code: def get_random_patch(
      dims: Sequence[int],
      patch_size: Sequence[int],
      rand_state: np.random.RandomState | None = None,
  ) -> tuple[slice, ...]:
      """
      Returns a tuple of slices to define a random patch in an array of shape `dims` with size `patch_size` or the as
      close to it as possible within the given dimension. It is expected that `patch_size` is a valid patch for a source
      of shape `dims` as returned by `get_valid_patch_size`.
  
      Args:
          dims: shape of source array
          patch_size: shape of patch size to generate
          rand_state: a random state object to generate random numbers from
  
      Returns:
          (tuple of slice): a tuple of slice objects defining the patch
      """
  
      # choose the minimal corner of the patch
      rand_int = (
          np.random.randint
          if rand_state is None
          else rand_state.randint
      )
      min_corner = tuple(
          rand_int(0, ms - ps + 1) if ms > ps else 0
          for ms, ps in zip(dims, patch_size)
      )
  
      # create the slices for each dimension which define the patch in the source array
      return tuple(
          slice(mc, mc + ps) for mc, ps in zip(min_corner, patch_size)
      )
  
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 1 for calling a global function with example usage: get_random_patch(dims, patch_size, rand_state) and returns: tuple