code: def test_tuple(self, solver):
      if not tf1.control_flow_v2_enabled():
          self.skipTest("b/152464477")
  
      alpha = np.float32(0.7)
      beta = np.float32(2.2)
  
      def ode_fn(time, state, alpha, beta):
          del time
          x, y = state
          return alpha * x, (alpha + beta) * y
  
      initial_time = 0.0
      initial_x = np.float32(3.2)
      initial_y = np.float32(-4.2)
      initial_state = (initial_x, initial_y)
      final_time = 0.5
  
      solver_instance = solver(rtol=_RTOL, atol=_ATOL)
  
      def grad_fn(alpha, beta):
          results = solver_instance.solve(
              ode_fn,
              initial_time,
              initial_state,
              solution_times=[final_time],
              constants={"alpha": alpha, "beta": beta},
          )
          final_state = results.states
          return final_state
  
      actual_grad_1 = self.evaluate(
          tfp_gradient.value_and_gradient(
              lambda alpha: grad_fn(alpha, beta)[0], alpha
          )[1]
      )
      actual_grad_2 = self.evaluate(
          tfp_gradient.value_and_gradient(
              lambda alpha: grad_fn(alpha, beta)[1], alpha
          )[1]
      )
      actual_grad_3 = self.evaluate(
          tfp_gradient.value_and_gradient(
              lambda beta: grad_fn(alpha, beta)[1], beta
          )[1]
      )
  
      expected_grad_1 = (
          initial_x * final_time * np.exp(alpha * final_time)
      )
      expected_grad_2 = (
          initial_y * final_time * np.exp((alpha + beta) * final_time)
      )
      expected_grad_3 = expected_grad_2
      self.assertAllClose(
          actual_grad_1, expected_grad_1, rtol=1e-4, atol=1e-4
      )
      self.assertAllClose(
          actual_grad_2, expected_grad_2, rtol=1e-4, atol=1e-4
      )
      self.assertAllClose(
          actual_grad_3, expected_grad_3, rtol=1e-4, atol=1e-4
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.ode import base
  from tensorflow_probability.python.math.ode import bdf
  from tensorflow_probability.python.math.ode import dormand_prince
label_desc: Use image node_id 2 for calling the GradientTestPforJacobian obj's underlying member method code with example usage: obj.test_tuple(solver) and returns: unknown,  unknown, final_state