code: def testGradientComputesCorrectly(self):
      dtype = np.float32
  
      def fn(x, y):
          return x**2 + y**2
  
      fn_args = [dtype(3), dtype(3)]
      # Convert function input to a list of tensors
      fn_args = [tf.convert_to_tensor(value=arg) for arg in fn_args]
      fn_result, grads = util.maybe_call_fn_and_grads(fn, fn_args)
      fn_result_, grads_ = self.evaluate([fn_result, grads])
      self.assertNear(18.0, fn_result_, err=1e-5)
      for grad in grads_:
          self.assertAllClose(grad, dtype(6), atol=0.0, rtol=1e-5)
  
imports: import collections
  import warnings
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc.internal import util
label_desc: Use image node_id 1 for calling the GradientTest obj's underlying member method code with example usage: obj.testGradientComputesCorrectly() and returns: unknown