tensorflow_probability/python/distributions/dirichlet.py
def _maybe_assert_valid_concentration(self, concentration, validate_args):
    """Checks the validity of the concentration parameter."""
    if not validate_args:
      return concentration
    return distribution_util.with_dependencies([
        assert_util.assert_positive(
            concentration, message="Concentration parameter must be positive."),
        assert_util.assert_rank_at_least(
            concentration,
            1,
            message="Concentration parameter must have >=1 dimensions."),
        assert_util.assert_less(
            1,
            tf.shape(input=concentration)[-1],
            message="Concentration parameter must have event_size >= 2."),
    ], concentration)
def _maybe_assert_valid_concentration(self, concentration, validate_args):
    """Checks the validity of the concentration parameter."""
    if not validate_args:
      return concentration
    return distribution_util.with_dependencies([
        assert_util.assert_positive(
            concentration, message="Concentration parameter must be positive."),
        assert_util.assert_rank_at_least(
            concentration,
            1,
            message="Concentration parameter must have >=1 dimensions."),
        assert_util.assert_less(
            1,
            tf.shape(input=concentration)[-1],
            message="Concentration parameter must have event_size >= 2."),
    ], concentration)
Checks the validity of the concentration parameter.