code: def testMatrixValuesAreCorrect(
      self, x_batch, y_batch, dim, amplitudes
  ):
      amplitudes = np.array(amplitudes).astype(np.float32)
  
      length_scale = tf.random.stateless_uniform(
          [dim],
          seed=test_util.test_seed(sampler_type="stateless"),
          minval=0.0,
          maxval=2.0,
          dtype=tf.float32,
      )
      inner_kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amplitude=1.0, length_scale=length_scale
      )
      kernel = additive_kernel.AdditiveKernel(
          kernel=inner_kernel, amplitudes=amplitudes
      )
  
      x = tf.random.stateless_uniform(
          x_batch + [3, dim],
          seed=test_util.test_seed(sampler_type="stateless"),
          minval=-2.0,
          maxval=2.0,
          dtype=tf.float32,
      )
      y = tf.random.stateless_uniform(
          y_batch + [5, dim],
          seed=test_util.test_seed(sampler_type="stateless"),
          minval=-2.0,
          maxval=2.0,
          dtype=tf.float32,
      )
  
      actual = kernel.matrix(x, y)
  
      expected = self._compute_additive_kernel(
          amplitudes, length_scale, dim, x, y, method="matrix"
      )
  
      self.assertAllClose(
          self.evaluate(actual), self.evaluate(expected), rtol=3e-5
      )
  
file_path: probability/tensorflow_probability/python/experimental/psd_kernels/additive_kernel_test.py
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.psd_kernels import additive_kernel
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 4 for calling the AdditiveKernelTest obj's underlying member method code with example usage: obj.testMatrixValuesAreCorrect(x_batch, y_batch, dim, amplitudes) without return types