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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 pb(name, data, display_name=None, description=None):
 """Create a legacy scalar summary protobuf. Arguments: name: A unique name for the generated summary, including any desired name scopes. data: A rank-0 `np.array` or array-like form (so raw `int`s and `float`s are fine, too). 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. """

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

  data = np.array(data)
  if data.shape != ():
    raise ValueError('Expected scalar shape for data, saw shape: %s.'
                     % data.shape)
  if data.dtype.kind not in ('b', 'i', 'u', 'f'):  # bool, int, uint, float
    raise ValueError('Cast %s to float is not supported' % data.dtype.name)
  tensor = tf.make_tensor_proto(data.astype(np.float32))

  if display_name is None:
    display_name = name
  summary_metadata = metadata.create_summary_metadata(
      display_name=display_name, description=description)
  tf_summary_metadata = tf.SummaryMetadata.FromString(
      summary_metadata.SerializeToString())
  summary = tf.Summary()
  summary.value.add(tag='%s/scalar_summary' % name,
                    metadata=tf_summary_metadata,
                    tensor=tensor)
  return summary