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 the Torch interface continues to work after
  tape expansions
code: def test_repeated_application_after_expand(
      self, torch_device, execute_kwargs
  ):
      """Test that the Torch interface continues to work after
      tape expansions"""
      # pylint: disable=unused-argument
      n_qubits = 2
      dev = qml.device("default.qubit.legacy", wires=n_qubits)
  
      weights = torch.ones((3,))
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.U3(*weights, wires=0)
          qml.expval(qml.PauliZ(wires=0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      tape = tape.expand()
      execute([tape], dev, **execute_kwargs)
  
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 15 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_repeated_application_after_expand(torch_device, execute_kwargs) without return types