each step. When more than
      `max_outputs` many clips are provided, the first `max_outputs`
      many clips will be used and the rest silently discarded.
    encoding: Optional constant `str` for the desired encoding. Only "wav"
      is currently supported, but this is not guaranteed to remain the
      default, so if you want "wav" in particular, set this explicitly.
    description: Optional long-form description for this summary, as a
      constant `str`. Markdown is supported. Defaults to empty.

  Returns:
    True on success, or false if no summary was emitted because no default
    summary writer was available.

  Raises:
    ValueError: if a default writer exists, but no step was provided and
      `tf.summary.experimental.get_step()` is None.
  """
  audio_ops = getattr(tf, 'audio', None)
  if audio_ops is None:
    # Fallback for older versions of TF without tf.audio.
    from tensorflow.python.ops import gen_audio_ops as audio_ops

  if encoding is None:
    encoding = 'wav'
  if encoding != 'wav':
    raise ValueError('Unknown encoding: %r' % encoding)
  summary_metadata = metadata.create_summary_metadata(
      display_name=None,
      description=description,
      encoding=metadata.Encoding.Value('WAV'))
  inputs = [data, sample_rate, max_outputs, step]
  # TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
  summary_scope = (
      getattr(tf.summary.experimental, 'summary_scope', None) or
      tf.summary.summary_scope)
  with summary_scope(
      name, 'audio_summary', values=inputs) as (tag, _):
    tf.debugging.assert_rank(data, 3)
    tf.debugging.assert_non_negative(max_outputs)
    limited_audio = data[:max_outputs]
    encode_fn = functools.partial(audio_ops.encode_wav,
                                  sample_rate=sample_rate)
    encoded_audio = tf.map_fn(encode_fn, limited_audio,
                              dtype=tf.string,
                              name='encode_each_audio')
    # Workaround for map_fn returning float dtype for an empty elems input.
    encoded_audio = tf.cond(
        tf.shape(input=encoded_audio)[0] > 0,
        lambda: encoded_audio, lambda: tf.constant([], tf.string))
    limited_labels = tf.tile([''], tf.shape(input=limited_audio)[:1])
    tensor = tf.transpose(a=tf.stack([encoded_audio, limited_labels]))
    return tf.summary.write(
        tag=tag, tensor=tensor, step=step, metadata=summary_metadata)