code: def remove_pixels(full_images, missing_square_size=32, random_seed=0):
      np.random.seed(random_seed)
      incomplete_faces = []
      n_faces = len(full_images)
      height, width = full_images[0].shape[:2]
      for i in range(n_faces):
          image = full_images[i].copy()
          start_x = np.random.randint(
              low=0, high=height - missing_square_size + 1
          )
          start_y = np.random.randint(
              low=0, high=width - missing_square_size + 1
          )
          image[
              start_x : start_x + missing_square_size,
              start_y : start_y + missing_square_size,
          ] = np.nan
          incomplete_faces.append(image)
      return np.array(incomplete_faces, dtype=np.float32)
  
imports: from os import mkdir
  from os.path import exists, join
  from collections import defaultdict
  import pylab
  from sklearn.datasets import fetch_lfw_people
  from sklearn.impute import IterativeImputer
  import numpy
  from fancyimpute import SimpleFill, IterativeSVD, SoftImpute, BiScaler, KNN
  from fancyimpute.common import masked_mae, masked_mse
label_desc: Use image node_id 1 for calling a global function with example usage: remove_pixels(full_images, missing_square_size, random_seed) and returns: np