Returns the last evaluations of the given metric at the given session.

  Args:
    multiplexer: The EventMultiplexer instance allowing access to
        the exported summary data.
    session_name: String. The session name for which to get the metric
        evaluations.
    metric_name: api_pb2.MetricName proto. The name of the metric to use.

  Returns:
    A 3-tuples, of the form [wall-time, step, value], denoting
    the last evaluation of the metric, where wall-time denotes the wall time
    in seconds since UNIX epoch of the time of the evaluation, step denotes
    the training step at which the model is evaluated, and value denotes the
    (scalar real) value of the metric.

  Raises:
    KeyError if the given session does not have the metric.

def last_metric_eval(multiplexer, session_name, metric_name):
  """Returns the last evaluations of the given metric at the given session.

  Args:
    multiplexer: The EventMultiplexer instance allowing access to
        the exported summary data.
    session_name: String. The session name for which to get the metric
        evaluations.
    metric_name: api_pb2.MetricName proto. The name of the metric to use.

  Returns:
    A 3-tuples, of the form [wall-time, step, value], denoting
    the last evaluation of the metric, where wall-time denotes the wall time
    in seconds since UNIX epoch of the time of the evaluation, step denotes
    the training step at which the model is evaluated, and value denotes the
    (scalar real) value of the metric.

  Raises:
    KeyError if the given session does not have the metric.
  """
  try:
    run, tag = run_tag_from_session_and_metric(session_name, metric_name)
    tensor_events = multiplexer.Tensors(run=run, tag=tag)
  except KeyError as e:
    raise KeyError(
        'Can\'t find metric %s for session: %s. Underlying error message: %s'
        % (metric_name, session_name, e))
  last_event = tensor_events[-1]
  # TODO(erez): Raise HParamsError if the tensor is not a 0-D real scalar.
  return (last_event.wall_time,
          last_event.step,
          tf.make_ndarray(last_event.tensor_proto).item())