class_docstr: Test for the dispatching logic of the parameter shift method
docstr: Test that the correct ouput and warning is generated in the absence of any trainable
  parameters
docstr_tok: ["Test","that","the","correct","ouput","and","warning","is","generated","in","the","absence","of","any","trainable","parameters"]
code: def test_no_trainable_params_qnode_tf(self):
      """Test that the correct ouput and warning is generated in the absence of any trainable
      parameters"""
  
      dev = qml.device("default.gaussian", wires=2)
  
      @qml.qnode(dev, interface="tf")
      def circuit(weights):
          qml.Displacement(weights[0], 0.0, wires=[0])
          qml.Rotation(weights[1], wires=[0])
          return qml.expval(qml.QuadX(0))
  
      weights = [0.1, 0.2]
      with pytest.raises(
          qml.QuantumFunctionError, match="No trainable parameters."
      ):
          qml.gradients.param_shift_cv(circuit)(weights)
  
imports: import unittest.mock
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients import param_shift_cv
  from pennylane.gradients.gradient_transform import choose_trainable_params
  from pennylane.gradients.parameter_shift_cv import _grad_method_cv, _find_gradient_methods_cv, _transform_observable
label_desc: Use image node_id 3 for calling the TestParameterShiftLogic obj's underlying member method code with example usage: obj.test_no_trainable_params_qnode_tf() and returns: qml