class_docstr: Test that the transform is differentiable
class_docstr_tok: ["Test","that","the","transform","is","differentiable"]
docstr: Tests that the output of the finite-difference transform
  of a ragged tape can be differentiated using autograd, yielding second derivatives.
code: def test_autograd_ragged(self, dev_name, approx_order, strategy, tol):
      """Tests that the output of the finite-difference transform
      of a ragged tape can be differentiated using autograd, yielding second derivatives.
      """
      dev = qml.device(dev_name, wires=2)
      execute_fn = (
          dev.execute
          if dev_name == "default.qubit"
          else dev.batch_execute
      )
      params = np.array([0.543, -0.654], requires_grad=True)
  
      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))
              qml.probs(wires=[1])
  
          tape = qml.tape.QuantumScript.from_queue(q)
          tape.trainable_params = {0, 1}
          tapes, fn = finite_diff(
              tape, n=1, approx_order=approx_order, strategy=strategy
          )
          jac = fn(execute_fn(tapes))
          return jac[1][0]
  
      x, y = params
      res = qml.jacobian(cost_fn)(params)[0]
      expected = np.array(
          [-np.cos(x) * np.cos(y) / 2, np.sin(x) * np.sin(y) / 2]
      )
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.devices import DefaultQubitLegacy
  from pennylane.gradients import finite_diff, finite_diff_coeffs
  from pennylane.operation import AnyWires, Observable
label_desc: Use image node_id 2 for calling the TestFiniteDiffGradients obj's underlying member method code with example usage: obj.test_autograd_ragged(dev_name, approx_order, strategy, tol) and returns: jac