class_docstr: Test the autograd interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","autograd","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, requires_grad=False), 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, requires_grad=False), 0)],
              [qml.probs(wires=[0, 1])],
              shots=shots,
          )
          res = qml.execute(
              [tape1, tape2, tape3, tape4], device, **execute_kwargs
          )
          if shots.has_partitioned_shots:
              res = tuple(i for r in res for i in r)
          return sum(autograd.numpy.hstack(res))
  
      params = np.array([0.1, 0.2], requires_grad=True)
      x, y = params
  
      res = cost(params)
      expected = 2 + np.cos(0.5) + np.cos(x) * np.cos(y)
      if shots.has_partitioned_shots:
          expected = shots.num_copies * expected
      assert np.allclose(
          res, expected, atol=atol_for_shots(shots), rtol=0
      )
  
      grad = qml.grad(cost)(params)
      expected = np.array(
          [-np.cos(y) * np.sin(x), -np.cos(x) * np.sin(y)]
      )
      if shots.has_partitioned_shots:
          expected = shots.num_copies * expected
      assert np.allclose(
          grad, expected, atol=atol_for_shots(shots), rtol=0
      )
  
imports: import autograd
  import pytest
  from param_shift_dev import ParamShiftDerivativesDevice
  from pennylane import numpy
  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 TestAutogradExecuteIntegration obj's underlying member method code with example usage: obj.test_tape_no_parameters(execute_kwargs, shots, device) and returns: sum