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: Test jax grad for adjoint diff method in backward mode
docstr_tok: ["Test","jax","grad","for","adjoint","diff","method","in","backward","mode"]
code: def test_grad_with_backward_mode(self, execute_kwargs):
      """Test jax grad for adjoint diff method in backward mode"""
      dev = qml.device("default.qubit.legacy", wires=2)
      params = jax.numpy.array([0.1, 0.2, 0.3])
      expected_results = jax.numpy.array(
          [-0.3875172, -0.18884787, -0.38355705]
      )
  
      def cost(a, cache):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RY(a[0], wires=0)
              qml.RX(a[1], wires=0)
              qml.RY(a[2], wires=0)
              qml.expval(qml.PauliZ(0))
  
          tape = qml.tape.QuantumScript.from_queue(q)
  
          res = qml.execute([tape], dev, cache=cache, **execute_kwargs)[
              0
          ]
          return res
  
      cost = jax.jit(cost)
  
      results = jax.grad(cost)(params, cache=None)
      for r, e in zip(results, expected_results):
          assert jax.numpy.allclose(r, e, atol=1e-7)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.gradients import param_shift
  from pennylane import execute
label_desc: Use image node_id 4 for calling the TestJaxExecuteIntegration obj's underlying member method code with example usage: obj.test_grad_with_backward_mode(execute_kwargs) and returns: res