code: def transform(
      quantum_transform,
      expand_transform=None,
      classical_cotransform=None,
      is_informative=None,
      final_transform=False,
  ):
      """Generalizes a function that transforms tapes to work with additional circuit-like objects such as a
      :class:`~.QNode`.
  
      ``transform`` should be applied to a function that transforms tapes. Once validated, the result will
      be an object that is able to transform PennyLane's range of circuit-like objects:
      :class:`~.QuantumTape`, quantum function and :class:`~.QNode`.
      A circuit-like object can be transformed either via decoration or by passing it functionally through
      the created transform.
  
      Args:
          quantum_transform (Callable): The input quantum transform must be a function that satisfies the
              following requirements:
  
              * Accepts a :class:`~.QuantumTape` as its first input and
                returns a sequence of :class:`~.QuantumTape` and a processing function.
  
              * The transform must have the following structure (type hinting is optional): ``my_quantum_transform(tape:
                qml.tape.QuantumTape, ...) -> ( Sequence[qml.tape.QuantumTape], Callable)``
  
          expand_transform (Callable): An optional expand transform is applied directly before the input
              quantum transform. It must be a function that satisfies the same requirements as
              ``quantum_transform``.
          classical_cotransform (Callable): A classical co-transform is a function to post-process the classical
              jacobian and the quantum jacobian and has the signature: ``my_cotransform(qjac, cjac, tape) -> tensor_like``
          is_informative (bool): Whether or not a transform is informative. If true the transform is queued at the end
              of the transform program and the tapes or qnode aren't executed.
          final_transform (bool): Whether or not the transform is terminal. If true the transform is queued at the end
              of the transform program. ``is_informative`` supersedes ``final_transform``.
  
      Returns:
  
          .TransformDispatcher: Returns a transform dispatcher object that that can transform any
          circuit-like object in PennyLane.
  
      **Example**
  
      First define an input quantum transform with the necessary structure defined above. In this example we copy the
      tape and sum the results of the execution of the two tapes.
  
      .. code-block:: python
  
          from typing import Sequence, Callable
  
          def my_quantum_transform(tape: qml.tape.QuantumTape) -> (Sequence[qml.tape.QuantumTape], Callable):
              tape1 = tape
              tape2 = tape.copy()
  
              def post_processing_fn(results):
                  return qml.math.sum(results)
  
              return [tape1, tape2], post_processing_fn
  
      We want to be able to apply this transform on both a ``qfunc`` and a :class:`pennylane.QNode` and will
      use ``transform`` to achieve this. ``transform`` validates the signature of your input quantum transform
      and makes it capable of transforming ``qfunc`` and :class:`pennylane.QNode` in addition to quantum tapes.
      Let's define a circuit as a :class:`pennylane.QNode`:
  
      .. code-block:: python
  
          dev = qml.device("default.qubit")
  
          @qml.qnode(device=dev)
          def qnode_circuit(a):
              qml.Hadamard(wires=0)
              qml.CNOT(wires=[0, 1])
              qml.PauliX(wires=0)
              qml.RZ(a, wires=1)
              return qml.expval(qml.PauliZ(wires=0))
  
      We first apply ``transform`` to ``my_quantum_transform``:
  
      >>> dispatched_transform = transform(my_quantum_transform)
  
      Now you can use the dispatched transform directly on a :class:`pennylane.QNode`.
  
      For :class:`pennylane.QNode`, the dispatched transform populates the ``TransformProgram`` of your QNode. The
      transform and its processing function are applied in the execution.
  
      >>> transformed_qnode = dispatched_transform(qnode_circuit)
      <QNode: wires=2, device='default.qubit', interface='auto', diff_method='best'>
  
      >>> transformed_qnode.transform_program
      TransformProgram(my_quantum_transform)
  
      If we apply ``dispatched_transform`` a second time to the :class:`pennylane.QNode`, we would add
      it to the transform program again and therefore the transform would be applied twice before execution.
  
      >>> transformed_qnode = dispatched_transform(transformed_qnode)
      >>> transformed_qnode.transform_program
      TransformProgram(my_quantum_transform, my_quantum_transform)
  
      When a transformed QNode is executed, the QNode's transform program is applied to the generated tape
      and creates a sequence of tapes to be executed. The execution results are then post-processed in the
      reverse order of the transform program to obtain the final results.
  
      .. details::
          :title: Signature of a transform
  
          A dispatched transform is able to handle several PennyLane circuit-like objects:
  
          - :class:`pennylane.QNode`
          - a quantum function (callable)
          - :class:`pennylane.tape.QuantumTape`
          - :class:`pennylane.devices.Device`.
  
          For each object, the transform will be applied in a different way, but it always preserves the underlying
          tape-based quantum transform behaviour.
  
          The return of a dispatched transform depends upon which of the above objects is passed as an input:
  
          - For a :class:`~.QNode` input, the underlying transform is added to the QNode's
            :class:`~.TransformProgram` and the return is the transformed :class:`~.QNode`.
            For each execution of the :class:`pennylane.QNode`, it first applies the transform program on the original captured
            circuit. Then the transformed circuits are executed by a device and finally the post-processing function is
            applied on the results.
  
          - For a quantum function (callable) input, the transform builds the tape when the quantum function is
            executed and then applies itself to the tape. The resulting tape is then converted back
            to a quantum function (callable). It therefore returns a transformed quantum function (Callable). The limitation
            is that the underlying transform can only return a sequence containing a single tape, because quantum
            functions only support a single circuit.
  
          - For a :class:`~.QuantumTape`, the underlying quantum transform is directly applied on the
            :class:`~.QuantumTape`. It returns a sequence of :class:`~.QuantumTape` and a processing
            function to be applied after execution.
  
          - For a :class:`~.devices.Device`, the transform is added to the device's transform program
            and a transformed :class:`pennylane.devices.Device` is returned. The transform is added
            to the end of the device program and will be last in the overall transform program.
      """
      # 1: Checks for the transform
      if not callable(quantum_transform):
          raise TransformError(
              f"The function to register, {quantum_transform}, "
              "does not appear to be a valid Python function or callable."
          )
  
      signature_transform = get_type_hints(quantum_transform)
  
      # 2: Checks for the expand transform
      if expand_transform is not None:
          if not callable(expand_transform):
              raise TransformError(
                  "The expand function must be a valid Python function."
              )
          signature_expand_transform = get_type_hints(expand_transform)
  
          if signature_expand_transform != signature_transform:
              raise TransformError(
                  "The expand transform must have the same signature as the transform"
              )
  
      # 3: CHeck the classical co-transform
      if classical_cotransform is not None:
          if not callable(classical_cotransform):
              raise TransformError(
                  "The classical co-transform must be a valid Python function."
              )
  
      dispatcher = TransformDispatcher(
          quantum_transform,
          expand_transform=expand_transform,
          classical_cotransform=classical_cotransform,
          is_informative=is_informative,
          final_transform=final_transform,
      )
      return dispatcher
  
label_desc: Use image node_id 1 for calling a global function with example usage: transform(quantum_transform, expand_transform, classical_cotransform, is_informative, final_transform) and returns: dispatcher