code: def add_snow(img, snow_point, brightness_coeff):
      """Bleaches out pixels, imitation snow.
  
      From https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library
  
      Args:
          img (numpy.ndarray): Image.
          snow_point: Number of show points.
          brightness_coeff: Brightness coefficient.
  
      Returns:
          numpy.ndarray: Image.
  
      """
      non_rgb_warning(img)
  
      input_dtype = img.dtype
      needs_float = False
  
      snow_point *= 127.5  # = 255 / 2
      snow_point += 85  # = 255 / 3
  
      if input_dtype == np.float32:
          img = from_float(img, dtype=np.dtype("uint8"))
          needs_float = True
      elif input_dtype not in (np.uint8, np.float32):
          raise ValueError(
              "Unexpected dtype {} for RandomSnow augmentation".format(
                  input_dtype
              )
          )
  
      image_HLS = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
      image_HLS = np.array(image_HLS, dtype=np.float32)
  
      image_HLS[:, :, 1][
          image_HLS[:, :, 1] < snow_point
      ] *= brightness_coeff
  
      image_HLS[:, :, 1] = clip(image_HLS[:, :, 1], np.uint8, 255)
  
      image_HLS = np.array(image_HLS, dtype=np.uint8)
  
      image_RGB = cv2.cvtColor(image_HLS, cv2.COLOR_HLS2RGB)
  
      if needs_float:
          image_RGB = to_float(image_RGB, max_value=255)
  
      return image_RGB
  
imports: from __future__ import division
  from typing import Optional, Sequence, Union
  from warnings import warn
  import cv2
  import numpy
  import skimage
  from albumentations import random_utils
  from albumentations.augmentations.utils import MAX_VALUES_BY_DTYPE, _maybe_process_in_chunks, clip, clipped, ensure_contiguous, is_grayscale_image, is_rgb_image, non_rgb_warning, preserve_channel_dim, preserve_shape
label_desc: Use image node_id 21 for calling a global function with example usage: add_snow(img, snow_point, brightness_coeff) and returns: image_RGB