code: def _expand_transform_spsa(
      tape: qml.tape.QuantumTape,
      argnum=None,
      h=1e-5,
      approx_order=2,
      n=1,
      strategy="center",
      f0=None,
      validate_params=True,
      num_directions=1,
      sampler=_rademacher_sampler,
      sampler_rng=None,
  ) -> (Sequence[qml.tape.QuantumTape], Callable):
      """Expand function to be applied before spsa gradient."""
      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
  
file_path: pennylane/pennylane/gradients/spsa_gradient.py
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane import transform
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from .finite_difference import _processing_fn, finite_diff_coeffs
  from .gradient_transform import _all_zero_grad, assert_no_tape_batching, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad
  from .general_shift_rules import generate_multishifted_tapes
label_desc: Use image node_id 2 for calling a global function with example usage: _expand_transform_spsa(tape, argnum, h, approx_order, n, strategy, f0, validate_params, num_directions, sampler, sampler_rng) and returns: list,  null_postprocessing, results