code: def testAssertionPositiveConcentrationAfterMutation(self):
      total_count = tf.constant(10.0)
      concentration = tf.Variable([1.0, 1.0, 1.0])
  
      d = dm.DirichletMultinomial(
          total_count, concentration, validate_args=True
      )
      self.evaluate([v.initializer for v in d.variables])
      self.evaluate(d.mean())
      self.evaluate(concentration.assign([1.0, 1.0, -1.0]))
  
      with self.assertRaisesOpError(
          "Concentration parameter must be positive."
      ):
          self.evaluate(d.mean())
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import dirichlet_multinomial
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 7 for calling the DirichletMultinomialFromVariableTest obj's underlying member method code with example usage: obj.testAssertionPositiveConcentrationAfterMutation() without return types