class_docstr: Test that the TensorFlow execute function can be differentiated
class_docstr_tok: ["Test","that","the","TensorFlow","execute","function","can","be","differentiated"]
docstr: Since the adjoint hessian is not a differentiable transform,
  higher-order derivatives are not supported.
docstr_tok: ["Since","the","adjoint","hessian","is","not","a","differentiable","transform",",","higher-order","derivatives","are","not","supported","."]
code: def test_adjoint_hessian(self, interface):
      """Since the adjoint hessian is not a differentiable transform,
      higher-order derivatives are not supported."""
      dev = qml.device("default.qubit.legacy", wires=2)
      params = tf.Variable([0.543, -0.654])
  
      with tf.GradientTape() as t2:
          with tf.GradientTape() as t1:
              with qml.queuing.AnnotatedQueue() as q:
                  qml.RX(params[0], wires=[0])
                  qml.RY(params[1], wires=[1])
                  qml.CNOT(wires=[0, 1])
                  qml.expval(qml.PauliZ(0))
  
              tape = qml.tape.QuantumScript.from_queue(q)
              res = execute(
                  [tape],
                  dev,
                  gradient_fn="device",
                  gradient_kwargs={
                      "method": "adjoint_jacobian",
                      "use_device_state": True,
                  },
                  interface=interface,
              )[0]
  
          grad = t1.gradient(res, params)
          assert grad is not None
          assert grad.dtype == tf.float64
          assert grad.shape == params.shape
  
      hess = t2.jacobian(grad, params)
      assert hess is None
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane.gradients import finite_diff, param_shift
  from pennylane import execute
label_desc: Use image node_id 3 for calling the TestHigherOrderDerivatives obj's underlying member method code with example usage: obj.test_adjoint_hessian(interface) without return types