code: def cut_patch(
      self,
      image: np.ndarray,
      slice_index,
      border_type=cv2.BORDER_CONSTANT,
      value=0,
  ):
      assert image.shape[0] == self.image_height
      assert image.shape[1] == self.image_width
  
      orig_shape_len = len(image.shape)
      x, y, tile_width, tile_height = self.bbox_crops[slice_index]
  
      x1 = max(x, 0)
      y1 = max(y, 0)
      x2 = min(image.shape[1], x + tile_width)
      y2 = min(image.shape[0], y + tile_height)
  
      tile = image[y1:y2, x1:x2]
      if (
          x < 0
          or y < 0
          or (x + tile_width) > image.shape[1]
          or (y + tile_height) > image.shape[0]
      ):
          tile = cv2.copyMakeBorder(
              tile,
              top=max(0, -y),
              bottom=max(0, y + tile_height - image.shape[0]),
              left=max(0, -x),
              right=max(0, x + tile_width - image.shape[1]),
              borderType=border_type,
              value=value,
          )
  
          # This check recovers possible lack of last dummy dimension for single-channel images
          if len(tile.shape) != orig_shape_len:
              tile = np.expand_dims(tile, axis=-1)
  
      return tile
  
imports: import dataclasses
  import math
  from typing import List, Iterable, Tuple, Union, Sequence
  import cv2
  import numpy
  import torch
label_desc: Use image node_id 4 for calling the ImageSlicer obj's underlying member method code with example usage: obj.cut_patch(image, slice_index, border_type, value) and returns: tile