code: def wrapped_pad_func(
      array, pad_func, iaxis_pad_width, iaxis, pad_func_kwargs
  ):
      result = np.empty_like(array)
      for i in np.ndindex(
          array.shape[:iaxis] + array.shape[iaxis + 1 :]
      ):
          i = i[:iaxis] + (slice(None),) + i[iaxis:]
          result[i] = pad_func(
              array[i], iaxis_pad_width, iaxis, pad_func_kwargs
          )
  
      return result
  
imports: from __future__ import annotations
  import itertools
  from collections.abc import Sequence
  from functools import partial
  from itertools import product
  from numbers import Integral, Number
  import numpy
  from tlz import sliding_window
  from dask.array import chunk
  from dask.array.backends import array_creation_dispatch
  from dask.array.core import Array, asarray, block, blockwise, broadcast_arrays, broadcast_to, concatenate, normalize_chunks, stack
  from dask.array.numpy_compat import AxisError
  from dask.array.ufunc import greater_equal, rint
  from dask.array.utils import meta_from_array
  from dask.array.wrap import empty, full, ones, zeros
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, derived_from, is_cupy_type
label_desc: Use image node_id 24 for calling a global function with example usage: wrapped_pad_func(array, pad_func, iaxis_pad_width, iaxis, pad_func_kwargs) and returns: result