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 re-using a quantum tape by passing new parameters
docstr_tok: ["Test","re-using","a","quantum","tape","by","passing","new","parameters"]
code: def test_reusing_quantum_tape(self, execute_kwargs, shots, device):
      """Test re-using a quantum tape by passing new parameters"""
      a = torch.tensor(0.1, requires_grad=True)
      b = torch.tensor(0.2, requires_grad=True)
  
      tape = qml.tape.QuantumScript(
          [qml.RY(a, 0), qml.RX(b, 1), qml.CNOT((0, 1))],
          [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))],
      )
      assert tape.trainable_params == [0, 1]
  
      def cost(a, b):
          new_tape = tape.bind_new_parameters([a, b], [0, 1])
          return torch.hstack(
              execute([new_tape], device, **execute_kwargs)[0]
          )
  
      jac = torch.autograd.functional.jacobian(cost, (a, b))
  
      a = torch.tensor(0.54, requires_grad=True)
      b = torch.tensor(0.8, requires_grad=True)
  
      # check that the cost function continues to depend on the
      # values of the parameters for subsequent calls
      res2 = cost(2 * a, b)
      expected = torch.tensor(
          [torch.cos(2 * a), -torch.cos(2 * a) * torch.sin(b)]
      )
      assert torch.allclose(
          res2, expected, atol=atol_for_shots(shots), rtol=0
      )
  
      jac = torch.autograd.functional.jacobian(
          lambda a, b: cost(2 * a, b), (a, b)
      )
      expected = (
          torch.tensor(
              [
                  -2 * torch.sin(2 * a),
                  2 * torch.sin(2 * a) * torch.sin(b),
              ]
          ),
          torch.tensor([0, -torch.cos(2 * a) * torch.cos(b)]),
      )
      assert isinstance(jac, tuple) and len(jac) == 2
      for _j, _e in zip(jac, expected):
          assert torch.allclose(
              _j, _e, 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 6 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_reusing_quantum_tape(execute_kwargs, shots, device) and returns: torch