code: def testCovarianceAllDefined(
      self, diag=None, full=None, expected_mvn_cov=None
  ):
      if diag is not None:
          scale = tf.linalg.LinearOperatorDiag(
              self._input(diag), is_non_singular=True
          )
      else:
          scale = tf.linalg.LinearOperatorFullMatrix(
              self._input(full), is_non_singular=True
          )
      dist = mvst.MultivariateStudentTLinearOperator(
          loc=self._input([0.0, 0.0]),
          df=self._input(3.0),
          scale=scale,
          validate_args=True,
      )
      cov = self.evaluate(dist.covariance())
      self.assertAllClose(
          np.array(expected_mvn_cov) * 3.0 / (3.0 - 2.0), cov
      )
  
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 10 for calling the MultivariateStudentTTestFloat32StaticShape obj's underlying member method code with example usage: obj.testCovarianceAllDefined(diag, full, expected_mvn_cov) without return types