code: def testVarianceAndStd(self):
      samples = [[[0, 1], [2, 3]], [[[1, 2], [1, 4]], [[1, 2], [3, 6]]]]
      expected_variances = [[1, 1], [[0, 1], [1, 4]]]
  
      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, event_ndims=1, 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 EmpiricalVectorTest obj's underlying member method code with example usage: obj.testVarianceAndStd() without return types