code: def testAssertsNonNegativeConcentration(self):
      concentration = tf.Variable(1.0)
      d = von_mises.VonMises(
          loc=0.0, concentration=concentration, validate_args=True
      )
      with self.assertRaisesOpError(
          "Argument `concentration` must be non-negative"
      ):
          self.evaluate([v.initializer for v in d.variables])
          with tf.control_dependencies([concentration.assign(-1.0)]):
              _ = self.evaluate(d.entropy())
  
imports: import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import von_mises
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 26 for calling the _VonMisesTest obj's underlying member method code with example usage: obj.testAssertsNonNegativeConcentration() without return types