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: Tests correct output shape and evaluation for a tape
  with prob and expval outputs
docstr_tok: ["Tests","correct","output","shape","and","evaluation","for","a","tape","with","prob","and","expval","outputs"]
code: def test_ragged_differentiation(self, execute_kwargs, device, shots):
      """Tests correct output shape and evaluation for a tape
      with prob and expval outputs"""
  
      def cost(x, y):
          ops = [qml.RX(x, wires=0), qml.RY(y, 1), qml.CNOT((0, 1))]
          m = [qml.expval(qml.PauliZ(0)), qml.probs(wires=1)]
          tape = qml.tape.QuantumScript(ops, m)
          return torch.hstack(
              execute([tape], device, **execute_kwargs)[0]
          )
  
      x = torch.tensor(0.543, requires_grad=True)
      y = torch.tensor(-0.654, requires_grad=True)
  
      res = cost(x, y)
      expected = torch.tensor(
          [
              torch.cos(x),
              (1 + torch.cos(x) * torch.cos(y)) / 2,
              (1 - torch.cos(x) * torch.cos(y)) / 2,
          ]
      )
      assert torch.allclose(
          res, expected, atol=atol_for_shots(shots), rtol=0
      )
  
      res = torch.autograd.functional.jacobian(cost, (x, y))
      assert isinstance(res, tuple) and len(res) == 2
      assert res[0].shape == (3,)
      assert res[1].shape == (3,)
  
      expected = (
          torch.tensor(
              [
                  -torch.sin(x),
                  -torch.sin(x) * torch.cos(y) / 2,
                  torch.sin(x) * torch.cos(y) / 2,
              ]
          ),
          torch.tensor(
              [
                  0,
                  -torch.cos(x) * torch.sin(y) / 2,
                  torch.cos(x) * torch.sin(y) / 2,
              ]
          ),
      )
      assert torch.allclose(
          res[0], expected[0], atol=atol_for_shots(shots), rtol=0
      )
      assert torch.allclose(
          res[1], expected[1], atol=atol_for_shots(shots), rtol=0
      )
  
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 12 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_ragged_differentiation(execute_kwargs, device, shots) and returns: torch