code: def testVarianceAndCovarianceMatrix(self):
      amp = np.float64(0.5)
      len_scale = np.float64(0.2)
      jitter = np.float64(1e-4)
      observation_noise_variance = np.float64(3e-3)
  
      kernel = psd_kernels.ExponentiatedQuadratic(amp, len_scale)
  
      index_points = np.expand_dims(
          np.random.uniform(-1.0, 1.0, 10), -1
      )
  
      gp = gaussian_process.GaussianProcess(
          kernel,
          index_points,
          observation_noise_variance=observation_noise_variance,
          jitter=jitter,
          validate_args=True,
      )
  
      def _kernel_fn(x, y):
          return amp**2 * np.exp(
              -0.5 * (np.squeeze((x - y) ** 2)) / (len_scale**2)
          )
  
      expected_covariance = _kernel_fn(
          np.expand_dims(index_points, 0),
          np.expand_dims(index_points, 1),
      ) + observation_noise_variance * np.eye(10)
  
      self.assertAllClose(
          expected_covariance, self.evaluate(gp.covariance())
      )
      self.assertAllClose(
          np.diag(expected_covariance), self.evaluate(gp.variance())
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import psd_kernels
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 2 for calling the _GaussianProcessTest obj's underlying member method code with example usage: obj.testVarianceAndCovarianceMatrix() and returns: unknown