code: def testVarianceAndStd(self):
      samples = [[0, 1, 1, 2], [[1, 3], [2, 6]]]
      expected_variances = [
          0.5,
          [1.0, 4.0],
      ]
  
      for sample, expected_variance in zip(samples, expected_variances):
          input_ = tf.convert_to_tensor(value=sample, dtype=np.float32)
          input_ph = tf1.placeholder_with_default(
              input_, shape=input_.shape if self.static_shape else None
          )
          dist = empirical.Empirical(
              samples=input_ph, 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 11 for calling the EmpiricalScalarTest obj's underlying member method code with example usage: obj.testVarianceAndStd() without return types