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 evaluation and Jacobian if there are no trainable parameters
code: def test_no_trainable_parameters(self, torch_device, execute_kwargs):
      """Test evaluation and Jacobian if there are no trainable parameters"""
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(0.2, wires=0)
          qml.RX(torch.tensor(0.1, device=torch_device), wires=0)
          qml.CNOT(wires=[0, 1])
          qml.expval(qml.PauliZ(0))
          qml.expval(qml.PauliZ(1))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      res = execute([tape], dev, **execute_kwargs)[0]
      assert tape.trainable_params == []
  
      assert isinstance(res, tuple)
      assert len(res) == 2
  
      assert isinstance(res[0], torch.Tensor)
      assert res[0].shape == ()
  
      assert isinstance(res[1], torch.Tensor)
      assert res[1].shape == ()
  
      with pytest.raises(
          RuntimeError,
          match="element 0 of tensors does not require grad and does not have a grad_fn",
      ):
          res[0].backward()
  
      with pytest.raises(
          RuntimeError,
          match="element 0 of tensors does not require grad and does not have a grad_fn",
      ):
          res[1].backward()
  
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 7 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_no_trainable_parameters(torch_device, execute_kwargs) without return types