class_docstr: Test the TensorFlow interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","TensorFlow","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Test scalar jacobian calculation
docstr_tok: ["Test","scalar","jacobian","calculation"]
code: def test_scalar_jacobian(self, execute_kwargs, tol):
      """Test scalar jacobian calculation"""
      a = tf.Variable(0.1, dtype=tf.float64)
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with tf.GradientTape() as t:
          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 = t.jacobian(res, a)
      assert res.shape == ()
  
      # compare to standard tape jacobian
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a, wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
      tape.trainable_params = [0]
      tapes, fn = param_shift(tape)
      expected = fn(dev.batch_execute(tapes))
  
      assert expected.shape == ()
      assert np.allclose(res, expected, 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 TestTensorFlowExecuteIntegration obj's underlying member method code with example usage: obj.test_scalar_jacobian(execute_kwargs, tol) without return types