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 scalar jacobian calculation by comparing two types of pipelines
code: def test_scalar_jacobian(self, torch_device, execute_kwargs, tol):
      """Test scalar jacobian calculation by comparing two types of pipelines"""
      a = torch.tensor(
          0.1,
          requires_grad=True,
          dtype=torch.float64,
          device=torch_device,
      )
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a, wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      res = execute([tape], dev, **execute_kwargs)[0]
      res.backward()
  
      # compare to backprop gradient
      def cost(a):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RY(a, wires=0)
              qml.expval(qml.PauliZ(0))
  
          tape = qml.tape.QuantumScript.from_queue(q)
  
          dev = qml.device("default.qubit.autograd", wires=2)
          return dev.batch_execute([tape])[0]
  
      expected = qml.grad(cost, argnum=0)(0.1)
      assert torch.allclose(
          a.grad,
          torch.tensor(expected, device=torch_device),
          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 2 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_scalar_jacobian(torch_device, execute_kwargs, tol) and returns: dev