code: def testVariableScaleWithDeferredTensor(self):
      scale = tf.linalg.LinearOperatorDiag(
          deferred_tensor.TransformedVariable([2.0, 2.0], tfb.Exp()),
          is_non_singular=True,
      )
      self.evaluate([v.initializer for v in scale.trainable_variables])
      d = mvst.MultivariateStudentTLinearOperator(
          loc=1.0, df=3.0, scale=scale, validate_args=True
      )
      with tf.GradientTape() as tape:
          lp = d.log_prob(d.sample(seed=test_util.test_seed()))
      self.assertLen(d.trainable_variables, 1)
      self.assertIsNotNone(tape.gradient(lp, d.trainable_variables))
  
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 26 for calling the MultivariateStudentTTestFloat32StaticShape obj's underlying member method code with example usage: obj.testVariableScaleWithDeferredTensor() without return types