class_docstr: Tests for the finite difference gradient transform
class_docstr_tok: ["Tests","for","the","finite","difference","gradient","transform"]
docstr: Test that the correct ouput and warning is generated in the absence of any trainable
  parameters
code: def test_no_trainable_params_tape(self):
      """Test that the correct ouput and warning is generated in the absence of any trainable
      parameters"""
      dev = qml.device("default.qubit", wires=2)
  
      weights = [0.1, 0.2]
      with qml.queuing.AnnotatedQueue() as q:
          qml.RX(weights[0], wires=0)
          qml.RY(weights[1], wires=0)
          qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
  
      tape = qml.tape.QuantumScript.from_queue(q)
      # TODO: remove once #2155 is resolved
      tape.trainable_params = []
      with pytest.warns(
          UserWarning,
          match="gradient of a tape with no trainable parameters",
      ):
          g_tapes, post_processing = qml.gradients.finite_diff(tape)
      res = post_processing(qml.execute(g_tapes, dev, None))
  
      assert g_tapes == []
      assert isinstance(res, numpy.ndarray)
      assert res.shape == (0,)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.devices import DefaultQubitLegacy
  from pennylane.gradients import finite_diff, finite_diff_coeffs
  from pennylane.operation import AnyWires, Observable
label_desc: Use image node_id 4 for calling the TestFiniteDiff obj's underlying member method code with example usage: obj.test_no_trainable_params_tape() without return types