class_docstr: Test vector-valued jacobian returns for the JAX Python interface.
class_docstr_tok: ["Test","vector-valued","jacobian","returns","for","the","JAX","Python","interface","."]
docstr: Test the jacobian computation with multiple tapes.
docstr_tok: ["Test","the","jacobian","computation","with","multiple","tapes","."]
code: def test_multi_tape_jacobian(self, execute_kwargs):
      """Test the jacobian computation with multiple tapes."""
  
      def cost(x, y, device, interface, ek):
          with qml.queuing.AnnotatedQueue() as q1:
              qml.RX(x, wires=[0])
              qml.RY(y, wires=[1])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0))
              qml.expval(qml.PauliZ(1))
  
          tape1 = qml.tape.QuantumScript.from_queue(q1)
          with qml.queuing.AnnotatedQueue() as q2:
              qml.RX(x, wires=[0])
              qml.RY(y, wires=[1])
              qml.CNOT(wires=[0, 1])
              qml.expval(qml.PauliZ(0))
              qml.expval(qml.PauliZ(1))
  
          tape2 = qml.tape.QuantumScript.from_queue(q2)
          return qml.execute(
              [tape1, tape2], device, **ek, interface=interface
          )
  
      dev = qml.device("default.qubit.legacy", wires=2)
      x = jax.numpy.array(0.543)
      y = jax.numpy.array(-0.654)
  
      x_ = np.array(0.543)
      y_ = np.array(-0.654)
  
      exec_jax = cost(
          x, y, dev, interface="jax-python", ek=execute_kwargs
      )
      exec_autograd = cost(
          x_, y_, dev, interface="autograd", ek=execute_kwargs
      )
  
      assert np.allclose(exec_jax, exec_autograd)
  
      res = jax.jacobian(cost, argnums=(0, 1))(
          x, y, dev, interface="jax-python", ek=execute_kwargs
      )
  
      import autograd.numpy as anp
  
      def cost_stack(x, y, device, interface, ek):
          return anp.hstack(cost(x, y, device, interface, ek))
  
      exp = qml.jacobian(cost_stack, argnum=(0, 1))(
          x_, y_, dev, interface="autograd", ek=execute_kwargs
      )
      res_0 = jax.numpy.array(
          [res[0][0][0], res[0][1][0], res[1][0][0], res[1][1][0]]
      )
      res_1 = jax.numpy.array(
          [res[0][0][1], res[0][1][1], res[1][0][1], res[1][1][1]]
      )
  
      assert np.allclose(res_0, exp[0])
      assert np.allclose(res_1, exp[1])
  
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 TestVectorValued obj's underlying member method code with example usage: obj.test_multi_tape_jacobian(execute_kwargs) and returns: qml, anp