class_docstr: Random crop and flip a patch in the image. Only used in text detection
  task.
  
  Required Keys:
  
  - img
  - gt_bboxes
  - gt_polygons
  
  Modified Keys:
  
  - img
  - gt_bboxes
  - gt_polygons
  
  Args:
      pad_ratio (float): The ratio of padding. Defaults to 0.1.
      crop_ratio (float): The ratio of cropping. Defaults to 0.5.
      iter_num (int): Number of operations. Defaults to 1.
      min_area_ratio (float): Minimal area ratio between cropped patch
          and original image. Defaults to 0.2.
      epsilon (float): The threshold of polygon IoU between cropped area
          and polygon, which is used to avoid cropping text instances.
          Defaults to 0.01.
docstr: Randomly select two coordinates from the axis.
  
  Args:
      axis (np.ndarray): Result dict containing the data to transform
  
  Returns:
      np.ndarray: The selected coordinates
docstr_tok: ["Randomly","select","two","coordinates","from","the","axis",".","Args",":","axis","(","np.ndarray",")",":","Result","dict","containing","the","data","to","transform","Returns",":","np.ndarray",":","The","selected","coordinates"]
code: def _random_choice(self, axis: np.ndarray) -> np.ndarray:
      """Randomly select two coordinates from the axis.
  
      Args:
          axis (np.ndarray): Result dict containing the data to transform
  
      Returns:
          np.ndarray: The selected coordinates
      """
      return np.random.choice(axis, size=2)
  
imports: import math
  import random
  from typing import Dict, List, Sequence, Tuple, Union
  import cv2
  import mmcv
  import numpy
  from mmcv.transforms import RandomFlip
  from mmcv.transforms.base import BaseTransform
  from mmcv.transforms.utils import avoid_cache_randomness, cache_randomness
  from shapely.geometry import Polygon
  from mmocr.registry import TRANSFORMS
  from mmocr.utils import crop_polygon, poly2bbox, poly_intersection
label_desc: Use image node_id 4 for calling the TextDetRandomCropFlip obj's underlying member method code with example usage: obj._random_choice(axis) and returns: np