code: def compute_shape_offset(
      spatial_shape: np.ndarray | Sequence[int],
      in_affine: NdarrayOrTensor,
      out_affine: NdarrayOrTensor,
      scale_extent: bool = False,
  ) -> tuple[np.ndarray, np.ndarray]:
      """
      Given input and output affine, compute appropriate shapes
      in the output space based on the input array's shape.
      This function also returns the offset to put the shape
      in a good position with respect to the world coordinate system.
  
      Args:
          spatial_shape: input array's shape
          in_affine (matrix): 2D affine matrix
          out_affine (matrix): 2D affine matrix
          scale_extent: whether the scale is computed based on the spacing or the full extent of voxels, for example, for
              a factor of 0.5 scaling:
  
              option 1, "o" represents a voxel, scaling the distance between voxels::
  
                  o--o--o
                  o-----o
  
              option 2, each voxel has a physical extent, scaling the full voxel extent::
  
                  | voxel 1 | voxel 2 | voxel 3 | voxel 4 |
                  |      voxel 1      |      voxel 2      |
  
              Option 1 may reduce the number of locations that requiring interpolation. Option 2 is more resolution
              agnostic, that is, resampling coordinates depend on the scaling factor, not on the number of voxels.
              Default is False, using option 1 to compute the shape and offset.
  
      """
      shape = np.array(spatial_shape, copy=True, dtype=float)
      sr = len(shape)
      in_affine_ = convert_data_type(
          to_affine_nd(sr, in_affine), np.ndarray
      )[0]
      out_affine_ = convert_data_type(
          to_affine_nd(sr, out_affine), np.ndarray
      )[0]
      in_coords = [
          (-0.5, dim - 0.5) if scale_extent else (0.0, dim - 1.0)
          for dim in shape
      ]
      corners: np.ndarray = np.asarray(
          np.meshgrid(*in_coords, indexing="ij")
      ).reshape((len(shape), -1))
      corners = np.concatenate((corners, np.ones_like(corners[:1])))
      try:
          corners_out = (
              np.linalg.solve(out_affine_, in_affine_) @ corners
          )
      except np.linalg.LinAlgError as e:
          raise ValueError(
              f"Affine {out_affine_} is not invertible"
          ) from e
      corners = in_affine_ @ corners
      all_dist = corners_out[:-1].copy()
      corners_out = corners_out[:-1] / corners_out[-1]
      out_shape = (
          np.round(corners_out.ptp(axis=1))
          if scale_extent
          else np.round(corners_out.ptp(axis=1) + 1.0)
      )
      offset = None
      for i in range(corners.shape[1]):
          min_corner = np.min(all_dist - all_dist[:, i : i + 1], 1)
          if np.allclose(min_corner, 0.0, rtol=AFFINE_TOL):
              offset = corners[
                  :-1, i
              ]  # corner is the smallest, shift the corner to origin
              break
      if (
          offset is None
      ):  # otherwise make output image center aligned with the input image center
          offset = (
              in_affine_[:-1, :-1] @ (shape / 2.0)
              + in_affine_[:-1, -1]
              - out_affine_[:-1, :-1] @ (out_shape / 2.0)
          )
      if scale_extent:
          in_offset = np.append(0.5 * (shape / out_shape - 1.0), 1.0)
          offset = np.abs(
              (in_affine_ @ in_offset / in_offset[-1])[:-1]
          ) * np.sign(offset)
      return out_shape.astype(int, copy=False), offset
  
imports: from __future__ import annotations
  import hashlib
  import json
  import logging
  import math
  import os
  import pickle
  import sys
  from collections import abc, defaultdict
  from collections.abc import Generator, Iterable, Mapping, Sequence, Sized
  from copy import deepcopy
  from functools import reduce
  from itertools import product, starmap, zip_longest
  from pathlib import PurePath
  from typing import Any
  import numpy
  import torch
  from torch.utils.data._utils.collate import default_collate
  from monai import config
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
  from monai.data.meta_obj import MetaObj
  from monai.utils import MAX_SEED, BlendMode, Method, NumpyPadMode, TraceKeys, convert_data_type, convert_to_dst_type, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, first, get_equivalent_dtype, issequenceiterable, look_up_option, optional_import, pytorch_after
label_desc: Use image node_id 22 for calling a global function with example usage: compute_shape_offset(spatial_shape, in_affine, out_affine, scale_extent) and returns: out_shape,  offset