class_docstr: Test the autograd interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","autograd","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 = np.array(0.1, requires_grad=False)
      b = np.array(0.2, requires_grad=False)
  
      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 autograd.numpy.hstack(
              execute([tape], device, **execute_kwargs)[0]
          )
  
      res = cost(a, b)
      assert res.shape == (2 * shots.num_copies,) if shots else (2,)
  
      with pytest.warns(
          UserWarning,
          match="Attempted to differentiate a function with no",
      ):
          res = qml.jacobian(cost)(a, b)
      assert len(res) == 0
  
      def loss(a, b):
          return np.sum(cost(a, b))
  
      with pytest.warns(
          UserWarning,
          match="Attempted to differentiate a function with no",
      ):
          res = qml.grad(loss)(a, b)
  
      assert np.allclose(res, 0)
  
imports: import autograd
  import pytest
  from param_shift_dev import ParamShiftDerivativesDevice
  from pennylane import numpy
  import pennylane
  from pennylane.devices import DefaultQubit
  from pennylane.gradients import param_shift
  from pennylane import execute
  from pennylane.measurements import Shots
label_desc: Use image node_id 8 for calling the TestAutogradExecuteIntegration obj's underlying member method code with example usage: obj.test_no_trainable_parameters(execute_kwargs, shots, device) and returns: autograd, np