code: def testLogProb(self):
      # Test that numerically integrating over some portion of the domain yields a
      # normalization constant of close to 1.
      # pyformat: disable
      scale = tf.linalg.LinearOperatorFullMatrix(
          self._input([[1.0, -0.5], [-0.5, 1.0]]), is_non_singular=True
      )
      # pyformat: enable
      dist = mvst.MultivariateStudentTLinearOperator(
          loc=self._input([1.0, 1.0]),
          df=self._input(5.0),
          scale=scale,
          validate_args=True,
      )
  
      spacings = tf.cast(tf.linspace(-20.0, 20.0, 100), self.dtype)
      x, y = tf.meshgrid(spacings, spacings)
      points = tf.concat([x[..., tf.newaxis], y[..., tf.newaxis]], -1)
      log_probs = dist.log_prob(points)
      normalization = (
          tf.exp(tf.reduce_logsumexp(log_probs))
          * (spacings[1] - spacings[0]) ** 2
      )
      self.assertAllClose(1.0, self.evaluate(normalization), atol=1e-3)
      self.assertDTypeEqual(log_probs, self.dtype)
  
      mode_log_prob = dist.log_prob(dist.mode())
      self.assertTrue(np.all(self.evaluate(mode_log_prob >= log_probs)))
  
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 21 for calling the MultivariateStudentTTestFloat32StaticShape obj's underlying member method code with example usage: obj.testLogProb() without return types