code: def testValuesAreCorrect(self, feature_ndims, dims):
      amplitude = 5.0
      length_scale = 0.2
      gamma = 0.85
  
      np.random.seed(42)
      k = gamma_exponential.GammaExponential(
          amplitude,
          length_scale=length_scale,
          feature_ndims=feature_ndims,
          power=gamma,
      )
      shape = [dims] * feature_ndims
      for _ in range(5):
          x = np.random.uniform(-1, 1, size=shape).astype(np.float32)
          y = np.random.uniform(-1, 1, size=shape).astype(np.float32)
          self.assertAllClose(
              amplitude**2
              * np.exp(
                  -(
                      (
                          np.sum((x - y) ** 2)
                          / (np.float32(2.0) * length_scale**2)
                      )
                      ** gamma
                  )
              ),
              self.evaluate(k.apply(x, y)),
          )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/gamma_exponential_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import gamma_exponential
label_desc: Use image node_id 2 for calling the GammaExponentialTest obj's underlying member method code with example usage: obj.testValuesAreCorrect(feature_ndims, dims) without return types