def __init__(self, groups=None):
        super(GroupedRequirements, self).__init__(NamedRequirements)
        self.groups = groups or self.default_groups
        self.filename = None
        self.create_default_groups()
def _ready(op, sess, msg):
  """Checks if the model is ready or not, as determined by op.

  Args:
    op: An op, either _ready_op or _ready_for_local_init_op, which defines the
      readiness of the model.
    sess: A `Session`.
    msg: A message to log to warning if not ready

  Returns:
    A tuple (is_ready, msg), where is_ready is True if ready and False
    otherwise, and msg is `None` if the model is ready, a `String` with the
    reason why it is not ready otherwise.
  """
  if op is None:
    return True, None
  else:
    try:
      ready_value = sess.run(op)
      # The model is considered ready if ready_op returns an empty 1-D tensor.
      # Also compare to `None` and dtype being int32 for backward
      # compatibility.
      if (ready_value is None or ready_value.dtype == np.int32 or
          ready_value.size == 0):
        return True, None
      else:
        # TODO(sherrym): If a custom ready_op returns other types of tensor,
        # or strings other than variable names, this message could be
        # confusing.
        non_initialized_varnames = ", ".join(
            [i.decode("utf-8") for i in ready_value])
        return False, "Variables not initialized: " + non_initialized_varnames
    except errors.FailedPreconditionError as e:
      if "uninitialized" not in str(e):
        logging.warning("%s : error [%s]", msg, str(e))
        raise e
      return False, str(e)