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 classical processing within the quantum tape
docstr_tok: ["Test","classical","processing","within","the","quantum","tape"]
code: def test_classical_processing(self, execute_kwargs, device, shots):
      """Test classical processing within the quantum tape"""
      a = torch.tensor(0.1, requires_grad=True)
      b = torch.tensor(0.2, requires_grad=False)
      c = torch.tensor(0.3, requires_grad=True)
  
      def cost(a, b, c):
          ops = [
              qml.RY(a * c, wires=0),
              qml.RZ(b, wires=0),
              qml.RX(c + c**2 + torch.sin(a), wires=0),
          ]
  
          tape = qml.tape.QuantumScript(
              ops, [qml.expval(qml.PauliZ(0))], shots=shots
          )
          return execute([tape], device, **execute_kwargs)[0]
  
      # PyTorch docs suggest a lambda for cost functions with some non-trainable args
      # See for more: https://pytorch.org/docs/stable/autograd.html#functional-higher-level-api
      res = torch.autograd.functional.jacobian(
          lambda _a, _c: cost(_a, b, _c), (a, c)
      )
  
      # Only two arguments are trainable
      assert isinstance(res, tuple) and len(res) == 2
      if not shots.has_partitioned_shots:
          assert res[0].shape == ()
          assert res[1].shape == ()
  
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 7 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_classical_processing(execute_kwargs, device, shots) and returns: execute