code: def adjust_boxes(
      boxes,
      scale=1,
      boxes_format: tx.Literal[
          "boxes", "predictions", "lines"
      ] = "boxes",
  ) -> typing.Union[
      np.ndarray,
      typing.List[typing.List[typing.Tuple[np.ndarray, str]]],
      typing.List[typing.Tuple[str, np.ndarray]],
  ]:
      """Adjust boxes using a given scale and offset.
  
      Args:
          boxes: The boxes to adjust
          boxes_format: The format for the boxes. See the `drawBoxes` function
              for an explanation on the options.
          scale: The scale to apply
      """
      if scale == 1:
          return boxes
      if boxes_format == "boxes":
          return np.array(boxes) * scale
      if boxes_format == "lines":
          return [
              [
                  (np.array(box) * scale, character)
                  for box, character in line
              ]
              for line in boxes
          ]
      if boxes_format == "predictions":
          return [(word, np.array(box) * scale) for word, box in boxes]
      raise NotImplementedError(
          f"Unsupported boxes format: {boxes_format}"
      )
  
imports: import os
  import io
  import typing
  import hashlib
  import urllib.request
  import urllib.parse
  import cv2
  import imgaug
  import numpy
  import validators
  import typing_extensions
  import matplotlib.pyplot
  from shapely import geometry
  from scipy import spatial
label_desc: Use image node_id 8 for calling a global function with example usage: adjust_boxes(boxes, scale, boxes_format) and returns: boxes, unknown, unknown, unknown