code: def testVarianceAndStd(self):
      sample = [[[0, 1], [1, 2]], [[0, 3], [3, 6]]]
  
      expected_variance = [[0, 1], [1, 4]]
  
      input_ = tf.convert_to_tensor(value=sample, dtype=np.float64)
      input_ph = tf1.placeholder_with_default(
          input_, shape=input_.shape if self.static_shape else None
      )
      dist = empirical.Empirical(
          samples=input_ph, event_ndims=2, validate_args=True
      )
      self.assertAllClose(
          self.evaluate(dist.variance()), expected_variance
      )
      self.assertAllClose(
          self.evaluate(dist.stddev()), np.sqrt(expected_variance)
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import empirical
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 10 for calling the EmpiricalNdTest obj's underlying member method code with example usage: obj.testVarianceAndStd() without return types