class_docstr: Test that the transform is differentiable
class_docstr_tok: ["Test","that","the","transform","is","differentiable"]
docstr: Tests that the output of the hadamard gradient transform
  can be differentiated using JAX, yielding second derivatives.
code: def test_jax(self, dev_name):
      """Tests that the output of the hadamard gradient transform
      can be differentiated using JAX, yielding second derivatives."""
      import jax
      from jax import numpy as jnp
  
      dev = qml.device(dev_name, wires=3)
      execute_fn = (
          dev.execute
          if dev_name == "default.qubit"
          else dev.batch_execute
      )
      params = jnp.array([0.543, -0.654])
  
      def cost_h(x):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RX(x[0], wires=[0])
              qml.RY(x[1], wires=[1])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0) @ qml.PauliX(1))
  
          tape = qml.tape.QuantumScript.from_queue(q)
          tape.trainable_params = {0, 1}
          tapes, fn = qml.gradients.hadamard_grad(tape)
  
          jac = fn(execute_fn(tapes))
          return jac
  
      def cost_p(x):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RX(x[0], wires=[0])
              qml.RY(x[1], wires=[1])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0) @ qml.PauliX(1))
  
          tape = qml.tape.QuantumScript.from_queue(q)
          tape.trainable_params = {0, 1}
          tapes, fn = qml.gradients.hadamard_grad(tape)
  
          jac = fn(execute_fn(tapes))
          return jac
  
      res_hadamard = jax.jacobian(cost_h)(params)
      res_param_shift = jax.jacobian(cost_p)(params)
      assert np.allclose(res_hadamard, res_param_shift)
  
imports: import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 4 for calling the TestHadamardTestGradDiff obj's underlying member method code with example usage: obj.test_jax(dev_name) and returns: jac, jac