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 sampling works as expected
code: def test_sampling(self, torch_device, execute_kwargs):
      """Test sampling works as expected"""
      # pylint: disable=unused-argument
      if (
          execute_kwargs["gradient_fn"] == "device"
          and execute_kwargs["grad_on_execution"] is True
      ):
          pytest.skip(
              "Adjoint differentiation does not support samples"
          )
      if execute_kwargs["interface"] == "auto":
          pytest.skip(
              "Can't detect interface without a parametrized gate in the tape"
          )
  
      dev = qml.device("default.qubit.legacy", wires=2, shots=10)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.Hadamard(wires=[0])
          qml.CNOT(wires=[0, 1])
          qml.sample(qml.PauliZ(0))
          qml.sample(qml.PauliX(1))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      res = execute([tape], dev, **execute_kwargs)[0]
  
      assert isinstance(res, tuple)
      assert len(res) == 2
  
      assert isinstance(res[0], torch.Tensor)
      assert res[0].shape == (10,)
  
      assert isinstance(res[1], torch.Tensor)
      assert res[1].shape == (10,)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane.gradients import finite_diff, param_shift
  from pennylane import execute
label_desc: Use image node_id 12 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_sampling(torch_device, execute_kwargs) without return types