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 classical processing within the quantum tape for multiple
  tapes
docstr_tok: ["Test","classical","processing","within","the","quantum","tape","for","multiple","tapes"]
code: def test_classical_processing_multiple_tapes(self, execute_kwargs):
      """Test classical processing within the quantum tape for multiple
      tapes"""
      dev = qml.device("default.qubit.legacy", wires=2)
      params = jax.numpy.array([0.3, 0.2])
  
      def cost_fn(x):
          with qml.queuing.AnnotatedQueue() as q1:
              qml.Hadamard(0)
              qml.RY(x[0], wires=[0])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0))
  
          tape1 = qml.tape.QuantumScript.from_queue(q1)
  
          with qml.queuing.AnnotatedQueue() as q2:
              qml.Hadamard(0)
              qml.CRX(2 * x[0] * x[1], wires=[0, 1])
              qml.RX(2 * x[1], wires=[1])
              qml.expval(qml.PauliZ(0))
  
          tape2 = qml.tape.QuantumScript.from_queue(q2)
  
          result = execute(
              tapes=[tape1, tape2], device=dev, **execute_kwargs
          )
          return result[0] + result[1] - 7 * result[1]
  
      res = jax.jit(jax.grad(cost_fn))(params)
      assert res.shape == (2,)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.gradients import param_shift
  from pennylane import execute
label_desc: Use image node_id 6 for calling the TestJaxExecuteIntegration obj's underlying member method code with example usage: obj.test_classical_processing_multiple_tapes(execute_kwargs) and returns: unknown