def maybe_check_quadrature_param(param, name, validate_args):
  """"""
  with tf.name_scope("check_" + name):
    assertions = []
    if tensorshape_util.rank(param.shape) is not None:
      if tensorshape_util.rank(param.shape) == 0:
        raise ValueError("Mixing params must be a (batch of) vector; "
                         "{}.rank={} is not at least one.".format(
                             name, tensorshape_util.rank(param.shape)))
    elif validate_args:
      assertions.append(
          assert_util.assert_rank_at_least(
              param,
              1,
              message=("Mixing params must be a (batch of) vector; "
                       "{}.rank is not at least one.".format(name))))

    # TODO(jvdillon): Remove once we support k-mixtures.
    if tensorshape_util.with_rank_at_least(param.shape, 1)[-1] is not None:
      if tf.compat.dimension_value(param.shape[-1]) != 1:
        raise NotImplementedError("Currently only bimixtures are supported; "
                                  "{}.shape[-1]={} is not 1.".format(
                                      name,
                                      tf.compat.dimension_value(
                                          param.shape[-1])))
    elif validate_args:
      assertions.append(
          assert_util.assert_equal(
              tf.shape(input=param)[-1],
              1,
              message=("Currently only bimixtures are supported; "
                       "{}.shape[-1] is not 1.".format(name))))

    if assertions:
      return distribution_util.with_dependencies(assertions, param)
    return param