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 tapes wit different can be executed and differentiated.
docstr_tok: ["Test","that","tapes","wit","different","can","be","executed","and","differentiated","."]
code: def test_tapes_with_different_return_size(
      self, execute_kwargs, shots, device
  ):
      """Test that tapes wit different can be executed and differentiated."""
  
      if (
          execute_kwargs["gradient_fn"] == "adjoint"
          and execute_kwargs["interface"] == "tf-autograph"
      ):
          pytest.skip(
              "Cannot compute the jacobian with adjoint-differentation and tf-autograph"
          )
  
      def cost(params):
          tape1 = qml.tape.QuantumScript(
              [qml.RY(params[0], 0), qml.RX(params[1], 0)],
              [qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))],
              shots=shots,
          )
  
          tape2 = qml.tape.QuantumScript(
              [qml.RY(tf.constant(0.5), 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,
          )
          return qml.math.hstack(
              execute([tape1, tape2, tape3], device, **execute_kwargs),
              like="tensorflow",
          )
  
      params = tf.Variable([0.1, 0.2])
      x, y = params
  
      with tf.GradientTape() as tape:
          res = cost(params)
  
      assert isinstance(res, tf.Tensor)
      assert res.shape == (4,)
  
      assert np.allclose(
          res[0], tf.cos(x) * tf.cos(y), atol=atol_for_shots(shots)
      )
      assert np.allclose(res[1], 1, atol=atol_for_shots(shots))
      assert np.allclose(
          res[2], tf.cos(0.5), atol=atol_for_shots(shots)
      )
      assert np.allclose(
          res[3], tf.cos(x) * tf.cos(y), atol=atol_for_shots(shots)
      )
  
      jac = tape.jacobian(res, params)
      assert isinstance(jac, tf.Tensor)
      assert jac.shape == (4, 2)  # pylint: disable=no-member
  
      assert np.allclose(jac[1:3], 0, atol=atol_for_shots(shots))
  
      d1 = -tf.sin(x) * tf.cos(y)
      assert np.allclose(jac[0, 0], d1, atol=atol_for_shots(shots))
      assert np.allclose(jac[3, 0], d1, atol=atol_for_shots(shots))
  
      d2 = -tf.cos(x) * tf.sin(y)
      assert np.allclose(jac[0, 1], d2, atol=atol_for_shots(shots))
      assert np.allclose(jac[3, 1], d2, atol=atol_for_shots(shots))
  
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 5 for calling the TestTensorflowExecuteIntegration obj's underlying member method code with example usage: obj.test_tapes_with_different_return_size(execute_kwargs, shots, device) and returns: qml