code: def affine_func(
      img,
      affine,
      grid,
      resampler,
      sp_size,
      mode,
      padding_mode,
      do_resampling,
      image_only,
      lazy,
      transform_info,
  ):
      """
      Functional implementation of affine.
      This function operates eagerly or lazily according to
      ``lazy`` (default ``False``).
  
      Args:
          img: data to be changed, assuming `img` is channel-first.
          affine: the affine transformation to be applied, it can be a 3x3 or 4x4 matrix. This should be defined
              for the voxel space spatial centers (``float(size - 1)/2``).
          grid: used in non-lazy mode to pre-compute the grid to do the resampling.
          resampler: the resampler function, see also: :py:class:`monai.transforms.Resample`.
          sp_size: output image spatial size.
          mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
              Interpolation mode to calculate output values.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              When it's an integer, the numpy (cpu tensor)/cupy (cuda tensor) backends will be used
              and the value represents the order of the spline interpolation.
              See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
          padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
              Padding mode for outside grid values.
              See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
              When `mode` is an integer, using numpy/cupy backends, this argument accepts
              {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}.
              See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
          do_resampling: whether to do the resampling, this is a flag for the use case of updating metadata but
              skipping the actual (potentially heavy) resampling operation.
          image_only: if True return only the image volume, otherwise return (image, affine).
          lazy: a flag that indicates whether the operation should be performed lazily or not
          transform_info: a dictionary with the relevant information pertaining to an applied transform.
  
      """
  
      # resampler should carry the align_corners and type info
      img_size = (
          img.peek_pending_shape()
          if isinstance(img, MetaTensor)
          else img.shape[1:]
      )
      rank = (
          img.peek_pending_rank()
          if isinstance(img, MetaTensor)
          else torch.tensor(3.0, dtype=torch.double)
      )
      extra_info = {
          "affine": affine,
          "mode": mode,
          "padding_mode": padding_mode,
          "do_resampling": do_resampling,
          "align_corners": resampler.align_corners,
      }
      affine = monai.transforms.Affine.compute_w_affine(
          rank, affine, img_size, sp_size
      )
      meta_info = TraceableTransform.track_transform_meta(
          img,
          sp_size=sp_size,
          affine=affine,
          extra_info=extra_info,
          orig_size=img_size,
          transform_info=transform_info,
          lazy=lazy,
      )
      if lazy:
          out = _maybe_new_metatensor(img)
          out = (
              out.copy_meta_from(meta_info)
              if isinstance(out, MetaTensor)
              else meta_info
          )
          return out if image_only else (out, affine)
      if do_resampling:
          out = resampler(
              img=img, grid=grid, mode=mode, padding_mode=padding_mode
          )
          out = _maybe_new_metatensor(out)
      else:
          out = _maybe_new_metatensor(
              img, dtype=torch.float32, device=resampler.device
          )
      out = (
          out.copy_meta_from(meta_info)
          if isinstance(out, MetaTensor)
          else out
      )
      return out if image_only else (out, affine)
  
imports: from __future__ import annotations
  import math
  import warnings
  from enum import Enum
  import numpy
  import torch
  import monai
  from monai.config import USE_COMPILED
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.data.meta_obj import get_track_meta
  from monai.data.meta_tensor import MetaTensor
  from monai.data.utils import AFFINE_TOL, compute_shape_offset, to_affine_nd
  from monai.networks.layers import AffineTransform
  from monai.transforms.croppad.array import ResizeWithPadOrCrop
  from monai.transforms.intensity.array import GaussianSmooth
  from monai.transforms.inverse import TraceableTransform
  from monai.transforms.utils import create_rotate, create_translate, resolves_modes, scale_affine
  from monai.transforms.utils_pytorch_numpy_unification import allclose
  from monai.utils import LazyAttr, TraceKeys, convert_to_dst_type, convert_to_numpy, convert_to_tensor, ensure_tuple, ensure_tuple_rep, fall_back_tuple, optional_import
label_desc: Use image node_id 9 for calling a global function with example usage: affine_func(img, affine, grid, resampler, sp_size, mode, padding_mode, do_resampling, image_only, lazy, transform_info) and returns: unknown, unknown