class_docstr: Test that differentiating edge case tapes with ``pulse_odegen`` works.
class_docstr_tok: ["Test","that","differentiating","edge","case","tapes","with","``","pulse_odegen","``","works","."]
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_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(Z(0) @ Z(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 = pulse_odegen(tape)
      res = post_processing(qml.execute(g_tapes, dev, None))
  
      assert g_tapes == []
      assert isinstance(res, np.ndarray)
      assert res.shape == (0,)
  
imports: import copy
  import pytest
  import numpy
  import pennylane
  from pennylane import numpy
  from pennylane.ops.qubit.special_unitary import pauli_basis_matrices, pauli_basis_strings
  from pennylane.math import expand_matrix
  from pennylane.gradients.pulse_gradient_odegen import _generate_tapes_and_coeffs, pulse_odegen, _insert_op, _nonzero_coeffs_and_words, _one_parameter_generators, _one_parameter_paulirot_coeffs, _parshift_and_contract
label_desc: Use image node_id 5 for calling the TestPulseOdegenEdgeCases obj's underlying member method code with example usage: obj.test_no_trainable_params_tape() without return types