def expand_to_vector(x, tensor_name=None, op_name=None, validate_args=False):
  """
  """
  with tf.name_scope(op_name or "expand_to_vector"):
    x = tf.convert_to_tensor(value=x, name="x")
    ndims = tensorshape_util.rank(x.shape)

    if ndims is None:
      # Maybe expand ndims from 0 to 1.
      if validate_args:
        x = with_dependencies([
            assert_util.assert_rank_at_most(
                x, 1, message="Input is neither scalar nor vector.")
        ], x)
      ndims = tf.rank(x)
      expanded_shape = pick_vector(
          tf.equal(ndims, 0), np.array([1], dtype=np.int32), tf.shape(input=x))
      return tf.reshape(x, expanded_shape)

    elif ndims == 0:
      # Definitely expand ndims from 0 to 1.
      x_const = tf.get_static_value(x)
      if x_const is not None:
        return tf.convert_to_tensor(
            value=dtype_util.as_numpy_dtype(x.dtype)([x_const]),
            name=tensor_name)

      else:
        return tf.reshape(x, [1])

    elif ndims != 1:
      raise ValueError("Input is neither scalar nor vector.")

    # ndims == 1
    return x