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 evaluation and Jacobian if there are no trainable parameters
docstr_tok: ["Test","evaluation","and","Jacobian","if","there","are","no","trainable","parameters"]
code: def test_no_trainable_parameters(self, execute_kwargs, shots, device):
      """Test evaluation and Jacobian if there are no trainable parameters"""
      a = tf.constant(0.1)
      b = tf.constant(0.2)
  
      def cost(a, b):
          ops = [qml.RY(a, 0), qml.RX(b, 0), qml.CNOT((0, 1))]
          m = [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))]
          tape = qml.tape.QuantumScript(ops, m, shots=shots)
          return qml.math.hstack(
              execute([tape], device, **execute_kwargs)[0],
              like="tensorflow",
          )
  
      with tf.GradientTape() as tape:
          cost_res = cost(a, b)
  
      assert cost_res.shape == (2,)
  
      res = tape.jacobian(cost_res, [a, b])
      assert len(res) == 2
      assert all(r is None for r in res)
  
      def loss(a, b):
          return tf.reduce_sum(cost(a, b))
  
      with tf.GradientTape() as tape:
          loss_res = loss(a, b)
  
      res = tape.gradient(loss_res, [a, b])
      assert all(r is None for r in res)
  
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 8 for calling the TestTensorflowExecuteIntegration obj's underlying member method code with example usage: obj.test_no_trainable_parameters(execute_kwargs, shots, device) and returns: qml, tf