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 classical processing within the quantum tape
docstr_tok: ["Test","classical","processing","within","the","quantum","tape"]
code: def test_classical_processing(self, execute_kwargs):
      """Test classical processing within the quantum tape"""
      a = tf.Variable(0.1, dtype=tf.float64)
      b = tf.constant(0.2, dtype=tf.float64)
      c = tf.Variable(0.3, dtype=tf.float64)
  
      dev = qml.device("default.qubit.legacy", wires=1)
  
      with tf.GradientTape() as t:
          with qml.queuing.AnnotatedQueue() as q:
              qml.RY(a * c, wires=0)
              qml.RZ(b, wires=0)
              qml.RX(c + c**2 + tf.sin(a), wires=0)
              qml.expval(qml.PauliZ(0))
  
          tape = qml.tape.QuantumScript.from_queue(q)
          res = execute([tape], dev, **execute_kwargs)[0]
          assert tape.trainable_params == [0, 2]
          assert tape.get_parameters() == [
              a * c,
              c + c**2 + tf.sin(a),
          ]
  
      res = t.jacobian(res, [a, b, c])
      assert isinstance(res[0], tf.Tensor)
      assert res[1] is None
      assert isinstance(res[2], tf.Tensor)
  
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 TestTensorFlowExecuteIntegration obj's underlying member method code with example usage: obj.test_classical_processing(execute_kwargs) without return types