code: def testVarianceStdAllDefined(
      self, diag=None, full=None, update=None, expected_mvn_var=None
  ):
      if diag is not None:
          scale = tf.linalg.LinearOperatorDiag(
              self._input(diag), is_non_singular=True
          )
      elif full is not None:
          scale = tf.linalg.LinearOperatorFullMatrix(
              self._input(full), is_non_singular=True
          )
      if update is not None:
          scale = tf.linalg.LinearOperatorLowRankUpdate(
              scale, self._input(update), is_non_singular=True
          )
  
      dist = mvst.MultivariateStudentTLinearOperator(
          loc=self._input([0.0, 0.0]),
          df=self._input(3.0),
          scale=scale,
          validate_args=True,
      )
      var = self.evaluate(dist.variance())
      std = self.evaluate(dist.stddev())
      # df = 3, so we expect the variance of the MVT to exceed MVN by a factor of
      # 3 / (3 - 2) = 3.
      self.assertAllClose(np.array(expected_mvn_var) * 3.0, var)
      self.assertAllClose(
          np.sqrt(np.array(expected_mvn_var) * 3.0), std
      )
  
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 13 for calling the MultivariateStudentTTestFloat32StaticShape obj's underlying member method code with example usage: obj.testVarianceStdAllDefined(diag, full, update, expected_mvn_var) without return types