code: def _expand_transform_param_shift_cv(
      tape: qml.tape.QuantumTape,
      dev,
      argnum=None,
      shifts=None,
      gradient_recipes=None,
      fallback_fn=finite_diff,
      f0=None,
      force_order2=False,
  ) -> (Sequence[qml.tape.QuantumTape], Callable):
      """Expand function to be applied before parameter shift CV."""
      expanded_tape = expand_invalid_trainable(tape)
  
      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]
  
      return [expanded_tape], null_postprocessing
  
imports: from typing import Sequence, Callable
  import itertools
  from functools import partial
  import warnings
  import numpy
  import pennylane
  from pennylane.measurements import ExpectationMP, ProbabilityMP, StateMP, VarianceMP, MeasurementProcess
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac, choose_trainable_params, _validate_gradient_methods
  from .finite_difference import finite_diff
  from .general_shift_rules import generate_shifted_tapes, process_shifts
  from .gradient_transform import _no_trainable_grad
  from .parameter_shift import _get_operation_recipe, expval_param_shift
label_desc: Use image node_id 7 for calling a global function with example usage: _expand_transform_param_shift_cv(tape, dev, argnum, shifts, gradient_recipes, fallback_fn, f0, force_order2) and returns: list,  null_postprocessing, results