code: def test_integer_running_covariance(self):
      cov = sample_stats.RunningCovariance.from_shape(
          shape=(), dtype=tf.int32
      )
      for sample in range(5):
          cov = cov.update(sample)
      final_cov = cov.covariance()
      # all int dtypes are converted to `tf.float32`
      self.assertEqual(tf.float32, cov.mean.dtype)
      self.assertEqual(tf.float32, final_cov.dtype)
      final_mean, final_cov = self.evaluate([cov.mean, final_cov])
      self.assertNear(2, final_mean, err=1e-6)
      self.assertNear(2, final_cov, err=1e-6)
  
imports: from absl.testing import parameterized
  import numpy
  import scipy.stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.stats import sample_stats
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import diagnostic
label_desc: Use image node_id 3 for calling the RunningCovarianceTest obj's underlying member method code with example usage: obj.test_integer_running_covariance() without return types