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 a tape with no parameters is correctly
  ignored during the gradient computation
docstr_tok: ["Test","that","a","tape","with","no","parameters","is","correctly","ignored","during","the","gradient","computation"]
code: def test_tape_no_parameters(self, execute_kwargs, shots, device):
      """Test that a tape with no parameters is correctly
      ignored during the gradient computation"""
  
      def cost(params):
          tape1 = qml.tape.QuantumScript(
              [qml.Hadamard(0)],
              [qml.expval(qml.PauliX(0))],
              shots=shots,
          )
  
          tape2 = qml.tape.QuantumScript(
              [qml.RY(np.array(0.5), wires=0)],
              [qml.expval(qml.PauliZ(0))],
              shots=shots,
          )
  
          tape3 = qml.tape.QuantumScript(
              [qml.RY(params[0], 0), qml.RX(params[1], 0)],
              [qml.expval(qml.PauliZ(0))],
              shots=shots,
          )
  
          tape4 = qml.tape.QuantumScript(
              [qml.RY(np.array(0.5), 0)],
              [qml.probs(wires=[0, 1])],
              shots=shots,
          )
          res = execute(
              [tape1, tape2, tape3, tape4], device, **execute_kwargs
          )
          if shots.has_partitioned_shots:
              res = [
                  qml.math.asarray(ri, like="torch")
                  for r in res
                  for ri in r
              ]
          else:
              res = [qml.math.asarray(r, like="torch") for r in res]
          return sum(torch.hstack(res))
  
      params = torch.tensor([0.1, 0.2], requires_grad=True)
      x, y = params.clone().detach()
  
      res = cost(params)
      expected = 2 + np.cos(0.5) + np.cos(x) * np.cos(y)
  
      if shots.has_partitioned_shots:
          assert torch.allclose(
              res, 2 * expected, atol=atol_for_shots(shots), rtol=0
          )
      else:
          assert torch.allclose(
              res, expected, atol=atol_for_shots(shots), rtol=0
          )
  
      res.backward()
      expected = torch.tensor(
          [-torch.cos(y) * torch.sin(x), -torch.cos(x) * torch.sin(y)]
      )
      if shots.has_partitioned_shots:
          assert torch.allclose(
              params.grad,
              2 * expected,
              atol=atol_for_shots(shots),
              rtol=0,
          )
      else:
          assert torch.allclose(
              params.grad, expected, 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 4 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_tape_no_parameters(execute_kwargs, shots, device) and returns: sum