code: def testAssertsPositiveDfAfterMutation(self):
      df = tf.Variable(3.0)
      scale = tf.linalg.LinearOperatorDiag(
          [2.0, 2.0], is_non_singular=True
      )
      self.evaluate(df.initializer)
      d = mvst.MultivariateStudentTLinearOperator(
          loc=1.0, df=df, scale=scale, validate_args=True
      )
      with self.assertRaisesOpError("`df` must be positive."):
          with tf.control_dependencies([df.assign(-2.0)]):
              self.evaluate(d.sample(seed=test_util.test_seed()))
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import multivariate_student_t
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 25 for calling the MultivariateStudentTTestFloat32StaticShape obj's underlying member method code with example usage: obj.testAssertsPositiveDfAfterMutation() without return types