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 execution
docstr_tok: ["Test","execution"]
code: def test_execution(self, execute_kwargs, shots, device):
      """Test execution"""
  
      def cost(a, b):
          ops1 = [qml.RY(a, wires=0), qml.RX(b, wires=0)]
          tape1 = qml.tape.QuantumScript(
              ops1, [qml.expval(qml.PauliZ(0))], shots=shots
          )
  
          ops2 = [qml.RY(a, wires="a"), qml.RX(b, wires="a")]
          tape2 = qml.tape.QuantumScript(
              ops2, [qml.expval(qml.PauliZ("a"))], shots=shots
          )
  
          return execute([tape1, tape2], device, **execute_kwargs)
  
      a = torch.tensor(0.1, requires_grad=True)
      b = torch.tensor(0.2, requires_grad=False)
  
      with device.tracker:
          res = cost(a, b)
  
      if execute_kwargs.get("grad_on_execution", False):
          assert (
              device.tracker.totals["execute_and_derivative_batches"]
              == 1
          )
      else:
          assert device.tracker.totals["batches"] == 1
      assert (
          device.tracker.totals["executions"] == 2
      )  # different wires so different hashes
  
      assert len(res) == 2
      if not shots.has_partitioned_shots:
          assert res[0].shape == ()
          assert res[1].shape == ()
      exp = torch.cos(a) * torch.cos(b)
      if shots.has_partitioned_shots:
          for shot in range(2):
              for wire in range(2):
                  assert qml.math.allclose(
                      res[shot][wire], exp, atol=atol_for_shots(shots)
                  )
      else:
          for wire in range(2):
              assert qml.math.allclose(
                  res[wire], exp, 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 1 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_execution(execute_kwargs, shots, device) and returns: execute