class_docstr: Test the torch interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","torch","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Test re-using a quantum tape by passing new parameters
code: def test_reusing_quantum_tape(
      self, torch_device, execute_kwargs, tol
  ):
      """Test re-using a quantum tape by passing new parameters"""
      a = torch.tensor(0.1, requires_grad=True, device=torch_device)
      b = torch.tensor(0.2, requires_grad=True, device=torch_device)
  
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a, wires=0)
          qml.RX(b, wires=1)
          qml.CNOT(wires=[0, 1])
          qml.expval(qml.PauliZ(0))
          qml.expval(qml.PauliY(1))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      assert tape.trainable_params == [0, 1]
  
      res = execute([tape], dev, **execute_kwargs)[0]
      loss = res[0] + res[1]
      loss.backward()
  
      a_val = 0.54
      b_val = 0.8
      a = torch.tensor(a_val, requires_grad=True, device=torch_device)
      b = torch.tensor(b_val, requires_grad=True, device=torch_device)
  
      tape = tape.bind_new_parameters([2 * a, b], [0, 1])
      res2 = execute([tape], dev, **execute_kwargs)[0]
  
      expected = torch.tensor(
          [np.cos(2 * a_val), -np.cos(2 * a_val) * np.sin(b_val)],
          device=torch_device,
          dtype=res2[0].dtype,
      )
      assert torch.allclose(
          res2[0].detach(), expected[0], atol=tol, rtol=0
      )
      assert torch.allclose(
          res2[1].detach(), expected[1], atol=tol, rtol=0
      )
  
      loss = res2[0] + res2[1]
      loss.backward()
  
      expected = torch.tensor(
          [
              -2 * np.sin(2 * a_val)
              + 2 * np.sin(2 * a_val) * np.sin(b_val),
              -np.cos(2 * a_val) * np.cos(b_val),
          ],
          dtype=a.dtype,
          device=torch_device,
      )
  
      assert torch.allclose(a.grad, expected[0], atol=tol, rtol=0)
      assert torch.allclose(b.grad, expected[1], 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 5 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_reusing_quantum_tape(torch_device, execute_kwargs, tol) without return types