class_docstr: Test that the torch execute function can be differentiated
class_docstr_tok: ["Test","that","the","torch","execute","function","can","be","differentiated"]
docstr: Since the adjoint hessian is not a differentiable transform,
  higher-order derivatives are not supported.
code: def test_adjoint_hessian(self, torch_device, tol):
      """Since the adjoint hessian is not a differentiable transform,
      higher-order derivatives are not supported."""
      dev = qml.device("default.qubit.legacy", wires=2)
      params = torch.tensor(
          [0.543, -0.654],
          requires_grad=True,
          dtype=torch.float64,
          device=torch_device,
      )
  
      def cost_fn(x):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RX(x[0], wires=[0])
              qml.RY(x[1], wires=[1])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0))
  
          tape = qml.tape.QuantumScript.from_queue(q)
  
          return execute(
              [tape],
              dev,
              gradient_fn="device",
              gradient_kwargs={
                  "method": "adjoint_jacobian",
                  "use_device_state": True,
              },
              interface="torch",
          )[0]
  
      res = torch.autograd.functional.hessian(cost_fn, params)
      expected = torch.zeros(
          [2, 2], dtype=torch.float64, device=torch_device
      )
      assert torch.allclose(res, expected, atol=tol, rtol=0)
  
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(torch_device, tol) and returns: execute