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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 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)