class_docstr: Test that using the QNode with Torch integrates with the PennyLane stack
class_docstr_tok: ["Test","that","using","the","QNode","with","Torch","integrates","with","the","PennyLane","stack"]
docstr: Test execution works with the interface
docstr_tok: ["Test","execution","works","with","the","interface"]
code: def test_execution_with_interface(
      self, interface, dev, diff_method, grad_on_execution, device_vjp
  ):
      """Test execution works with the interface"""
      if diff_method == "backprop":
          pytest.skip("Test does not support backprop")
  
      @qnode(
          dev,
          diff_method=diff_method,
          grad_on_execution=grad_on_execution,
          interface=interface,
          device_vjp=device_vjp,
      )
      def circuit(a):
          qml.RY(a, wires=0)
          qml.RX(0.2, wires=0)
          return qml.expval(qml.PauliZ(0))
  
      a = torch.tensor(0.1, requires_grad=True)
      res = circuit(a)
  
      assert circuit.interface == interface
  
      # with the interface, the tape returns torch tensors
  
      assert isinstance(res, torch.Tensor)
      assert res.shape == ()
  
      # the tape is able to deduce trainable parameters
      assert circuit.qtape.trainable_params == [0]
  
      # gradients should work
      res.backward()
      grad = a.grad
  
      assert isinstance(grad, torch.Tensor)
      assert grad.shape == ()
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import qnode
  from pennylane.devices import DefaultQubit
label_desc: Use image node_id 1 for calling the TestQNode obj's underlying member method code with example usage: obj.test_execution_with_interface(interface, dev, diff_method, grad_on_execution, device_vjp) and returns: qml