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 jacobian calculation
docstr_tok: ["Test","jacobian","calculation"]
code: def test_jacobian(self, execute_kwargs, tol):
      """Test jacobian calculation"""
      a = tf.Variable(0.1, dtype=tf.float64)
      b = tf.Variable(0.2, 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.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)
          res = execute([tape], dev, max_diff=2, **execute_kwargs)[0]
          res = tf.stack(res)
  
      expected = [np.cos(a), -np.cos(a) * np.sin(b)]
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
      (agrad, bgrad) = t.jacobian(res, [a, b])
      assert agrad.shape == (2,)
      assert bgrad.shape == (2,)
  
      expected = [
          [-np.sin(a), np.sin(a) * np.sin(b)],
          [0, -np.cos(a) * np.cos(b)],
      ]
      assert np.allclose(expected, [agrad, bgrad], 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 3 for calling the TestTensorFlowExecuteIntegration obj's underlying member method code with example usage: obj.test_jacobian(execute_kwargs, tol) without return types