code: def dense_patch_slices(
      image_size: Sequence[int],
      patch_size: Sequence[int],
      scan_interval: Sequence[int],
      return_slice: bool = True,
  ) -> list[tuple[slice, ...]]:
      """
      Enumerate all slices defining ND patches of size `patch_size` from an `image_size` input image.
  
      Args:
          image_size: dimensions of image to iterate over
          patch_size: size of patches to generate slices
          scan_interval: dense patch sampling interval
          return_slice: whether to return a list of slices (or tuples of indices), defaults to True
  
      Returns:
          a list of slice objects defining each patch
  
      """
      num_spatial_dims = len(image_size)
      patch_size = get_valid_patch_size(image_size, patch_size)
      scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
  
      scan_num = []
      for i in range(num_spatial_dims):
          if scan_interval[i] == 0:
              scan_num.append(1)
          else:
              num = int(
                  math.ceil(float(image_size[i]) / scan_interval[i])
              )
              scan_dim = first(
                  d
                  for d in range(num)
                  if d * scan_interval[i] + patch_size[i]
                  >= image_size[i]
              )
              scan_num.append(
                  scan_dim + 1 if scan_dim is not None else 1
              )
  
      starts = []
      for dim in range(num_spatial_dims):
          dim_starts = []
          for idx in range(scan_num[dim]):
              start_idx = idx * scan_interval[dim]
              start_idx -= max(
                  start_idx + patch_size[dim] - image_size[dim], 0
              )
              dim_starts.append(start_idx)
          starts.append(dim_starts)
      out = np.asarray(
          [x.flatten() for x in np.meshgrid(*starts, indexing="ij")]
      ).T
      if return_slice:
          return [
              tuple(
                  slice(s, s + patch_size[d]) for d, s in enumerate(x)
              )
              for x in out
          ]
      return [
          tuple((s, s + patch_size[d]) for d, s in enumerate(x))
          for x in out
      ]
  
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 3 for calling a global function with example usage: dense_patch_slices(image_size, patch_size, scan_interval, return_slice) and returns: unknown, unknown