class_docstr: Test the tensorflow interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","tensorflow","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(tf.constant(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(tf.constant(0.5), 0)],
              [qml.probs(wires=[0, 1])],
              shots=shots,
          )
          return tf.reduce_sum(
              qml.math.hstack(
                  execute(
                      [tape1, tape2, tape3, tape4],
                      device,
                      **execute_kwargs,
                  ),
                  like="tensorflow",
              )
          )
  
      params = tf.Variable([0.1, 0.2])
      x, y = params
  
      with tf.GradientTape() as tape:
          res = cost(params)
      expected = 2 + tf.cos(0.5) + tf.cos(x) * tf.cos(y)
      assert np.allclose(
          res, expected, atol=atol_for_shots(shots), rtol=0
      )
  
      if (
          execute_kwargs.get("interface", "") == "tf-autograph"
          and execute_kwargs.get("gradient_fn", "") == "adjoint"
      ):
          with pytest.raises(NotImplementedError):
              tape.gradient(res, params)
          return
  
      grad = tape.gradient(res, params)
      expected = [-tf.cos(y) * tf.sin(x), -tf.cos(x) * tf.sin(y)]
      assert np.allclose(
          grad, expected, atol=atol_for_shots(shots), rtol=0
      )
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.devices import DefaultQubit
  from pennylane.gradients import param_shift
  from pennylane import execute
label_desc: Use image node_id 4 for calling the TestTensorflowExecuteIntegration obj's underlying member method code with example usage: obj.test_tape_no_parameters(execute_kwargs, shots, device) and returns: tf