code: def test_batch_slice_precomputed_gprm(self):
      base_kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          length_scale=tf.linspace(tf.ones([]), 2.0, 64),
          feature_ndims=0,
      )
      x = tf.linspace(tf.zeros([]), 1.0, 126)
      y = tf.linspace(tf.zeros([]), 1.5, 162)
      d = gprm.GaussianProcessRegressionModel.precompute_regression_model(
          base_kernel,
          index_points=y,
          observation_index_points=x,
          observations=tf.math.sin(x),
          observation_noise_variance=1e-3,
      )
      self.assertEqual((64,), d.batch_shape)
      self.assertEqual((162,), d.event_shape)
      self.assertEqual(
          (
              64,
              162,
          ),
          d.sample(seed=test_util.test_seed()).shape,
      )
  
      self.assertEqual((), d[2].batch_shape)
      self.assertEqual((162,), d[2].event_shape)
      self.assertEqual(
          (162,), d[2].sample(seed=test_util.test_seed()).shape
      )
  
imports: from unittest import mock
  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.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exp_sin_squared
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 12 for calling the _GaussianProcessRegressionModelTest obj's underlying member method code with example usage: obj.test_batch_slice_precomputed_gprm() without return types