class_docstr: Unit tests for torch execution
class_docstr_tok: ["Unit","tests","for","torch","execution"]
docstr: Test that grad_on_execution uses the `device.execute_and_gradients` pathway
  while reusing the quantum state.
code: def test_grad_on_execution_reuse_state(self, interface, mocker):
      """Test that grad_on_execution uses the `device.execute_and_gradients` pathway
      while reusing the quantum state."""
      dev = qml.device("default.qubit.legacy", wires=1)
      spy = mocker.spy(dev, "execute_and_gradients")
  
      a = torch.tensor([0.1, 0.2], requires_grad=True)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a[0], wires=0)
          qml.RX(a[1], wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      execute(
          [tape],
          dev,
          gradient_fn="device",
          gradient_kwargs={
              "method": "adjoint_jacobian",
              "use_device_state": True,
          },
          interface=interface,
      )
  
      # adjoint method only performs a single device execution, but gets both result and gradient
      assert dev.num_executions == 1
      spy.assert_called()
  
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 TestTorchExecuteUnitTests obj's underlying member method code with example usage: obj.test_grad_on_execution_reuse_state(interface, mocker) without return types