class_docstr: Test the torch interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","torch","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), 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)
          return torch.hstack(
              [qml.math.asarray(r, like="torch") for r in res]
          )
  
      params = torch.tensor([0.1, 0.2], requires_grad=True)
      x, y = params.clone().detach()
  
      res = cost(params)
      assert isinstance(res, torch.Tensor)
      assert res.shape == (4,)
  
      assert torch.allclose(
          res[0],
          torch.cos(x) * torch.cos(y),
          atol=atol_for_shots(shots),
      )
      assert torch.allclose(
          res[1], torch.tensor(1.0), atol=atol_for_shots(shots)
      )
      assert torch.allclose(
          res[2],
          torch.cos(torch.tensor(0.5)),
          atol=atol_for_shots(shots),
      )
      assert torch.allclose(
          res[3],
          torch.cos(x) * torch.cos(y),
          atol=atol_for_shots(shots),
      )
  
      jac = torch.autograd.functional.jacobian(cost, params)
      assert isinstance(jac, torch.Tensor)
      assert jac.shape == (4, 2)  # pylint: disable=no-member
  
      assert torch.allclose(
          jac[1:3], torch.tensor(0.0), atol=atol_for_shots(shots)
      )
  
      d1 = -torch.sin(x) * torch.cos(y)
      assert torch.allclose(
          jac[0, 0], d1, atol=atol_for_shots(shots)
      )  # fails for torch
      assert torch.allclose(jac[3, 0], d1, atol=atol_for_shots(shots))
  
      d2 = -torch.cos(x) * torch.sin(y)
      assert torch.allclose(
          jac[0, 1], d2, atol=atol_for_shots(shots)
      )  # fails for torch
      assert torch.allclose(jac[3, 1], d2, atol=atol_for_shots(shots))
  
imports: import numpy
  import pytest
  from param_shift_dev import ParamShiftDerivativesDevice
  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 TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_tapes_with_different_return_size(execute_kwargs, shots, device) and returns: torch