code: def append_gate(
      tape: QuantumTape, params, gates
  ) -> (Sequence[QuantumTape], Callable):
      """Append parameterized gates to an existing tape.
  
      Args:
          tape (QuantumTape or QNode or Callable): quantum circuit to transform by adding gates
          params (array[float]): parameters of the gates to be added
          gates (list[Operator]): list of the gates to be added
  
      Returns:
          qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]: The transformed circuit as described in :func:`qml.transform <pennylane.transform>`.
  
      """
      new_operations = []
  
      for i, g in enumerate(gates):
          g = copy.copy(g)
          new_params = (params[i], *g.data[1:])
          g.data = new_params
          new_operations.append(g)
  
      new_tape = type(tape)(
          tape.operations + new_operations,
          tape.measurements,
          shots=tape.shots,
      )
  
      def null_postprocessing(results):
          """A postprocesing function returned by a transform that only converts the batch of results
          into a result for a single ``QuantumTape``.
          """
          return results[0]  # pragma: no cover
  
      return [new_tape], null_postprocessing
  
file_path: pennylane/pennylane/optimize/adaptive.py
imports: import copy
  from typing import Sequence, Callable
  import pennylane
  from pennylane import numpy
  from pennylane.tape import QuantumTape
  from pennylane import transform
label_desc: Use image node_id 1 for calling a global function with example usage: append_gate(tape, params, gates) and returns: list,  null_postprocessing, results