code: def _process_argnum(argnum, tape):
      """Process the argnum keyword argument to ``param_shift_hessian`` from any of ``None``,
      ``int``, ``Sequence[int]``, ``array_like[bool]`` to an ``array_like[bool]``.
      """
      _trainability_note = (
          "This may be caused by attempting to differentiate with respect to parameters "
          "that are not marked as trainable."
      )
      if argnum is None:
          # All trainable tape parameters are considered
          argnum = list(range(tape.num_params))
      elif isinstance(argnum, int):
          if argnum >= tape.num_params:
              raise ValueError(
                  f"The index {argnum} exceeds the number of trainable tape parameters "
                  f"({tape.num_params}). " + _trainability_note
              )
          # Make single marked parameter an iterable
          argnum = [argnum]
  
      if len(qml.math.shape(argnum)) == 1:
          # If the iterable is 1D, consider all combinations of all marked parameters
          if not qml.math.array(argnum).dtype == bool:
              # If the 1D iterable contains indices, make sure it contains valid indices...
              if qml.math.max(argnum) >= tape.num_params:
                  raise ValueError(
                      f"The index {qml.math.max(argnum)} exceeds the number of "
                      f"trainable tape parameters ({tape.num_params})."
                      + _trainability_note
                  )
              # ...and translate it to Boolean 1D iterable
              argnum = [i in argnum for i in range(tape.num_params)]
          elif len(argnum) != tape.num_params:
              # If the 1D iterable already is Boolean, check its length
              raise ValueError(
                  "One-dimensional Boolean array argnum is expected to have as many entries as the "
                  f"tape has trainable parameters ({tape.num_params}), but got {len(argnum)}."
                  + _trainability_note
              )
          # Finally mark all combinations using the outer product
          argnum = qml.math.tensordot(argnum, argnum, axes=0)
  
      elif not (
          qml.math.shape(argnum) == (tape.num_params,) * 2
          and qml.math.array(argnum).dtype == bool
          and qml.math.allclose(qml.math.transpose(argnum), argnum)
      ):
          # If the iterable is 2D, make sure it is Boolean, symmetric and of the correct size
          raise ValueError(
              f"Expected a symmetric 2D Boolean array with shape {(tape.num_params,) * 2} "
              f"for argnum, but received {argnum}." + _trainability_note
          )
      return argnum
  
imports: import itertools
  import warnings
  from functools import partial
  from typing import Sequence, Callable
  import numpy
  import pennylane
  from pennylane.measurements import ProbabilityMP, StateMP, VarianceMP
  from pennylane.transforms import transform
  from .general_shift_rules import _combine_shift_rules, generate_multishifted_tapes, generate_shifted_tapes
  from .gradient_transform import find_and_validate_gradient_methods
  from .parameter_shift import _get_operation_recipe
  from .hessian_transform import _process_jacs
label_desc: Use image node_id 1 for calling a global function with example usage: _process_argnum(argnum, tape) and returns: argnum