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, device, shots):
      """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)
  
      def cost(a, b, c):
          ops = [
              qml.RY(a * c, wires=0),
              qml.RZ(b, wires=0),
              qml.RX(c + c**2 + tf.sin(a), wires=0),
          ]
  
          tape = qml.tape.QuantumScript(
              ops, [qml.expval(qml.PauliZ(0))], shots=shots
          )
          return execute([tape], device, **execute_kwargs)[0]
  
      with tf.GradientTape() as tape:
          cost_res = cost(a, b, c)
  
      res = tape.jacobian(cost_res, [a, c])
  
      # Only two arguments are trainable
      assert isinstance(res, list) and len(res) == 2
      assert res[0].shape == ()
      assert res[1].shape == ()
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.devices import DefaultQubit
  from pennylane.gradients import 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, device, shots) and returns: execute