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 that tapes wit different can be executed and differentiated.
docstr_tok: ["Test","that","tapes","wit","different","can","be","executed","and","differentiated","."]
code: def test_tapes_with_different_return_size(
      self, execute_kwargs, shots, device
  ):
      """Test that tapes wit different can be executed and differentiated."""
  
      if execute_kwargs["gradient_fn"] == "backprop":
          pytest.xfail(
              "backprop is not compatible with something about this situation."
          )
  
      def cost(params):
          tape1 = qml.tape.QuantumScript(
              [qml.RY(params[0], 0), qml.RX(params[1], 0)],
              [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))],
              shots=shots,
          )
  
          tape2 = qml.tape.QuantumScript(
              [qml.RY(np.array(0.5, requires_grad=False), 0)],
              [qml.expval(qml.PauliZ(0))],
              shots=shots,
          )
  
          tape3 = qml.tape.QuantumScript(
              [qml.RY(params[0], 0), qml.RX(params[1], 0)],
              [qml.expval(qml.PauliZ(0))],
              shots=shots,
          )
          res = execute([tape1, tape2, tape3], device, **execute_kwargs)
          if shots.has_partitioned_shots:
              res = tuple(i for r in res for i in r)
          return autograd.numpy.hstack(res)
  
      params = np.array([0.1, 0.2], requires_grad=True)
      x, y = params
  
      res = cost(params)
      assert isinstance(res, np.ndarray)
      if not shots:
          assert res.shape == (4,)
  
      if shots.has_partitioned_shots:
          for i in (0, 1):
              assert np.allclose(
                  res[2 * i],
                  np.cos(x) * np.cos(y),
                  atol=atol_for_shots(shots),
              )
              assert np.allclose(
                  res[2 * i + 1], 1, atol=atol_for_shots(shots)
              )
              assert np.allclose(
                  res[4 + i], np.cos(0.5), atol=atol_for_shots(shots)
              )
              assert np.allclose(
                  res[6 + i],
                  np.cos(x) * np.cos(y),
                  atol=atol_for_shots(shots),
              )
      else:
          assert np.allclose(
              res[0], np.cos(x) * np.cos(y), atol=atol_for_shots(shots)
          )
          assert np.allclose(res[1], 1, atol=atol_for_shots(shots))
          assert np.allclose(
              res[2], np.cos(0.5), atol=atol_for_shots(shots)
          )
          assert np.allclose(
              res[3], np.cos(x) * np.cos(y), atol=atol_for_shots(shots)
          )
  
      if shots.has_partitioned_shots:
          pytest.xfail(
              "autograd jacobians do not work with ragged results and shot vectors."
          )
          # TODO: autograd jacobians with ragged results and shot vectors
      jac = qml.jacobian(cost)(params)
      assert isinstance(jac, np.ndarray)
      if not shots.has_partitioned_shots:
          assert jac.shape == (4, 2)  # pylint: disable=no-member
  
      d1 = -np.sin(x) * np.cos(y)
      d2 = -np.cos(x) * np.sin(y)
  
      if shots.has_partitioned_shots:
          assert np.allclose(jac[1], 0, atol=atol_for_shots(shots))
          assert np.allclose(jac[3:4], 0, atol=atol_for_shots(shots))
  
          assert np.allclose(jac[0, 0], d1, atol=atol_for_shots(shots))
          assert np.allclose(jac[2, 0], d1, atol=atol_for_shots(shots))
  
          assert np.allclose(jac[6, 0], d1, atol=atol_for_shots(shots))
          assert np.allclose(jac[7, 0], d1, atol=atol_for_shots(shots))
  
          assert np.allclose(jac[0, 1], d2, atol=atol_for_shots(shots))
          assert np.allclose(jac[6, 1], d2, atol=atol_for_shots(shots))
          assert np.allclose(jac[7, 1], d2, atol=atol_for_shots(shots))
  
      else:
          assert np.allclose(jac[1:3], 0, atol=atol_for_shots(shots))
  
          assert np.allclose(jac[0, 0], d1, atol=atol_for_shots(shots))
          assert np.allclose(jac[3, 0], d1, atol=atol_for_shots(shots))
  
          assert np.allclose(jac[0, 1], d2, atol=atol_for_shots(shots))
          assert np.allclose(jac[3, 1], d2, atol=atol_for_shots(shots))
  
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 5 for calling the TestAutogradExecuteIntegration obj's underlying member method code with example usage: obj.test_tapes_with_different_return_size(execute_kwargs, shots, device) and returns: autograd