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 re-using a quantum tape by passing new parameters
docstr_tok: ["Test","re-using","a","quantum","tape","by","passing","new","parameters"]
code: def test_reusing_quantum_tape(self, execute_kwargs, tol):
      """Test re-using a quantum tape by passing new parameters"""
      a = jax.numpy.array(0.1)
      b = jax.numpy.array(0.2)
  
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a, wires=0)
          qml.RX(b, wires=1)
          qml.CNOT(wires=[0, 1])
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      assert tape.trainable_params == [0, 1]
  
      def cost(a, b):
          # An explicit call to _update() is required here to update the
          # trainable parameters in between tape executions.
          # This is different from how the autograd interface works.
          # Unless the update is issued, the validation check related to the
          # number of provided parameters fails in the tape: (len(params) !=
          # required_length) and the tape produces incorrect results.
          tape._update()
          new_tape = tape.bind_new_parameters([a, b], [0, 1])
          return execute([new_tape], dev, **execute_kwargs)[0]
  
      jac_fn = jax.jit(jax.grad(cost))
      jac = jac_fn(a, b)
  
      a = jax.numpy.array(0.54)
      b = jax.numpy.array(0.8)
  
      # check that the cost function continues to depend on the
      # values of the parameters for subsequent calls
      res2 = cost(2 * a, b)
      expected = [np.cos(2 * a)]
      assert np.allclose(res2, expected, atol=tol, rtol=0)
  
      jac_fn = jax.jit(jax.grad(lambda a, b: cost(2 * a, b)))
      jac = jac_fn(a, b)
      expected = -2 * np.sin(2 * a)
      assert np.allclose(jac, expected, atol=tol, rtol=0)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.gradients import param_shift
  from pennylane import execute
label_desc: Use image node_id 3 for calling the TestJaxExecuteIntegration obj's underlying member method code with example usage: obj.test_reusing_quantum_tape(execute_kwargs, tol) and returns: execute