code: def testCholeskyLikeMarginalFn(self):
      fake_input = np.array([[1.0, 2.0], [2.0, 1.0]], dtype=np.float32)
      fake_output = np.array([[2.0, 1.0], [1.0, 2.0]], dtype=np.float32)
      fake_chol = np.linalg.cholesky(fake_output)
      marginal_fn = marginal_fns.make_cholesky_like_marginal_fn(
          lambda x: tf.convert_to_tensor(fake_chol)
      )
      test = self.evaluate(
          marginal_fn(tf.zeros(2), fake_input).covariance()
      )
      self.assertAllClose(test, fake_output)
  
file_path: probability/tensorflow_probability/python/experimental/distributions/marginal_fns_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.distributions import marginal_fns
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 6 for calling the MarginalFnsTest obj's underlying member method code with example usage: obj.testCholeskyLikeMarginalFn() without return types