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 that the execute function produces results with the expected shapes
code: def test_execution(self, torch_device, execute_kwargs):
      """Test that the execute function produces results with the expected shapes"""
      dev = qml.device("default.qubit.legacy", wires=1)
      a = torch.tensor(0.1, requires_grad=True, device=torch_device)
      b = torch.tensor(0.2, requires_grad=False, device=torch_device)
  
      with qml.queuing.AnnotatedQueue() as q1:
          qml.RY(a, wires=0)
          qml.RX(b, wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape1 = qml.tape.QuantumScript.from_queue(q1)
  
      with qml.queuing.AnnotatedQueue() as q2:
          qml.RY(a, wires=0)
          qml.RX(b, wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape2 = qml.tape.QuantumScript.from_queue(q2)
  
      res = execute([tape1, tape2], dev, **execute_kwargs)
  
      assert isinstance(res, list)
      assert len(res) == 2
      assert res[0].shape == ()
      assert res[1].shape == ()
  
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 1 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_execution(torch_device, execute_kwargs) without return types