class_docstr: Test preprocess produces output that can be executed by the device.
class_docstr_tok: ["Test","preprocess","produces","output","that","can","be","executed","by","the","device","."]
docstr: Test that batch_transform splits broadcasted tapes correctly when
  the diff method is adjoint
docstr_tok: ["Test","that","batch_transform","splits","broadcasted","tapes","correctly","when","the","diff","method","is","adjoint"]
code: def test_batch_transform_broadcast_adjoint(self):
      """Test that batch_transform splits broadcasted tapes correctly when
      the diff method is adjoint"""
      ops = [
          qml.Hadamard(0),
          qml.CNOT([0, 1]),
          qml.RX([np.pi, np.pi / 2], wires=1),
      ]
      measurements = [qml.expval(qml.PauliZ(1))]
      tape = qml.tape.QuantumScript(ops=ops, measurements=measurements)
  
      execution_config = ExecutionConfig(gradient_method="adjoint")
  
      device = qml.devices.DefaultQubit()
  
      program, _ = device.preprocess(execution_config=execution_config)
      tapes, _ = program([tape])
      expected_ops = [
          [qml.Hadamard(0), qml.CNOT([0, 1]), qml.RX(np.pi, wires=1)],
          [
              qml.Hadamard(0),
              qml.CNOT([0, 1]),
              qml.RX(np.pi / 2, wires=1),
          ],
      ]
  
      assert len(tapes) == 2
      for i, t in enumerate(tapes):
          for op, expected in zip(
              t.circuit, expected_ops[i] + measurements
          ):
              assert qml.equal(op, expected)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.devices import DefaultQubit, ExecutionConfig
  from pennylane.devices.default_qubit import stopping_condition
label_desc: Use image node_id 3 for calling the TestPreprocessingIntegration obj's underlying member method code with example usage: obj.test_batch_transform_broadcast_adjoint() without return types