code: def testInverseLengthScaleValuesAreCorrect(self, feature_ndims, dims):
      amplitude = 5.0
      inverse_length_scale = np.float32([2.0, 3.0, 1.0, 0.5])
      power = np.float32([0.8, 0.2, 0.9, 0.7])
  
      np.random.seed(42)
      k = gamma_exponential.GammaExponential(
          amplitude,
          inverse_length_scale=inverse_length_scale,
          power=power,
          feature_ndims=feature_ndims,
      )
      k_with_ls = gamma_exponential.GammaExponential(
          amplitude,
          length_scale=1.0 / inverse_length_scale,
          power=power,
          feature_ndims=feature_ndims,
      )
      shape = [dims] * feature_ndims
      x = np.random.uniform(-1, 1, size=shape).astype(np.float32)
      y = np.random.uniform(-1, 1, size=shape).astype(np.float32)
      self.assertAllClose(
          self.evaluate(k.apply(x, y)),
          self.evaluate(k_with_ls.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 7 for calling the GammaExponentialTest obj's underlying member method code with example usage: obj.testInverseLengthScaleValuesAreCorrect(feature_ndims, dims) without return types