class_docstr: Tests for adjoint_jacobian
docstr: Test that a parametrized `QubitUnitary` is accounted for correctly
  when it is not trainable.
docstr_tok: ["Test","that","a","parametrized","`","QubitUnitary","`","is","accounted","for","correctly","when","it","is","not","trainable","."]
code: def test_with_nontrainable_parametrized(self):
      """Test that a parametrized `QubitUnitary` is accounted for correctly
      when it is not trainable."""
  
      par = np.array(0.6)
  
      ops = [
          qml.RY(par, wires=0),
          qml.QubitUnitary(np.eye(2), wires=0),
      ]
      qs = QuantumScript(
          ops, [qml.expval(qml.PauliZ(0))], trainable_params=[0]
      )
  
      grad_adjoint = adjoint_jacobian(qs)
      expected = [-np.sin(par)]
      assert np.allclose(grad_adjoint, expected)
  
imports: import pytest
  import pennylane
  from pennylane.devices.qubit import adjoint_jacobian, adjoint_jvp, adjoint_vjp
  from pennylane.tape import QuantumScript
  import pennylane.numpy
label_desc: Use image node_id 11 for calling the TestAdjointJacobian obj's underlying member method code with example usage: obj.test_with_nontrainable_parametrized() without return types