code: def apply_mask(
      X: np.ndarray,
      mask_size: tuple = (4, 4),
      n_masks: int = 1,
      coord: tuple = None,
      channels: list = [0, 1, 2],
      mask_type: str = "uniform",
      noise_distr: tuple = (0, 1),
      noise_rng: tuple = (0, 1),
      clip_rng: tuple = (0, 1),
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Mask images. Can zero out image patches or add normal or uniformly distributed noise.
  
      Parameters
      ----------
      X
          Batch of instances to be masked.
      mask_size
          Tuple with the size of the mask.
      n_masks
          Number of masks applied for each instance in the batch X.
      coord
          Upper left (x,y)-coordinates for the mask.
      channels
          Channels of the image to apply the mask to.
      mask_type
          Type of mask. One of 'uniform', 'random' (both additive noise) or 'zero' (zero values for mask).
      noise_distr
          Mean and standard deviation for noise of 'random' mask type.
      noise_rng
          Min and max value for noise of 'uniform' type.
      clip_rng
          Min and max values for the masked instances.
  
      Returns
      -------
      Tuple with masked instances and the masks.
      """
      X_shape = X.shape
  
      # initialize mask
      if mask_type != "zero":
          mask = np.zeros((n_masks,) + X_shape[1:])
      elif mask_type == "zero":
          mask = np.ones((n_masks,) + X_shape[1:])
      else:
          raise ValueError(
              "Only `normal`, `uniform` and `zero` masking available."
          )
  
      # create noise for mask
      if mask_type == "normal":
          noise = np.random.normal(
              loc=noise_distr[0],
              scale=noise_distr[1],
              size=(n_masks,) + mask_size,
          )
      elif mask_type == "uniform":
          noise = np.random.uniform(
              low=noise_rng[0],
              high=noise_rng[1],
              size=(n_masks,) + mask_size,
          )
  
      # find upper left coordinate for mask
      if coord is None:
          x_start = np.random.randint(
              0, X_shape[1] - mask_size[0], n_masks
          )
          y_start = np.random.randint(
              0, X_shape[2] - mask_size[1], n_masks
          )
      else:
          x_start, y_start = coord
  
      # update masks
      for _ in range(x_start.shape[0]):
          if mask_type == "zero":
              update_val: Union[float, np.ndarray] = 0.0
          else:
              update_val = noise[_]
  
          for c in channels:
              mask[
                  _,
                  x_start[_] : x_start[_] + mask_size[0],
                  y_start[_] : y_start[_] + mask_size[1],
                  c,
              ] = update_val
  
      # apply masks to instances
      X_masks = []
      for _ in range(X_shape[0]):
          if mask_type == "zero":
              X_mask_ = X[_].reshape((1,) + X_shape[1:]) * mask
          else:
              X_mask_ = np.clip(
                  X[_].reshape((1,) + X_shape[1:]) + mask,
                  clip_rng[0],
                  clip_rng[1],
              )
          X_masks.append(X_mask_)
      X_mask = np.concatenate(X_masks, axis=0)
  
      return X_mask, mask
  
imports: import random
  from io import BytesIO
  from typing import List, Tuple, Union
  import cv2
  import numpy
  import skimage
  from alibi_detect.utils.data import Bunch
  from alibi_detect.utils.discretizer import Discretizer
  from alibi_detect.utils.distance import abdm, multidim_scaling
  from alibi_detect.utils.mapping import ohe2ord
  from PIL import Image
  from scipy.ndimage import zoom
  from scipy.ndimage.interpolation import map_coordinates
  from skimage.filters import gaussian
label_desc: Use image node_id 1 for calling a global function with example usage: apply_mask(X, mask_size, n_masks, coord, channels, mask_type, noise_distr, noise_rng, clip_rng) and returns: X_mask,  mask