class_docstr: Test the jax interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","jax","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Tests correct output shape and evaluation for a tape
  with prob and expval outputs
docstr_tok: ["Tests","correct","output","shape","and","evaluation","for","a","tape","with","prob","and","expval","outputs"]
code: def test_ragged_differentiation(self, execute_kwargs, device, shots):
      """Tests correct output shape and evaluation for a tape
      with prob and expval outputs"""
  
      def cost(x, y):
          ops = [qml.RX(x, wires=0), qml.RY(y, 1), qml.CNOT((0, 1))]
          m = [qml.expval(qml.PauliZ(0)), qml.probs(wires=1)]
          tape = qml.tape.QuantumScript(ops, m, shots=shots)
          res = qml.execute([tape], device, **execute_kwargs)[0]
          return jnp.hstack(jax.tree_util.tree_leaves(res))
  
      x = jnp.array(0.543)
      y = jnp.array(-0.654)
  
      res = cost(x, y)
      expected = jnp.array(
          [
              jnp.cos(x),
              (1 + jnp.cos(x) * jnp.cos(y)) / 2,
              (1 - jnp.cos(x) * jnp.cos(y)) / 2,
          ]
      )
      if shots.has_partitioned_shots:
          assert np.allclose(
              res[:3], expected, atol=atol_for_shots(shots), rtol=0
          )
          assert np.allclose(
              res[3:], expected, atol=atol_for_shots(shots), rtol=0
          )
      else:
          assert np.allclose(
              res, expected, atol=atol_for_shots(shots), rtol=0
          )
  
      jac_fn = jax.jacobian(cost, argnums=[0, 1])
      res = jac_fn(x, y)
      assert isinstance(res, tuple) and len(res) == 2
      assert (
          res[0].shape == (3 * shots.num_copies,)
          if shots.has_partitioned_shots
          else (3,)
      )
      assert (
          res[1].shape == (3 * shots.num_copies,)
          if shots.has_partitioned_shots
          else (3,)
      )
  
      expected = (
          jnp.array(
              [
                  -jnp.sin(x),
                  -jnp.sin(x) * jnp.cos(y) / 2,
                  jnp.sin(x) * jnp.cos(y) / 2,
              ]
          ),
          jnp.array(
              [
                  0,
                  -jnp.cos(x) * jnp.sin(y) / 2,
                  jnp.cos(x) * jnp.sin(y) / 2,
              ]
          ),
      )
      if shots.has_partitioned_shots:
          assert np.allclose(
              res[0][:3],
              expected[0],
              atol=atol_for_shots(shots),
              rtol=0,
          )
          assert np.allclose(
              res[0][3:],
              expected[0],
              atol=atol_for_shots(shots),
              rtol=0,
          )
          assert np.allclose(
              res[1][:3],
              expected[1],
              atol=atol_for_shots(shots),
              rtol=0,
          )
          assert np.allclose(
              res[1][3:],
              expected[1],
              atol=atol_for_shots(shots),
              rtol=0,
          )
      else:
          assert np.allclose(
              res[0], expected[0], atol=atol_for_shots(shots), rtol=0
          )
          assert np.allclose(
              res[1], expected[1], atol=atol_for_shots(shots), rtol=0
          )
  
imports: import pytest
  import numpy
  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 11 for calling the TestJaxExecuteIntegration obj's underlying member method code with example usage: obj.test_ragged_differentiation(execute_kwargs, device, shots) and returns: jnp