class_docstr: Test the tensorflow interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","tensorflow","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Test execution
docstr_tok: ["Test","execution"]
code: def test_execution(self, execute_kwargs, shots, device):
      """Test execution"""
  
      def cost(a, b):
          ops1 = [qml.RY(a, wires=0), qml.RX(b, wires=0)]
          tape1 = qml.tape.QuantumScript(
              ops1, [qml.expval(qml.PauliZ(0))], shots=shots
          )
  
          ops2 = [qml.RY(a, wires="a"), qml.RX(b, wires="a")]
          tape2 = qml.tape.QuantumScript(
              ops2, [qml.expval(qml.PauliZ("a"))], shots=shots
          )
  
          return execute([tape1, tape2], device, **execute_kwargs)
  
      a = tf.Variable(0.1)
      b = tf.constant(0.2)
      with device.tracker:
          res = cost(a, b)
  
      if execute_kwargs.get("gradient_fn", None) == "adjoint":
          assert (
              device.tracker.totals["execute_and_derivative_batches"]
              == 1
          )
      else:
          assert device.tracker.totals["batches"] == 1
      assert (
          device.tracker.totals["executions"] == 2
      )  # different wires so different hashes
  
      assert len(res) == 2
      assert res[0].shape == ()
      assert res[1].shape == ()
  
      assert qml.math.allclose(
          res[0], tf.cos(a) * tf.cos(b), atol=atol_for_shots(shots)
      )
      assert qml.math.allclose(
          res[1], tf.cos(a) * tf.cos(b), atol=atol_for_shots(shots)
      )
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.devices import DefaultQubit
  from pennylane.gradients import param_shift
  from pennylane import execute
label_desc: Use image node_id 1 for calling the TestTensorflowExecuteIntegration obj's underlying member method code with example usage: obj.test_execution(execute_kwargs, shots, device) and returns: execute