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):
      """Test evaluation and Jacobian if there are no trainable parameters"""
      b = tf.constant(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(0.2, wires=0)
              qml.RX(b, wires=0)
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0))
              qml.expval(qml.PauliZ(1))
  
          tape = qml.tape.QuantumScript.from_queue(q)
          res = execute([tape], dev, **execute_kwargs)[0]
          res = qml.math.stack(res)
  
      assert res.shape == (2,)
      assert isinstance(res, tf.Tensor)
  
      res = t.jacobian(res, b)
      assert res is None
  
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 8 for calling the TestTensorFlowExecuteIntegration obj's underlying member method code with example usage: obj.test_no_trainable_parameters(execute_kwargs) without return types